Bank_smote.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "0e146349",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import seaborn as sns\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.preprocessing import OneHotEncoder\n",
    "from sklearn.preprocessing import LabelEncoder\n",
    "from sklearn.preprocessing import MinMaxScaler\n",
    "from sklearn.naive_bayes import GaussianNB\n",
    "from sklearn.linear_model import LogisticRegression\n",
    "from sklearn.neighbors import KNeighborsClassifier  \n",
    "from sklearn.svm import SVC\n",
    "from sklearn.tree import DecisionTreeClassifier \n",
    "from sklearn.ensemble import RandomForestClassifier\n",
    "from xgboost import XGBClassifier\n",
    "from sklearn.ensemble import GradientBoostingClassifier\n",
    "from sklearn import metrics\n",
    "from sklearn.metrics import confusion_matrix, accuracy_score, classification_report, precision_score, recall_score, f1_score\n",
    "from sklearn.model_selection import GridSearchCV\n",
    "from imblearn.over_sampling import SMOTE #generating synthetic samples of the minority class \n",
    "#from imblearn.under_sampling import RandomUnderSampler\n",
    "from imblearn.pipeline import Pipeline\n",
    "from collections import Counter"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "77749157",
   "metadata": {},
   "outputs": [],
   "source": [
    "bank=pd.read_csv('/Users/souravghoshhansda/Library/CloudStorage/OneDrive-Personal/data exploration/Bank_Data.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "47abb893",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>age</th>\n",
       "      <th>job</th>\n",
       "      <th>marital</th>\n",
       "      <th>education</th>\n",
       "      <th>default</th>\n",
       "      <th>balance</th>\n",
       "      <th>housing</th>\n",
       "      <th>loan</th>\n",
       "      <th>contact</th>\n",
       "      <th>day</th>\n",
       "      <th>month</th>\n",
       "      <th>duration</th>\n",
       "      <th>campaign</th>\n",
       "      <th>pdays</th>\n",
       "      <th>previous</th>\n",
       "      <th>poutcome</th>\n",
       "      <th>deposit</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>59</td>\n",
       "      <td>admin.</td>\n",
       "      <td>married</td>\n",
       "      <td>secondary</td>\n",
       "      <td>no</td>\n",
       "      <td>2343</td>\n",
       "      <td>yes</td>\n",
       "      <td>no</td>\n",
       "      <td>unknown</td>\n",
       "      <td>5</td>\n",
       "      <td>may</td>\n",
       "      <td>1042</td>\n",
       "      <td>1</td>\n",
       "      <td>-1</td>\n",
       "      <td>0</td>\n",
       "      <td>unknown</td>\n",
       "      <td>yes</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>56</td>\n",
       "      <td>admin.</td>\n",
       "      <td>married</td>\n",
       "      <td>secondary</td>\n",
       "      <td>no</td>\n",
       "      <td>45</td>\n",
       "      <td>no</td>\n",
       "      <td>no</td>\n",
       "      <td>unknown</td>\n",
       "      <td>5</td>\n",
       "      <td>may</td>\n",
       "      <td>1467</td>\n",
       "      <td>1</td>\n",
       "      <td>-1</td>\n",
       "      <td>0</td>\n",
       "      <td>unknown</td>\n",
       "      <td>yes</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>41</td>\n",
       "      <td>technician</td>\n",
       "      <td>married</td>\n",
       "      <td>secondary</td>\n",
       "      <td>no</td>\n",
       "      <td>1270</td>\n",
       "      <td>yes</td>\n",
       "      <td>no</td>\n",
       "      <td>unknown</td>\n",
       "      <td>5</td>\n",
       "      <td>may</td>\n",
       "      <td>1389</td>\n",
       "      <td>1</td>\n",
       "      <td>-1</td>\n",
       "      <td>0</td>\n",
       "      <td>unknown</td>\n",
       "      <td>yes</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>55</td>\n",
       "      <td>services</td>\n",
       "      <td>married</td>\n",
       "      <td>secondary</td>\n",
       "      <td>no</td>\n",
       "      <td>2476</td>\n",
       "      <td>yes</td>\n",
       "      <td>no</td>\n",
       "      <td>unknown</td>\n",
       "      <td>5</td>\n",
       "      <td>may</td>\n",
       "      <td>579</td>\n",
       "      <td>1</td>\n",
       "      <td>-1</td>\n",
       "      <td>0</td>\n",
       "      <td>unknown</td>\n",
       "      <td>yes</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>54</td>\n",
       "      <td>admin.</td>\n",
       "      <td>married</td>\n",
       "      <td>tertiary</td>\n",
       "      <td>no</td>\n",
       "      <td>184</td>\n",
       "      <td>no</td>\n",
       "      <td>no</td>\n",
       "      <td>unknown</td>\n",
       "      <td>5</td>\n",
       "      <td>may</td>\n",
       "      <td>673</td>\n",
       "      <td>2</td>\n",
       "      <td>-1</td>\n",
       "      <td>0</td>\n",
       "      <td>unknown</td>\n",
       "      <td>yes</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   age         job  marital  education default  balance housing loan  contact  \\\n",
       "0   59      admin.  married  secondary      no     2343     yes   no  unknown   \n",
       "1   56      admin.  married  secondary      no       45      no   no  unknown   \n",
       "2   41  technician  married  secondary      no     1270     yes   no  unknown   \n",
       "3   55    services  married  secondary      no     2476     yes   no  unknown   \n",
       "4   54      admin.  married   tertiary      no      184      no   no  unknown   \n",
       "\n",
       "   day month  duration  campaign  pdays  previous poutcome deposit  \n",
       "0    5   may      1042         1     -1         0  unknown     yes  \n",
       "1    5   may      1467         1     -1         0  unknown     yes  \n",
       "2    5   may      1389         1     -1         0  unknown     yes  \n",
       "3    5   may       579         1     -1         0  unknown     yes  \n",
       "4    5   may       673         2     -1         0  unknown     yes  "
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "bank.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "61fb37ac",
   "metadata": {},
   "outputs": [],
   "source": [
    "# age: age of a person\n",
    "# job: type of job\n",
    "# marital: marital status\n",
    "# education\n",
    "# default: has credit in default? ('no','yes','unknown')\n",
    "# balance: average yearly balance\n",
    "# housing: has housing loan? ('no','yes','unknown')\n",
    "# loan: has personal loanduration ? ('no','yes','unknown')\n",
    "# contact: contact communication type ('cellular','telephone')\n",
    "# day: The day of the month when the contact was last made.\n",
    "# month: The month of the year when the last contact was made.\n",
    "# duration: The duration of the last contact in seconds.\n",
    "# campaign: The number of contacts performed during the campaign for the individual.\n",
    "# pdays: The number of days that passed after the last contact from a previous campaign. A value of -1 indicates that the\n",
    "# individual was not previously contacted.\n",
    "# previous: The number of contacts performed before this campaign for the individual.\n",
    "# poutcome: The outcome of the previous marketting campaign (e.g. success, failure, others)\n",
    "# The aim of the project is to predict if the client will subscribe(yes/no) to a term deposit. This is a classification\n",
    "# model, because the target variable is categorical. We can apply Logistic, Decision Tree, Random Forest etc for this data set."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "7e65a43e",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>age</th>\n",
       "      <th>balance</th>\n",
       "      <th>day</th>\n",
       "      <th>duration</th>\n",
       "      <th>campaign</th>\n",
       "      <th>pdays</th>\n",
       "      <th>previous</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>11162.000000</td>\n",
       "      <td>11162.000000</td>\n",
       "      <td>11162.000000</td>\n",
       "      <td>11162.000000</td>\n",
       "      <td>11162.000000</td>\n",
       "      <td>11162.000000</td>\n",
       "      <td>11162.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>41.231948</td>\n",
       "      <td>1528.538524</td>\n",
       "      <td>15.658036</td>\n",
       "      <td>371.993818</td>\n",
       "      <td>2.508421</td>\n",
       "      <td>51.330407</td>\n",
       "      <td>0.832557</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>11.913369</td>\n",
       "      <td>3225.413326</td>\n",
       "      <td>8.420740</td>\n",
       "      <td>347.128386</td>\n",
       "      <td>2.722077</td>\n",
       "      <td>108.758282</td>\n",
       "      <td>2.292007</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>18.000000</td>\n",
       "      <td>-6847.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>32.000000</td>\n",
       "      <td>122.000000</td>\n",
       "      <td>8.000000</td>\n",
       "      <td>138.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>39.000000</td>\n",
       "      <td>550.000000</td>\n",
       "      <td>15.000000</td>\n",
       "      <td>255.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>-1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>49.000000</td>\n",
       "      <td>1708.000000</td>\n",
       "      <td>22.000000</td>\n",
       "      <td>496.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>20.750000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>95.000000</td>\n",
       "      <td>81204.000000</td>\n",
       "      <td>31.000000</td>\n",
       "      <td>3881.000000</td>\n",
       "      <td>63.000000</td>\n",
       "      <td>854.000000</td>\n",
       "      <td>58.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                age       balance           day      duration      campaign  \\\n",
       "count  11162.000000  11162.000000  11162.000000  11162.000000  11162.000000   \n",
       "mean      41.231948   1528.538524     15.658036    371.993818      2.508421   \n",
       "std       11.913369   3225.413326      8.420740    347.128386      2.722077   \n",
       "min       18.000000  -6847.000000      1.000000      2.000000      1.000000   \n",
       "25%       32.000000    122.000000      8.000000    138.000000      1.000000   \n",
       "50%       39.000000    550.000000     15.000000    255.000000      2.000000   \n",
       "75%       49.000000   1708.000000     22.000000    496.000000      3.000000   \n",
       "max       95.000000  81204.000000     31.000000   3881.000000     63.000000   \n",
       "\n",
       "              pdays      previous  \n",
       "count  11162.000000  11162.000000  \n",
       "mean      51.330407      0.832557  \n",
       "std      108.758282      2.292007  \n",
       "min       -1.000000      0.000000  \n",
       "25%       -1.000000      0.000000  \n",
       "50%       -1.000000      0.000000  \n",
       "75%       20.750000      1.000000  \n",
       "max      854.000000     58.000000  "
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "bank.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "33fcb19f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "age           int64\n",
       "job          object\n",
       "marital      object\n",
       "education    object\n",
       "default      object\n",
       "balance       int64\n",
       "housing      object\n",
       "loan         object\n",
       "contact      object\n",
       "day           int64\n",
       "month        object\n",
       "duration      int64\n",
       "campaign      int64\n",
       "pdays         int64\n",
       "previous      int64\n",
       "poutcome     object\n",
       "deposit      object\n",
       "dtype: object"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "bank.dtypes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "303441ee",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "age          0\n",
       "job          0\n",
       "marital      0\n",
       "education    0\n",
       "default      0\n",
       "balance      0\n",
       "housing      0\n",
       "loan         0\n",
       "contact      0\n",
       "day          0\n",
       "month        0\n",
       "duration     0\n",
       "campaign     0\n",
       "pdays        0\n",
       "previous     0\n",
       "poutcome     0\n",
       "deposit      0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "bank.isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "6e6f0e04",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "loan\n",
       "no     9702\n",
       "yes    1460\n",
       "Name: count, dtype: int64"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "bank.loan.value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "62d6e7b4",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "housing\n",
       "no     5881\n",
       "yes    5281\n",
       "Name: count, dtype: int64"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "bank.housing.value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "afdeddd6",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "deposit\n",
       "no     5873\n",
       "yes    5289\n",
       "Name: count, dtype: int64"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "bank.deposit.value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "a89461ae",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['job', 'marital', 'education', 'default', 'housing', 'loan', 'contact', 'month', 'poutcome', 'deposit']\n"
     ]
    }
   ],
   "source": [
    "categorical_columns = list(bank.select_dtypes(include=['object']).dtypes.index)\n",
    "print(categorical_columns)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "5918a6f3",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "job : ['admin.' 'technician' 'services' 'management' 'retired' 'blue-collar'\n",
      " 'unemployed' 'entrepreneur' 'housemaid' 'unknown' 'self-employed'\n",
      " 'student']\n",
      "job : job\n",
      "management       2566\n",
      "blue-collar      1944\n",
      "technician       1823\n",
      "admin.           1334\n",
      "services          923\n",
      "retired           778\n",
      "self-employed     405\n",
      "student           360\n",
      "unemployed        357\n",
      "entrepreneur      328\n",
      "housemaid         274\n",
      "unknown            70\n",
      "Name: count, dtype: int64\n",
      "====================================\n",
      "marital : ['married' 'single' 'divorced']\n",
      "marital : marital\n",
      "married     6351\n",
      "single      3518\n",
      "divorced    1293\n",
      "Name: count, dtype: int64\n",
      "====================================\n",
      "education : ['secondary' 'tertiary' 'primary' 'unknown']\n",
      "education : education\n",
      "secondary    5476\n",
      "tertiary     3689\n",
      "primary      1500\n",
      "unknown       497\n",
      "Name: count, dtype: int64\n",
      "====================================\n",
      "default : ['no' 'yes']\n",
      "default : default\n",
      "no     10994\n",
      "yes      168\n",
      "Name: count, dtype: int64\n",
      "====================================\n",
      "housing : ['yes' 'no']\n",
      "housing : housing\n",
      "no     5881\n",
      "yes    5281\n",
      "Name: count, dtype: int64\n",
      "====================================\n",
      "loan : ['no' 'yes']\n",
      "loan : loan\n",
      "no     9702\n",
      "yes    1460\n",
      "Name: count, dtype: int64\n",
      "====================================\n",
      "contact : ['unknown' 'cellular' 'telephone']\n",
      "contact : contact\n",
      "cellular     8042\n",
      "unknown      2346\n",
      "telephone     774\n",
      "Name: count, dtype: int64\n",
      "====================================\n",
      "month : ['may' 'jun' 'jul' 'aug' 'oct' 'nov' 'dec' 'jan' 'feb' 'mar' 'apr' 'sep']\n",
      "month : month\n",
      "may    2824\n",
      "aug    1519\n",
      "jul    1514\n",
      "jun    1222\n",
      "nov     943\n",
      "apr     923\n",
      "feb     776\n",
      "oct     392\n",
      "jan     344\n",
      "sep     319\n",
      "mar     276\n",
      "dec     110\n",
      "Name: count, dtype: int64\n",
      "====================================\n",
      "poutcome : ['unknown' 'other' 'failure' 'success']\n",
      "poutcome : poutcome\n",
      "unknown    8326\n",
      "failure    1228\n",
      "success    1071\n",
      "other       537\n",
      "Name: count, dtype: int64\n",
      "====================================\n",
      "deposit : ['yes' 'no']\n",
      "deposit : deposit\n",
      "no     5873\n",
      "yes    5289\n",
      "Name: count, dtype: int64\n",
      "====================================\n"
     ]
    }
   ],
   "source": [
    "categorical_col = [] #Initializes an empty list to store the names of columns that are identified as categorical.\n",
    "for column in bank.columns: #Iterates through each column in the DataFrame.\n",
    "    if len(bank[column].unique()) <= 30:#Checks if the column's data type is 'object' (typically indicating a categorical variable) and if the number of unique values in that column is 30 or less.\n",
    "        categorical_col.append(column) # If the conditions in the if statement are met, it appends the column name to the categorical_col list.\n",
    "        print(f\"{column} : {bank[column].unique()}\") #Prints the name of the column and its unique values.\n",
    "        print(f\"{column} : {bank[column].value_counts()}\")\n",
    "        print(\"====================================\") # Prints a separator line for clarity.\n",
    "        \n",
    "#The purpose of this code is to help you identify and inspect categorical columns in your DataFrame, particularly those with a manageable number of unique categories. This information is useful for data exploration and analysis, especially when you need to decide how to handle categorical variables in machine learning or data analysis tasks."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "0be1b915",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['job',\n",
       " 'marital',\n",
       " 'education',\n",
       " 'default',\n",
       " 'housing',\n",
       " 'loan',\n",
       " 'contact',\n",
       " 'month',\n",
       " 'poutcome',\n",
       " 'deposit']"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "categorical_col"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "3668d8e4",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/souravghoshhansda/anaconda3/envs/notebook/lib/python3.10/site-packages/seaborn/_oldcore.py:1498: FutureWarning: is_categorical_dtype is deprecated and will be removed in a future version. Use isinstance(dtype, CategoricalDtype) instead\n",
      "  if pd.api.types.is_categorical_dtype(vector):\n",
      "/Users/souravghoshhansda/anaconda3/envs/notebook/lib/python3.10/site-packages/seaborn/_oldcore.py:1498: FutureWarning: is_categorical_dtype is deprecated and will be removed in a future version. Use isinstance(dtype, CategoricalDtype) instead\n",
      "  if pd.api.types.is_categorical_dtype(vector):\n",
      "/Users/souravghoshhansda/anaconda3/envs/notebook/lib/python3.10/site-packages/seaborn/_oldcore.py:1498: FutureWarning: is_categorical_dtype is deprecated and will be removed in a future version. Use isinstance(dtype, CategoricalDtype) instead\n",
      "  if pd.api.types.is_categorical_dtype(vector):\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<Axes: xlabel='deposit', ylabel='count'>"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.countplot(x='deposit',data=bank)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "79f81ce0",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "5289"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "deposit_yes=len(bank[bank.deposit=='yes'])\n",
    "deposit_yes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "77d101ca",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "5873"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "deposit_no=len(bank[bank.deposit=='no'])\n",
    "deposit_no"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "5340b0be",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Having Bank Deposit %: 47.38%\n"
     ]
    }
   ],
   "source": [
    "print(\"Having Bank Deposit %: {:.2f}%\".format((deposit_yes / (len(bank.deposit))*100)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "6b01892b",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Having no Bank Deposit %: 52.62%\n"
     ]
    }
   ],
   "source": [
    "print(\"Having no Bank Deposit %: {:.2f}%\".format((deposit_no / (len(bank.deposit))*100)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "85bd6319",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1460"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "loan_yes=len(bank[bank.loan=='yes'])\n",
    "loan_yes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "b80d0164",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "9702"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "loan_no=len(bank[bank.loan=='no'])\n",
    "loan_no"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "ab635988",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "484"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "deposit_yes_loan=len(bank[(bank['loan'] =='yes') & (bank['deposit'] == 'yes')])\n",
    "deposit_yes_loan"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "3cb77b3f",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Having Bank Deposit and loan %: 33.15%\n"
     ]
    }
   ],
   "source": [
    "print(\"Having Bank Deposit and loan %: {:.2f}%\".format((deposit_yes_loan / loan_yes*100)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "a5f65f27",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "4805"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "deposit_no_loan=len(bank[(bank['loan'] =='no') & (bank['deposit'] == 'yes')])\n",
    "deposit_no_loan"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "0f2b2931",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Having Bank Deposit and no loan %: 54.51%\n"
     ]
    }
   ],
   "source": [
    "print(\"Having Bank Deposit and no loan %: {:.2f}%\".format((deposit_yes / loan_no*100)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "a16f54e2",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/souravghoshhansda/anaconda3/envs/notebook/lib/python3.10/site-packages/seaborn/_oldcore.py:1498: FutureWarning: is_categorical_dtype is deprecated and will be removed in a future version. Use isinstance(dtype, CategoricalDtype) instead\n",
      "  if pd.api.types.is_categorical_dtype(vector):\n",
      "/Users/souravghoshhansda/anaconda3/envs/notebook/lib/python3.10/site-packages/seaborn/_oldcore.py:1498: FutureWarning: is_categorical_dtype is deprecated and will be removed in a future version. Use isinstance(dtype, CategoricalDtype) instead\n",
      "  if pd.api.types.is_categorical_dtype(vector):\n",
      "/Users/souravghoshhansda/anaconda3/envs/notebook/lib/python3.10/site-packages/seaborn/_oldcore.py:1498: FutureWarning: is_categorical_dtype is deprecated and will be removed in a future version. Use isinstance(dtype, CategoricalDtype) instead\n",
      "  if pd.api.types.is_categorical_dtype(vector):\n",
      "/Users/souravghoshhansda/anaconda3/envs/notebook/lib/python3.10/site-packages/seaborn/_oldcore.py:1498: FutureWarning: is_categorical_dtype is deprecated and will be removed in a future version. Use isinstance(dtype, CategoricalDtype) instead\n",
      "  if pd.api.types.is_categorical_dtype(vector):\n",
      "/Users/souravghoshhansda/anaconda3/envs/notebook/lib/python3.10/site-packages/seaborn/_oldcore.py:1498: FutureWarning: is_categorical_dtype is deprecated and will be removed in a future version. Use isinstance(dtype, CategoricalDtype) instead\n",
      "  if pd.api.types.is_categorical_dtype(vector):\n",
      "/Users/souravghoshhansda/anaconda3/envs/notebook/lib/python3.10/site-packages/seaborn/_oldcore.py:1498: FutureWarning: is_categorical_dtype is deprecated and will be removed in a future version. Use isinstance(dtype, CategoricalDtype) instead\n",
      "  if pd.api.types.is_categorical_dtype(vector):\n",
      "/Users/souravghoshhansda/anaconda3/envs/notebook/lib/python3.10/site-packages/seaborn/_oldcore.py:1498: FutureWarning: is_categorical_dtype is deprecated and will be removed in a future version. Use isinstance(dtype, CategoricalDtype) instead\n",
      "  if pd.api.types.is_categorical_dtype(vector):\n",
      "/Users/souravghoshhansda/anaconda3/envs/notebook/lib/python3.10/site-packages/seaborn/_oldcore.py:1498: FutureWarning: is_categorical_dtype is deprecated and will be removed in a future version. Use isinstance(dtype, CategoricalDtype) instead\n",
      "  if pd.api.types.is_categorical_dtype(vector):\n",
      "/Users/souravghoshhansda/anaconda3/envs/notebook/lib/python3.10/site-packages/seaborn/_oldcore.py:1498: FutureWarning: is_categorical_dtype is deprecated and will be removed in a future version. Use isinstance(dtype, CategoricalDtype) instead\n",
      "  if pd.api.types.is_categorical_dtype(vector):\n",
      "/Users/souravghoshhansda/anaconda3/envs/notebook/lib/python3.10/site-packages/seaborn/_oldcore.py:1498: FutureWarning: is_categorical_dtype is deprecated and will be removed in a future version. Use isinstance(dtype, CategoricalDtype) instead\n",
      "  if pd.api.types.is_categorical_dtype(vector):\n",
      "/Users/souravghoshhansda/anaconda3/envs/notebook/lib/python3.10/site-packages/seaborn/_oldcore.py:1498: FutureWarning: is_categorical_dtype is deprecated and will be removed in a future version. Use isinstance(dtype, CategoricalDtype) instead\n",
      "  if pd.api.types.is_categorical_dtype(vector):\n",
      "/Users/souravghoshhansda/anaconda3/envs/notebook/lib/python3.10/site-packages/seaborn/_oldcore.py:1498: FutureWarning: is_categorical_dtype is deprecated and will be removed in a future version. Use isinstance(dtype, CategoricalDtype) instead\n",
      "  if pd.api.types.is_categorical_dtype(vector):\n",
      "/Users/souravghoshhansda/anaconda3/envs/notebook/lib/python3.10/site-packages/seaborn/_oldcore.py:1498: FutureWarning: is_categorical_dtype is deprecated and will be removed in a future version. Use isinstance(dtype, CategoricalDtype) instead\n",
      "  if pd.api.types.is_categorical_dtype(vector):\n",
      "/Users/souravghoshhansda/anaconda3/envs/notebook/lib/python3.10/site-packages/seaborn/_oldcore.py:1498: FutureWarning: is_categorical_dtype is deprecated and will be removed in a future version. Use isinstance(dtype, CategoricalDtype) instead\n",
      "  if pd.api.types.is_categorical_dtype(vector):\n",
      "/Users/souravghoshhansda/anaconda3/envs/notebook/lib/python3.10/site-packages/seaborn/_oldcore.py:1498: FutureWarning: is_categorical_dtype is deprecated and will be removed in a future version. Use isinstance(dtype, CategoricalDtype) instead\n",
      "  if pd.api.types.is_categorical_dtype(vector):\n",
      "/Users/souravghoshhansda/anaconda3/envs/notebook/lib/python3.10/site-packages/seaborn/_oldcore.py:1498: FutureWarning: is_categorical_dtype is deprecated and will be removed in a future version. Use isinstance(dtype, CategoricalDtype) instead\n",
      "  if pd.api.types.is_categorical_dtype(vector):\n",
      "/Users/souravghoshhansda/anaconda3/envs/notebook/lib/python3.10/site-packages/seaborn/_oldcore.py:1498: FutureWarning: is_categorical_dtype is deprecated and will be removed in a future version. Use isinstance(dtype, CategoricalDtype) instead\n",
      "  if pd.api.types.is_categorical_dtype(vector):\n",
      "/Users/souravghoshhansda/anaconda3/envs/notebook/lib/python3.10/site-packages/seaborn/_oldcore.py:1498: FutureWarning: is_categorical_dtype is deprecated and will be removed in a future version. Use isinstance(dtype, CategoricalDtype) instead\n",
      "  if pd.api.types.is_categorical_dtype(vector):\n",
      "/Users/souravghoshhansda/anaconda3/envs/notebook/lib/python3.10/site-packages/seaborn/_oldcore.py:1498: FutureWarning: is_categorical_dtype is deprecated and will be removed in a future version. Use isinstance(dtype, CategoricalDtype) instead\n",
      "  if pd.api.types.is_categorical_dtype(vector):\n",
      "/Users/souravghoshhansda/anaconda3/envs/notebook/lib/python3.10/site-packages/seaborn/_oldcore.py:1498: FutureWarning: is_categorical_dtype is deprecated and will be removed in a future version. Use isinstance(dtype, CategoricalDtype) instead\n",
      "  if pd.api.types.is_categorical_dtype(vector):\n",
      "/Users/souravghoshhansda/anaconda3/envs/notebook/lib/python3.10/site-packages/seaborn/_oldcore.py:1498: FutureWarning: is_categorical_dtype is deprecated and will be removed in a future version. Use isinstance(dtype, CategoricalDtype) instead\n",
      "  if pd.api.types.is_categorical_dtype(vector):\n",
      "/Users/souravghoshhansda/anaconda3/envs/notebook/lib/python3.10/site-packages/seaborn/_oldcore.py:1498: FutureWarning: is_categorical_dtype is deprecated and will be removed in a future version. Use isinstance(dtype, CategoricalDtype) instead\n",
      "  if pd.api.types.is_categorical_dtype(vector):\n",
      "/Users/souravghoshhansda/anaconda3/envs/notebook/lib/python3.10/site-packages/seaborn/_oldcore.py:1498: FutureWarning: is_categorical_dtype is deprecated and will be removed in a future version. Use isinstance(dtype, CategoricalDtype) instead\n",
      "  if pd.api.types.is_categorical_dtype(vector):\n",
      "/Users/souravghoshhansda/anaconda3/envs/notebook/lib/python3.10/site-packages/seaborn/_oldcore.py:1498: FutureWarning: is_categorical_dtype is deprecated and will be removed in a future version. Use isinstance(dtype, CategoricalDtype) instead\n",
      "  if pd.api.types.is_categorical_dtype(vector):\n",
      "/Users/souravghoshhansda/anaconda3/envs/notebook/lib/python3.10/site-packages/seaborn/_oldcore.py:1498: FutureWarning: is_categorical_dtype is deprecated and will be removed in a future version. Use isinstance(dtype, CategoricalDtype) instead\n",
      "  if pd.api.types.is_categorical_dtype(vector):\n",
      "/Users/souravghoshhansda/anaconda3/envs/notebook/lib/python3.10/site-packages/seaborn/_oldcore.py:1498: FutureWarning: is_categorical_dtype is deprecated and will be removed in a future version. Use isinstance(dtype, CategoricalDtype) instead\n",
      "  if pd.api.types.is_categorical_dtype(vector):\n",
      "/Users/souravghoshhansda/anaconda3/envs/notebook/lib/python3.10/site-packages/seaborn/_oldcore.py:1498: FutureWarning: is_categorical_dtype is deprecated and will be removed in a future version. Use isinstance(dtype, CategoricalDtype) instead\n",
      "  if pd.api.types.is_categorical_dtype(vector):\n",
      "/Users/souravghoshhansda/anaconda3/envs/notebook/lib/python3.10/site-packages/seaborn/_oldcore.py:1498: FutureWarning: is_categorical_dtype is deprecated and will be removed in a future version. Use isinstance(dtype, CategoricalDtype) instead\n",
      "  if pd.api.types.is_categorical_dtype(vector):\n",
      "/Users/souravghoshhansda/anaconda3/envs/notebook/lib/python3.10/site-packages/seaborn/_oldcore.py:1498: FutureWarning: is_categorical_dtype is deprecated and will be removed in a future version. Use isinstance(dtype, CategoricalDtype) instead\n",
      "  if pd.api.types.is_categorical_dtype(vector):\n",
      "/Users/souravghoshhansda/anaconda3/envs/notebook/lib/python3.10/site-packages/seaborn/_oldcore.py:1498: FutureWarning: is_categorical_dtype is deprecated and will be removed in a future version. Use isinstance(dtype, CategoricalDtype) instead\n",
      "  if pd.api.types.is_categorical_dtype(vector):\n",
      "/Users/souravghoshhansda/anaconda3/envs/notebook/lib/python3.10/site-packages/seaborn/_oldcore.py:1498: FutureWarning: is_categorical_dtype is deprecated and will be removed in a future version. Use isinstance(dtype, CategoricalDtype) instead\n",
      "  if pd.api.types.is_categorical_dtype(vector):\n",
      "/Users/souravghoshhansda/anaconda3/envs/notebook/lib/python3.10/site-packages/seaborn/_oldcore.py:1498: FutureWarning: is_categorical_dtype is deprecated and will be removed in a future version. Use isinstance(dtype, CategoricalDtype) instead\n",
      "  if pd.api.types.is_categorical_dtype(vector):\n",
      "/Users/souravghoshhansda/anaconda3/envs/notebook/lib/python3.10/site-packages/seaborn/_oldcore.py:1498: FutureWarning: is_categorical_dtype is deprecated and will be removed in a future version. Use isinstance(dtype, CategoricalDtype) instead\n",
      "  if pd.api.types.is_categorical_dtype(vector):\n",
      "/Users/souravghoshhansda/anaconda3/envs/notebook/lib/python3.10/site-packages/seaborn/_oldcore.py:1498: FutureWarning: is_categorical_dtype is deprecated and will be removed in a future version. Use isinstance(dtype, CategoricalDtype) instead\n",
      "  if pd.api.types.is_categorical_dtype(vector):\n",
      "/Users/souravghoshhansda/anaconda3/envs/notebook/lib/python3.10/site-packages/seaborn/_oldcore.py:1498: FutureWarning: is_categorical_dtype is deprecated and will be removed in a future version. Use isinstance(dtype, CategoricalDtype) instead\n",
      "  if pd.api.types.is_categorical_dtype(vector):\n",
      "/Users/souravghoshhansda/anaconda3/envs/notebook/lib/python3.10/site-packages/seaborn/_oldcore.py:1498: FutureWarning: is_categorical_dtype is deprecated and will be removed in a future version. Use isinstance(dtype, CategoricalDtype) instead\n",
      "  if pd.api.types.is_categorical_dtype(vector):\n"
     ]
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 1500x1000 with 9 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#checking the inter relationship of all the features with the target variable to understand the importance of the feature columns\n",
    "features = ['job','marital','education','default','housing', 'loan', 'contact', 'month', 'poutcome']\n",
    "\n",
    "plt.figure(figsize=(15, 10))\n",
    "\n",
    "for i, feature in enumerate(features, 1):\n",
    "    plt.subplot(3, 3, i)#3 rows, 3 columns\n",
    "    sns.countplot(x=feature, hue='deposit', data=bank)\n",
    "    plt.title(f'Count plot of {feature} with Deposit')\n",
    "    plt.xlabel(feature)\n",
    "    plt.ylabel('Count')\n",
    "\n",
    "plt.tight_layout()\n",
    "plt.show()\n",
    "#hw:write the insights from this output to explain which of the 8 features are impacted by target and which are not"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "106ee62b",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "26d18678",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/souravghoshhansda/anaconda3/envs/notebook/lib/python3.10/site-packages/seaborn/_oldcore.py:1498: FutureWarning: is_categorical_dtype is deprecated and will be removed in a future version. Use isinstance(dtype, CategoricalDtype) instead\n",
      "  if pd.api.types.is_categorical_dtype(vector):\n",
      "/Users/souravghoshhansda/anaconda3/envs/notebook/lib/python3.10/site-packages/seaborn/_oldcore.py:1498: FutureWarning: is_categorical_dtype is deprecated and will be removed in a future version. Use isinstance(dtype, CategoricalDtype) instead\n",
      "  if pd.api.types.is_categorical_dtype(vector):\n",
      "/Users/souravghoshhansda/anaconda3/envs/notebook/lib/python3.10/site-packages/seaborn/_oldcore.py:1498: FutureWarning: is_categorical_dtype is deprecated and will be removed in a future version. Use isinstance(dtype, CategoricalDtype) instead\n",
      "  if pd.api.types.is_categorical_dtype(vector):\n",
      "/Users/souravghoshhansda/anaconda3/envs/notebook/lib/python3.10/site-packages/seaborn/_oldcore.py:1498: FutureWarning: is_categorical_dtype is deprecated and will be removed in a future version. Use isinstance(dtype, CategoricalDtype) instead\n",
      "  if pd.api.types.is_categorical_dtype(vector):\n",
      "/Users/souravghoshhansda/anaconda3/envs/notebook/lib/python3.10/site-packages/seaborn/_oldcore.py:1498: FutureWarning: is_categorical_dtype is deprecated and will be removed in a future version. Use isinstance(dtype, CategoricalDtype) instead\n",
      "  if pd.api.types.is_categorical_dtype(vector):\n",
      "/Users/souravghoshhansda/anaconda3/envs/notebook/lib/python3.10/site-packages/seaborn/_oldcore.py:1498: FutureWarning: is_categorical_dtype is deprecated and will be removed in a future version. Use isinstance(dtype, CategoricalDtype) instead\n",
      "  if pd.api.types.is_categorical_dtype(vector):\n",
      "/Users/souravghoshhansda/anaconda3/envs/notebook/lib/python3.10/site-packages/seaborn/_oldcore.py:1498: FutureWarning: is_categorical_dtype is deprecated and will be removed in a future version. Use isinstance(dtype, CategoricalDtype) instead\n",
      "  if pd.api.types.is_categorical_dtype(vector):\n",
      "/Users/souravghoshhansda/anaconda3/envs/notebook/lib/python3.10/site-packages/seaborn/_oldcore.py:1498: FutureWarning: is_categorical_dtype is deprecated and will be removed in a future version. Use isinstance(dtype, CategoricalDtype) instead\n",
      "  if pd.api.types.is_categorical_dtype(vector):\n",
      "/Users/souravghoshhansda/anaconda3/envs/notebook/lib/python3.10/site-packages/seaborn/_oldcore.py:1498: FutureWarning: is_categorical_dtype is deprecated and will be removed in a future version. Use isinstance(dtype, CategoricalDtype) instead\n",
      "  if pd.api.types.is_categorical_dtype(vector):\n",
      "/Users/souravghoshhansda/anaconda3/envs/notebook/lib/python3.10/site-packages/seaborn/_oldcore.py:1498: FutureWarning: is_categorical_dtype is deprecated and will be removed in a future version. Use isinstance(dtype, CategoricalDtype) instead\n",
      "  if pd.api.types.is_categorical_dtype(vector):\n",
      "/Users/souravghoshhansda/anaconda3/envs/notebook/lib/python3.10/site-packages/seaborn/_oldcore.py:1498: FutureWarning: is_categorical_dtype is deprecated and will be removed in a future version. Use isinstance(dtype, CategoricalDtype) instead\n",
      "  if pd.api.types.is_categorical_dtype(vector):\n",
      "/Users/souravghoshhansda/anaconda3/envs/notebook/lib/python3.10/site-packages/seaborn/_oldcore.py:1498: FutureWarning: is_categorical_dtype is deprecated and will be removed in a future version. Use isinstance(dtype, CategoricalDtype) instead\n",
      "  if pd.api.types.is_categorical_dtype(vector):\n",
      "/Users/souravghoshhansda/anaconda3/envs/notebook/lib/python3.10/site-packages/seaborn/_oldcore.py:1498: FutureWarning: is_categorical_dtype is deprecated and will be removed in a future version. Use isinstance(dtype, CategoricalDtype) instead\n",
      "  if pd.api.types.is_categorical_dtype(vector):\n",
      "/Users/souravghoshhansda/anaconda3/envs/notebook/lib/python3.10/site-packages/seaborn/_oldcore.py:1498: FutureWarning: is_categorical_dtype is deprecated and will be removed in a future version. Use isinstance(dtype, CategoricalDtype) instead\n",
      "  if pd.api.types.is_categorical_dtype(vector):\n",
      "/Users/souravghoshhansda/anaconda3/envs/notebook/lib/python3.10/site-packages/seaborn/_oldcore.py:1498: FutureWarning: is_categorical_dtype is deprecated and will be removed in a future version. Use isinstance(dtype, CategoricalDtype) instead\n",
      "  if pd.api.types.is_categorical_dtype(vector):\n",
      "/Users/souravghoshhansda/anaconda3/envs/notebook/lib/python3.10/site-packages/seaborn/_oldcore.py:1498: FutureWarning: is_categorical_dtype is deprecated and will be removed in a future version. Use isinstance(dtype, CategoricalDtype) instead\n",
      "  if pd.api.types.is_categorical_dtype(vector):\n",
      "/Users/souravghoshhansda/anaconda3/envs/notebook/lib/python3.10/site-packages/seaborn/_oldcore.py:1498: FutureWarning: is_categorical_dtype is deprecated and will be removed in a future version. Use isinstance(dtype, CategoricalDtype) instead\n",
      "  if pd.api.types.is_categorical_dtype(vector):\n",
      "/Users/souravghoshhansda/anaconda3/envs/notebook/lib/python3.10/site-packages/seaborn/_oldcore.py:1498: FutureWarning: is_categorical_dtype is deprecated and will be removed in a future version. Use isinstance(dtype, CategoricalDtype) instead\n",
      "  if pd.api.types.is_categorical_dtype(vector):\n",
      "/Users/souravghoshhansda/anaconda3/envs/notebook/lib/python3.10/site-packages/seaborn/_oldcore.py:1498: FutureWarning: is_categorical_dtype is deprecated and will be removed in a future version. Use isinstance(dtype, CategoricalDtype) instead\n",
      "  if pd.api.types.is_categorical_dtype(vector):\n",
      "/Users/souravghoshhansda/anaconda3/envs/notebook/lib/python3.10/site-packages/seaborn/_oldcore.py:1498: FutureWarning: is_categorical_dtype is deprecated and will be removed in a future version. Use isinstance(dtype, CategoricalDtype) instead\n",
      "  if pd.api.types.is_categorical_dtype(vector):\n",
      "/Users/souravghoshhansda/anaconda3/envs/notebook/lib/python3.10/site-packages/seaborn/_oldcore.py:1498: FutureWarning: is_categorical_dtype is deprecated and will be removed in a future version. Use isinstance(dtype, CategoricalDtype) instead\n",
      "  if pd.api.types.is_categorical_dtype(vector):\n"
     ]
    },
    {
     "data": {
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2FAcAAAAAAABqgaI4AMuZpqm0tDSVlZW5tJeWliotLY3lYQEAPskwDE2bNq3KdrYcAQAAAAAAAGqGojgAy7E8LAAAlYuOjlZCQoKzAG4YhhISEhQVFWVxZgAAAAAAAEDTQVEcgOViYmIUEhJSaSwkJEQxMTGNnBEAAJ4jMTFRYWFhkqR27dopISHB4owAAAAAAACApoWiOADLZWVlqaCgoNJYQUGBsrKyGjkjAAA8h81m04wZMxQeHq7p06fLZrNZnRIAAAAAAADQpARYnQAAxMbGauDAgdq6davLEuqGYWjgwIGKjY21MDsAAKwXFxenuLg4q9MAAAAAAAAAmiRmigOwnGEYmjZtmnO/1HJ+fn6VtgMA4GsyMzM1btw4ZWZmWp0KAAAAAAAA0ORQFAfgEaKjo5WYmOjSlpiYqKioKIsyAgDAMxQVFSk1NVWHDh1SamqqioqKrE4JAAAAAAAAaFIoigPwGDfccIP8/E7/b8nPz09jxoyxOCMAAKy3cuVK5eXlSZLy8vKUnp5ucUYAAAAAAABA00JRHIDHePPNN1VWViZJKisr01tvvWVxRgAAWCs7O1vp6ekyTVOSZJqm0tPTlZ2dbXFmAAAAAAAAQNNBURyARyj/0v+3+NIfAODLTNNUWlpale3lhXIAAAAAAAAAZ0dRHIDl+NIfAICKHA6H7Ha7SktLXdpLS0tlt9vlcDgsygwAAAAAAABoWiiKA7AcX/oDAFBRbGysBg4cKH9/f5d2f39/DRo0SLGxsRZlBgAAAAAAADQtFMUBWK78S38/P9f/JfGlPwDAlxmGoWnTplXZbhiGBVkBAAAAAAAATQ9FcQCWK/9y/8xl0k3T5Et/AIBPi46OVkJCgvOz0DAMJSQkKCoqyuLMAAAAAAAAgKaDojgAj2WaJvuJAwB8XmJiosLCwiRJ7dq1U0JCgsUZAQAAAAAAAE0LRXEAljNNU2lpaRVmhBuGobS0NArjAACfZrPZNGPGDIWHh2v69Omy2WxWpwQAAAAAAAA0KQFWJwAADodDdru9QntZWZnsdrscDoc6depkQWYAAHiGuLg4xcXFWZ0GAAAAAAAA0CQxUxyA5WJjY9W7d+9KY3369FFsbGwjZwQAAAAAAAAAQOUyMzM1btw4ZWZmWp0KgBqiKA7Ao7F0OgAAAAAAAADAUxQVFSk1NVWHDh1SamqqioqKrE4JQA1YXhQ/cOCAbr31VoWFhalFixa66KKLtG3bNmfcNE0lJycrMjJSQUFBio+P165du1zOUVxcrPvvv1/t2rVTy5Ytde211yo7O7uxnwqAOnI4HNqxY0elsR07dsjhcDRyRgAAeBauQAcAAAAAwDOsXLlSeXl5kqS8vDylp6dbnBGAmrC0KH7kyBENGTJEzZo107p16/Ttt99qwYIFat26tbPPvHnzlJqaqsWLF8tutysiIkIjRozQ0aNHnX2SkpK0Zs0arVq1Sp999pkKCwt19dVXq7S01IJnBaC2WD4dAICqcQU6AAAAAACeITs7W+np6c4VTk3TVHp6OhM1gSbA0qL43LlzFRMToyVLlmjQoEHq3Lmzhg0bpnPOOUfS6f+ZLFy4UI888ojGjBmjXr16admyZTp+/Ljzypv8/Hy99tprWrBggYYPH66+fftqxYoV2rFjhzZu3Gjl0wNQD1g+HQDg67gCHQAAAAAA65mmqbS0tArfWZeVlVXaDsCzWFoUX7t2rQYMGKCbbrpJHTp0UN++ffXKK68443v37lVOTo5GjhzpbAsMDNTQoUOdS0du27ZNJ0+edOkTGRmpXr16Vbm8ZHFxsQoKClwOANZh+XQAACrHFegAPFlKSooMw1BSUpKzrSZboAEAAABNkcPhkN1uV1lZmUt7WVmZ7HY732MDHs7SoviPP/6oF154Qd26ddMHH3yge+65R1OnTtXy5cslSTk5OZKk8PBwl/uFh4c7Yzk5OWrevLnatGlTZZ8zpaSkKDQ01HnExMTU91MDUAsxMTEKCQmpNBYSEsJ7FADgk8qvQK+qnSvQAVjJbrfr5ZdfVp8+fVzaa7IFGgAAANAUsQ0o0LRZWhQvKytTv379NGfOHPXt21d33323Jk2apBdeeMGln2EYLrdN06zQdqaz9Zk9e7by8/OdR1ZWlntPBIBbsrKyqlyxoaCggPcoAMAnlV+BXlpa6tJeWlrKFegALFVYWKjExES98sorLheo12QLtMqwmhsAAACaOi5cBzyfpUXxjh076oILLnBp69Gjh/MLvoiICEmqMOM7NzfXOXs8IiJCJSUlOnLkSJV9zhQYGKiQkBCXA4B1oqOj5e/vX2nM399f0dHRjZwRAADWi42N1cCBAyt8Rvr7+2vQoEFcgQ7AMvfdd5+uuuoqDR8+3KW9JlugVYbV3AAAANAUsA0o0LRZWhQfMmSIdu/e7dL2/fffq1OnTpKkLl26KCIiQhkZGc54SUmJNm3apLi4OElS//791axZM5c+Bw8e1M6dO519AHi2LVu2VJgFV660tFRbtmxp5IwAALCeYRiaNm1ale3VrZwEAA1h1apV+vLLL5WSklIhVpMt0CrDam4AAABoCsovXvfzcy2tcfE60DRYWhSfPn26vvjiC82ZM0c//PCD0tPT9fLLL+u+++6TdPoLv6SkJM2ZM0dr1qzRzp07NWHCBLVo0UIJCQmSpNDQUN1111364x//qA8//FBfffWVbr31VvXu3bvCVesAPNOgQYPOOlN80KBBjZwRAACeITo6WgkJCc4CuGEYSkhIUFRUlMWZAfBFWVlZmjZtmlasWCGbzVZlv9pugcZqbgAAAGgKqrpInYvXgabB0qL4wIEDtWbNGr3xxhvq1auX/vSnP2nhwoVKTEx09pk5c6aSkpI0efJkDRgwQAcOHNCGDRsUHBzs7PPss89q9OjRGjt2rIYMGaIWLVro3XffrbLIBsCzZGdnn3WmeHZ2diNnBACA50hMTFRYWJgkqV27ds6LQwGgsW3btk25ubnq37+/AgICFBAQoE2bNum5555TQECAc4b42bZAAwAAAJoyLl4Hmi7DNE3T6iSsVlBQoNDQUOXn53NFOmAB0zQ1c+ZM2e32CrFBgwZp7ty5XGUHAPBpmZmZSktL07Rp09giCPAAvjqGPHr0qPbv3+/Sdscdd+j888/XrFmz1LNnT0VGRmr69OmaOXOmpNNboHXo0EFz587V3XffXaPH8dXXFwAAAE1DUVGRbr31Vh0+fFjt27fX66+/ftaVlAA0rJqOIQMaMScAqJRhGBo2bFilRfFhw4ZREAcA+Ly4uDiK4QAsFxwcrF69erm0tWzZUmFhYc728i3QunXrpm7dumnOnDkuW6ABAAAATZ3NZtOMGTOcF69TEAeaBoriACxXVlamxYsXVxpbtGiRRowYIT8/S3d7AAAAAFADM2fO1IkTJzR58mQdOXJEgwcPrrAFGgAAAAAAjY2iOADLffHFFyosLKw0VlhYqC+++ILZcQAAAIAH+uSTT1xuG4ah5ORkJScnW5IPAAAA0NCKioqUmpqqw4cPKzU1Vf369WO2ONAEMPUSgOXat2/vVhwAAG+XmZmpcePGKTMz0+pUAAAAAADwaStXrlReXp4kKS8vT+np6RZnBKAmKIoDsNynn37qVhwAAG9WfgX6oUOHlJqaqqKiIqtTAgAAAADAJ2VnZys9PV2maUqSTNNUenq6srOzLc4MQHUoigOw3NChQ92KAwDgzbgCHQAAAAAA65mmqbS0tCrbywvlADwTRXEAlvP393crDgCAt+IKdAAAAAAAPIPD4ZDdbldpaalLe2lpqex2uxwOh0WZAagJiuIAAACAB+IKdAAAAAAAPEdsbKwGDhxYYRKXv7+/Bg0apNjYWIsyA1ATFMUBAAAAD8QV6AAAAAAAeA7DMDRt2rQq2w3DsCArADVFURwAAADwQOVXoJ85qDYMgyvQAQAAAACwQHR0tBISEpxjdcMwlJCQoKioKIszA1AdiuIALFfdFXRcYQcA8EWGYWjcuHEVlkk3TVPjxo3j8xEAAAAAAAskJiYqLCxMktSuXTslJCRYnBGAmqAoDsBy1V1Fx1V2AABfZJqmVq9eXelM8VWrVrGnOAAAAAAAFrDZbJoxY4bCw8M1ffp02Ww2q1MCUAN1Kopfdtll+vXXXyu0FxQU6LLLLnM3JwA+5vXXX3crDgCANyrfU7yymeLsKQ6gLhjLAwAAAPUjLi5Oq1evVlxcnNWpAKihOhXFP/nkE5WUlFRoLyoq0r///W+3kwLgW4YOHepWHAAAb1S+p7i/v79Lu7+/P3uKA6gTxvIAAAAAAF8VUJvO33zzjfPnb7/9Vjk5Oc7bpaWlWr9+PcscA6i1rl27qnXr1pXOWmnTpo26du3a+EkBAGAxwzA0bdo0jR8/vtJ29hQHUFOM5QEAAAAAvq5WRfGLLrpIhmHIMIxKl1YLCgrSokWL6i05AL6hrKxMR48erTRWUFCgsrKyCrPkAADwBdHR0UpISNCKFStkmqYMw1BCQgLFKwC1wlgeAAAAAODralUU37t3r0zTVNeuXbVlyxa1b9/eGWvevLk6dOhA4QpArb333nsqLS2tNFZaWqr33ntP1113XSNnBQCAZ7jhhhu0cuVKZ1F8zJgxVqcEoIlhLA8AAAAA8HW1Kop36tRJ0ulZnQBQX6666io9++yzZ40DAOCr3nzzTeff32VlZXrrrbd05513WpwVgKaEsTwAAAAAwNfVuCi+du1ajRo1Ss2aNdPatWvP2vfaa691OzEAviMrK6vaeJcuXRopGwAAPEd2drbS09Nd2tLT0zVy5EhFR0dblBWApoSxPAAAAAAAtSiKjx49Wjk5OerQoYNGjx5dZT/DMKpcBhkAKnPw4MFq4xTFAQC+xjRNpaWlVdk+b948GYZhQWYAmhLG8gAAAAAA1KIo/ttl1lhyDUB9Gjx4sAzDkGmaFWKGYWjw4MEWZAUAgLUcDofsdnuF9tLSUtntdjkcDueSyABQFcbyAAAAAABIfvV1ol9//bW+TgXAx+zfv7/Sgrh0ejbc/v37GzkjAACsFxsbq4EDB8rf39+l3d/fX4MGDVJsbKxFmQHwJozlAQAAAAC+oE5F8blz52r16tXO2zfddJPatm2rqKgoff311/WWHADf8OWXX7oVBwDAGxmGoWnTplV64di0adNYOh1ArTGWBwAAAAD4qjoVxV966SXFxMRIkjIyMrRx40atX79eo0aN0oMPPlivCQLwfnl5eW7FAQDwVtHR0erZs6dLW8+ePRUVFWVRRgCaMsbyAAAAAABfVeM9xX/r4MGDzoH0e++9p7Fjx2rkyJHq3Lkze/8CqLV27dq5FQcAwFtlZ2fr22+/dWn79ttvlZ2drejoaIuyAtBUMZYHAAAAAPiqOs0Ub9OmjbKysiRJ69ev1/DhwyWd3vu3tLS0/rID4BMuuOACt+IAAHgj0zSVlpZWZXtly6oDwNkwlgcAAAAA+Ko6FcXHjBmjhIQEjRgxQnl5eRo1apQkafv27Tr33HPrNUEA3m/RokVuxQEA8EYOh0N2u71Coaq0tFR2u10Oh8OizAA0VYzlAdRWZmamxo0bp8zMTKtTAQAAANxSp6L4s88+qylTpuiCCy5QRkaGWrVqJen0UmyTJ0+u1wQBeL/yJRzrGgcAwBvFxsZq4MCB8vNz/ZPd399fgwYNUmxsrEWZAWiq6mMs/8ILL6hPnz4KCQlRSEiILrnkEq1bt84ZN01TycnJioyMVFBQkOLj47Vr164GeT4AGlZRUZFSU1N16NAhpaamqqioyOqUAAAAgDozTNZdVEFBgUJDQ5Wfn6+QkBCr0wF8zp49ezRp0qQq46+88oq6devWiBkBAOAZsrOzdeutt7q0GYahFStWKCoqyqKsAPjyGPLdd9+Vv7+/c2b5smXLNH/+fH311Vfq2bOn5s6dq2eeeUZLly7Veeedp6efflqffvqpdu/ereDg4Bo9hi+/voAnee2117RixQqZpinDMHTbbbfpzjvvtDotAAAAwEVNx5B1mikuSf/73/90//33a/jw4RoxYoSmTp2qH3/8sa6nA+DDDh486FYcAABfYpom+4kDqDN3x/LXXHONrrzySp133nk677zz9Mwzz6hVq1b64osvZJqmFi5cqEceeURjxoxRr169tGzZMh0/flzp6ekN+KwA1Lfs7Gylp6c7/+YwTVPp6enKzs62ODMAAACgbupUFP/ggw90wQUXaMuWLerTp4969eqlzZs3O5dgA4Da2L17t1txAAC8kWmamjt3bqWxuXPnUhgHUGv1PZYvLS3VqlWrdOzYMV1yySXau3evcnJyNHLkSGefwMBADR069Kz7ERcXF6ugoMDlAGAd0zSVlpZWZTt/gwAAAKApCqjLnR566CFNnz5df/7znyu0z5o1SyNGjKiX5AD4huqWRmfpdACAL9q/f7927NhRaWzHjh3av3+/Onfu3LhJAWjS6mssv2PHDl1yySUqKipSq1attGbNGl1wwQXOwnd4eLhL//DwcO3fv7/K86WkpOjJJ5+s5bMB0FAcDofsdnuF9tLSUtntdjkcDnXq1MmCzAAAAIC6q9NM8e+++0533XVXhfY777xT3377rdtJAfAt33//vVtxAAAAANWrr7F89+7dtX37dn3xxRe69957NX78eJf7G4bh0r98P+KqzJ49W/n5+c4jKyurxrkAqH+xsbEaOHBghfetYRgaNGiQYmNjLcoMAAAAqLs6FcXbt2+v7du3V2jfvn27OnTo4G5OAHxMSEiIW3EAALxRbGysWrVqVWmsVatWfCENoNbqayzfvHlznXvuuRowYIBSUlJ04YUXKi0tTREREZKknJwcl/65ubkVZo//VmBgoEJCQlwOANYxDEPjxo2rsEy6aZoaN27cWS9yAQDAV2RmZmrcuHFn3SYIgGep0/LpkyZN0v/93//pxx9/VFxcnAzD0Geffaa5c+fqj3/8Y33nCMDL7dq1y604AADeKCsrS4WFhZXGCgsLlZWVxdKlAGqlocbypmmquLhYXbp0UUREhDIyMtS3b19JUklJiTZt2qS5c+fW19MA0MBM09Tq1atlGIZLYdwwDK1atUr9+vWjMA4A8GlFRUVKTU3V4cOHlZqaqn79+slms1mdFoBq1Kko/thjjyk4OFgLFizQ7NmzJUmRkZFKTk7W1KlT6zVBAN4vMjLSrTgAAN6ofOnSyvb0ZOlSAHVRH2P5hx9+WKNGjVJMTIyOHj2qVatW6ZNPPtH69etlGIaSkpI0Z84cdevWTd26ddOcOXPUokULJSQkNORTA1CPqtpT3DRN9hQHAEDSypUrlZeXJ0nKy8tTenq67rzzTouzAlCdOhXFDcPQ9OnTNX36dB09elSSFBwcXK+JAfAd7dq1cysOAIA3Kl+6tLIvpVm6FEBd1MdY/tChQ7rtttt08OBBhYaGqk+fPlq/fr1GjBghSZo5c6ZOnDihyZMn68iRIxo8eLA2bNjAdwZAE1J+Yd6XX36p0tJSZ7u/v7/69+/PhXkAAJ+WnZ2t9PR052oqpmkqPT1dI0eOVHR0tMXZATibOu0pXi43N1fbt2/X119/rZ9//tmtRFJSUpxXlZczTVPJycmKjIxUUFCQ4uPjKyyjXFxcrPvvv1/t2rVTy5Ytde211yo7O9utXAA0rrKyMrfiAAB4o/KlSyuzatWqCvt8AkBNuTOWf+2117Rv3z4VFxcrNzdXGzdudBbEpdOF9+TkZB08eFBFRUXatGmTevXqVd9PAUADMgxD06ZNq7KdC/MAAL7KNE2lpaVV2c44HfBsdSqKFxQU6LbbblNkZKSGDh2qP/zhD4qMjNStt96q/Pz8Wp/Pbrfr5ZdfVp8+fVza582bp9TUVC1evFh2u10REREaMWKE84p2SUpKStKaNWu0atUqffbZZyosLNTVV1/tciUrAM+2ZcsWt+IAAHijqpYuleRcuhQAaqO+x/IAvFd0dLQSEhKcBXDDMJSQkKCoqCiLMwMAwDrl4/Qz60+lpaWM04EmoE5F8YkTJ2rz5s16//339euvvyo/P1/vvfeetm7dqkmTJtXqXIWFhUpMTNQrr7yiNm3aONtN09TChQv1yCOPaMyYMerVq5eWLVum48ePKz09XZKUn5+v1157TQsWLNDw4cPVt29frVixQjt27NDGjRvr8tQAWODqq692Kw4AgDeKiYlRq1atKo21atVKMTExjZwRgKauPsfyALxfYmKiwsLCJJ3e1iwhIcHijAAAsFb5FiOVGTRoEFuMAB6uTkXx999/X3/72990+eWXKyQkRMHBwbr88sv1yiuv6P3336/Vue677z5dddVVGj58uEv73r17lZOTo5EjRzrbAgMDNXToUGVmZkqStm3bppMnT7r0iYyMVK9evZx9KlNcXKyCggKXA4B1/P393YoDAOCNHA6HCgsLK40VFhZyBTqAWqvPsTwA72ez2TRq1Cj5+fnpiiuukM1mszolAAAsZRiGhg0bVmnssssuY4sRwMPVqSgeFham0NDQCu2hoaEus72rs2rVKn355ZdKSUmpEMvJyZEkhYeHu7SHh4c7Yzk5OWrevHmFx/xtn8qkpKQoNDTUeTDLBrBWbm6uW3EAALxRdXuRsVcZgNqqr7E8AN9QVFSkdevWqaysTOvWrVNRUZHVKQEAYKmysjI9//zzlcaef/55lZWVNXJGAGqjTkXxRx99VDNmzNDBgwedbTk5OXrwwQf12GOP1egcWVlZmjZtmlasWHHWK03PvLLGNM1qr7aprs/s2bOVn5/vPLKysmqUM4CGcejQIbfiAAB4I4riAOpbfYzlAfiOlStXKi8vT5KUl5fn3M4QAABftXnz5ipXHi4oKNDmzZsbOSMAtRFQlzu98MIL+uGHH9SpUyfnHgkOh0OBgYH6+eef9dJLLzn7fvnll5WeY9u2bcrNzVX//v2dbaWlpfr000+1ePFi7d69W9LpAXrHjh2dfXJzc52zxyMiIlRSUqIjR464XNWem5uruLi4KvMPDAxUYGBgHZ45gIbw/fffuxUHAMAb/bZoVVW8a9eujZQNAG9QH2N5AL4hOztb6enpzovwTNNUenq6Ro4cqejoaIuzAwDAGoMHD1ZISEilhfHQ0FANHjzYgqwA1FSdiuKjR492+4GHDRumHTt2uLTdcccdOv/88zVr1ix17dpVERERysjIUN++fSVJJSUl2rRpk+bOnStJ6t+/v5o1a6aMjAyNHTtW0ukvB3fu3Kl58+a5nSOAxrF371634gAAeKPIyEi34gBwpvoYywPwfqZpKi0trcr2efPmsWcqAMAn+fn56fHHH9cDDzxQIfbEE0/Iz69OizMDaCR1Koo/8cQTbj9wcHCwevXq5dLWsmVLhYWFOduTkpI0Z84cdevWTd26ddOcOXPUokULJSQkSDp95c1dd92lP/7xjwoLC1Pbtm31wAMPqHfv3ho+fLjbOQJoHOHh4VUuO1MeBwDA13Tq1EnNmzdXSUlJhVjz5s3VqVMnC7IC0JTVx1gegPdzOByy2+0V2ktLS2W32+VwOPg7BADgswYMGKDevXu7TPrs06eP+vXrZ2FWAGqizpet/Prrr3r11Vc1e/Zs/fLLL5JOL6924MCBektu5syZSkpK0uTJkzVgwAAdOHBAGzZsUHBwsLPPs88+q9GjR2vs2LEaMmSIWrRooXfffVf+/v71lgeAhtWnTx+34gAAeKP9+/dXWhCXTq+gtH///kbOCIA3aIyxPICmLTY2VgMHDqzw3Zq/v78GDRrk3H4BAABf9ac//ck5K9zPz09PPfWUxRkBqIk6zRT/5ptvNHz4cIWGhmrfvn2aNGmS2rZtqzVr1mj//v1avnx5nZL55JNPXG4bhqHk5GQlJydXeR+bzaZFixZp0aJFdXpMANb773//61YcAABv9PXXX1cb79KlSyNlA8AbNNRYHoB3MQxD06ZN0/jx4yttZ+l0AICva926tRITE7Vy5UolJiaqdevWVqcEoAbqNFN8xowZmjBhgvbs2SObzeZsHzVqlD799NN6Sw6Ab9i1a5dbcQAAvNHChQvdigPAmRjLA6ip6OhoJSQkOAvghmEoISFBUVFRFmcGAIBnuOuuu/TRRx/prrvusjoVADVUp6K43W7X3XffXaE9KipKOTk5bicFwLcEBga6FQcAAABQPcbyAGojMTFRYWFhkqR27dopISHB4owAAACAuqtTUdxms6mgoKBC++7du9W+fXu3kwLgW87cp6y2cQAAAADVYywPoDZsNptmzJih8PBwTZ8+3WWFCQAAAKCpqVNR/LrrrtNTTz2lkydPSjq9hJLD4dBDDz2kG264oV4TBOD9mjVr5lYcAAAAQPUYywMAAAD1IzMzU+PGjVNmZqbVqQCooToVxf/yl7/o559/VocOHXTixAkNHTpU5557roKDg/XMM8/Ud44AvFxZWZlbcQAAAADVYywPoDaKioqUmpqqQ4cOKTU1VUVFRVanBACAR+AzEmiaAupyp5CQEH322Wf6+OOPtW3bNpWVlalfv34aPnx4fecHwAecOHHCrTgAAACA6jGWB1AbK1euVF5eniQpLy9P6enpuvPOOy3OCgAA6/EZCTRNtS6Kl5WVaenSpXrrrbe0b98+GYahLl26KCIiQqZpyjCMhsgTgBc7deqUW3EAAAAAZ8dYHkBtZGdnKz09XaZpSpJM01R6erpGjhyp6Ohoi7MDAMA6fEYCTVetlk83TVPXXnutJk6cqAMHDqh3797q2bOn9u/frwkTJuj6669vqDwBAAAAAEAdMJYHUBumaSotLa3K9vIiAAAAvobPSKBpq9VM8aVLl+rTTz/Vhx9+qEsvvdQl9tFHH2n06NFavny5br/99npNEoB369u3r7766quzxgEAAADUDWN5ALXhcDhkt9srtJeWlsput8vhcKhTp04WZAYAgLX4jASatlrNFH/jjTf08MMPVxhES9Jll12mhx56SCtXrqy35AD4hl9++cWtOAAAAICqMZYHUBuxsbHq3bt3pbE+ffooNja2kTMCAMAzxMbGauDAgfL393dp9/f316BBg/iMBDxcrYri33zzja644ooq46NGjdLXX3/tdlIAfEvr1q3digMAAACoGmN5APWFZWEBAL7MMAxNmzatynbDMCzICkBN1aoo/ssvvyg8PLzKeHh4uI4cOeJ2UgB8yznnnONWHAAAbxQQcPadjqqLA0A5xvIAasPhcGjHjh2Vxnbs2CGHw9HIGQEA4Dmio6OVkJDgLIAbhqGEhARFRUVZnBmA6tSqKF5aWnrWL9/8/f116tQpt5MC4FvOtp94TeIAAHijdu3auRUHgHKM5QHURvnSsJVhaVgAAKTExESFhYVJOj02T0hIsDgjADVRq+klpmlqwoQJCgwMrDReXFxcL0kB8C3t2rXT3r17zxoHAMDXtGnTRjk5OWeNA0BNMJYHUBuGYWjcuHGy2+0VYuPGjWNpWACAz7PZbJoxY4bS0tI0bdo02Ww2q1MCUAO1KoqPHz++2j633357nZMB4Juqm5XCrBUAgC+67rrr9N133501DgA1wVgeQG2YpqnVq1fLMAyXPcQNw9CqVavUr18/CuMAAABocmpVFF+yZElD5QHAh7Vv396tOAAA3mjDhg3Vxq+44opGygZAU1afY/mUlBS99dZb+u9//6ugoCDFxcVp7ty56t69u7OPaZp68skn9fLLL+vIkSMaPHiw/vrXv6pnz571lgeAhuNwOCqdJW6apux2uxwOhzp16mRBZgAAeIaioiKlpqbq8OHDSk1NVb9+/ZgtDjQBtdpTHAAawq+//upWHAAAb9S8eXO34gDQEDZt2qT77rtPX3zxhTIyMnTq1CmNHDlSx44dc/aZN2+eUlNTtXjxYtntdkVERGjEiBE6evSohZkDqKnyPcXPnA1uGAZ7igMAIGnlypXKy8uTJOXl5Sk9Pd3ijADUBEVxAJbbuXOnW3EAALzRnj173IoDQENYv369JkyYoJ49e+rCCy/UkiVL5HA4tG3bNkmnZ5IuXLhQjzzyiMaMGaNevXpp2bJlOn78OF8WAk1E+Z7iv106XTr9/mZPcQCAr8vOzlZ6errzc9I0TaWnpys7O9vizABUh6I4AMsVFxe7FQcAwBtVt2c4e4oD8AT5+fmSpLZt20qS9u7dq5ycHI0cOdLZJzAwUEOHDlVmZmal5yguLlZBQYHLAcA6v91T/LfK9xQ/s1gOAICvME1TaWlpFT4Ly8rKKm0H4FkoigOwXLNmzdyKAwDgjb7++mu34gDQ0EzT1IwZM/S73/1OvXr1kiTl5ORIksLDw136hoeHO2NnSklJUWhoqPOIiYlp2MQBnFX5nuKVzRQv31McAABfVP4ZWVZW5tJeVlbGZyTQBFAUB2A59kwFAKAim83mVhwAGtqUKVP0zTff6I033qgQO3OGqWmaVS65PHv2bOXn5zuPrKysBskXQM3ExsbqvPPOqzTWvXt39hQHAPgsPiOBpo2iOADLdezY0a04AADe6NZbb3UrDgAN6f7779fatWv18ccfKzo62tkeEREhSRVmhefm5laYPV4uMDBQISEhLgcA65imqZ9++qnS2IEDB1gaFgDgs/iMBJo2iuIALLdv3z634gAAeKNNmza5FQeAhmCapqZMmaK33npLH330kbp06eIS79KliyIiIpSRkeFsKykp0aZNmxQXF9fY6QKog82bN6uwsLDSWGFhoTZv3tzIGQEA4Bn4jASaNoriACwXGBjoVhwAAG/06aefuhUHgIZw3333acWKFUpPT1dwcLBycnKUk5OjEydOSDq9bHpSUpLmzJmjNWvWaOfOnZowYYJatGihhIQEi7MHUBODBw+ucsWG0NBQDR48uJEzAgDAM/AZCTRtFMUBWK78C7S6xgEA8EaHDh1yKw4ADeGFF15Qfn6+4uPj1bFjR+exevVqZ5+ZM2cqKSlJkydP1oABA3TgwAFt2LBBwcHBFmYOoKb8/Pz0+OOPVxp74okn5OfH14kAAN/k5+enyZMnVxqbPHkyn5GAhwuwOgEAKCsrcysOAIA3Ki0tdSsOAA2hJvskGoah5ORkJScnN3xCABrEgAED1L17d+3evdvZdv7556tfv34WZgUAgLVM09SHH35YaWzjxo0aOXKkDMNo5KwA1BSXrQCwXIsWLdyKAwAAAACA+nX48OGz3gYAwNc4HA7Z7fZKY3a7XQ6Ho5EzAlAbFMUBWC40NNStOAAAAAAAqD/r169XXl6eS9vhw4e1fv16izICAMB6sbGxVe4pHhISotjY2EbOCEBtsHw6UA3TNFVUVGR1Gl4tKipK2dnZZ42zr3jDsdlsLOsDAB7ozCVLK4sDAADUt9LSUs2fP7/S2Pz58zVixAj5+/s3clYAAFivuLhYBQUFlcYKCgpUXFwsm83WyFkBqCmK4kA1ioqKNGrUKKvT8GmbN2/m36ABrVu3TkFBQVanAQA4Q3FxsVtxAACAunjvvfdUWlpaaay0tFTvvfeerrvuukbOCgAA6z344IPVxhctWtRI2QCoLZZPBwAAADzQueee61YcAACgLq6++uoqZ4IHBATo6quvbuSMAADwDFWtpFLTOABrMVMcqIbNZtO6deusTsPrbdy4UQsWLKjQ/sADD2jYsGEWZOQ7WNIHADxTs2bN3IoDAADUhb+/vx588EH9+c9/rhCbOXMmS6cDAHyWzWarcquzHj168D0r4OEoigPVMAyDpaUbwTXXXKNXX31V+fn5zrbWrVtzBToAwGdVtWxpTeMAAAB1dcUVV+jVV1/V4cOHnW3t27fXyJEjLcwKAABrlZWV6eDBg5XGDhw4oLKyMvn5sUAz4Kl4dwLwGM8995zL7ZdeesmiTAAAsN6JEyfcigMAALjjD3/4w1lvAwDgazZv3qyCgoJKYwUFBdq8eXMjZwSgNiiKA/AYHTp0cP78+9//XuHh4RZmAwCAtTp27OhWHAAAoK6ys7P1zjvvuLS98847ys7OtigjAACsN3jwYDVv3rzSWPPmzTV48OBGzghAbVAUB+CRHn74YatTAADAUtUtucaSbAAAoCGYpqm0tLQq203TtCArAACsV1ZWppKSkkpjJSUlKisra+SMANQG36QBAAAAHsgwDLfiAAAAdeFwOGS321VaWurSXlpaKrvdLofDYVFmAABYa/ny5W7FAVjL0qJ4SkqKBg4cqODgYHXo0EGjR4/W7t27XfqYpqnk5GRFRkYqKChI8fHx2rVrl0uf4uJi3X///WrXrp1atmypa6+9luWcAAAA0KSdd955bsUBAADqIjY2VgMHDqw0NmjQIMXGxjZyRgAAeIbbb7/drTgAa1laFN+0aZPuu+8+ffHFF8rIyNCpU6c0cuRIHTt2zNln3rx5Sk1N1eLFi2W32xUREaERI0bo6NGjzj5JSUlas2aNVq1apc8++0yFhYW6+uqrK1zRCgAAADQVW7ZscSsOAABQF4ZhqEOHDpXG2rdvz2o1AACfFRAQoJtvvrnS2C233KKAgIBGzghAbVhaFF+/fr0mTJignj176sILL9SSJUvkcDi0bds2SadniS9cuFCPPPKIxowZo169emnZsmU6fvy40tPTJUn5+fl67bXXtGDBAg0fPlx9+/bVihUrtGPHDm3cuNHKpwcAAADU2Q8//OBWHAAAoC5Onjyp999/v9LY+++/r5MnTzZyRgAAeAbTNPW///2v0tgPP/wg0zQbOSMAteFRe4rn5+dLktq2bStJ2rt3r3JycjRy5Ehnn8DAQA0dOlSZmZmSpG3btunkyZMufSIjI9WrVy9nnzMVFxeroKDA5QAAAAA8ic1mcysOAABQF4sWLXIrDgCAt3I4HLLb7ZXG7Ha7HA5HI2cEoDY8pihumqZmzJih3/3ud+rVq5ckKScnR5IUHh7u0jc8PNwZy8nJUfPmzdWmTZsq+5wpJSVFoaGhziMmJqa+nw4AAADglry8PLfiAAAAdXH//fe7FQcAwFvFxsbqvPPOqzTWvXt3xcbGNnJGAGrDY4riU6ZM0TfffKM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      "text/plain": [
       "<Figure size 2000x1500 with 7 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "features = ['age', 'balance', 'day', 'duration','pdays','previous','campaign']\n",
    "\n",
    "plt.figure(figsize=(20, 15))\n",
    "\n",
    "for i, feature in enumerate(features, 1):\n",
    "    plt.subplot(5, 2, i)#5rows, 2 columns\n",
    "    sns.boxplot(y=feature, x='deposit', data=bank)\n",
    "    plt.title(f'Box plot of {feature} with Deposit')\n",
    "    plt.xlabel(feature)\n",
    "    plt.ylabel('Deposit')\n",
    "\n",
    "plt.tight_layout()\n",
    "plt.show()\n",
    "#hw:write the insights from this output to explain which of the 5 features are impacted by target and which are not"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "34a114f4",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "e1ab1e92",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['age', 'balance', 'day', 'duration', 'campaign', 'pdays', 'previous']"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "numerical_df=list(bank.select_dtypes(exclude=['object']).dtypes.index)\n",
    "numerical_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "ff19ca39",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>age</th>\n",
       "      <th>balance</th>\n",
       "      <th>day</th>\n",
       "      <th>duration</th>\n",
       "      <th>campaign</th>\n",
       "      <th>pdays</th>\n",
       "      <th>previous</th>\n",
       "      <th>deposit</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>59</td>\n",
       "      <td>2343</td>\n",
       "      <td>5</td>\n",
       "      <td>1042</td>\n",
       "      <td>1</td>\n",
       "      <td>-1</td>\n",
       "      <td>0</td>\n",
       "      <td>yes</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>56</td>\n",
       "      <td>45</td>\n",
       "      <td>5</td>\n",
       "      <td>1467</td>\n",
       "      <td>1</td>\n",
       "      <td>-1</td>\n",
       "      <td>0</td>\n",
       "      <td>yes</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>41</td>\n",
       "      <td>1270</td>\n",
       "      <td>5</td>\n",
       "      <td>1389</td>\n",
       "      <td>1</td>\n",
       "      <td>-1</td>\n",
       "      <td>0</td>\n",
       "      <td>yes</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>55</td>\n",
       "      <td>2476</td>\n",
       "      <td>5</td>\n",
       "      <td>579</td>\n",
       "      <td>1</td>\n",
       "      <td>-1</td>\n",
       "      <td>0</td>\n",
       "      <td>yes</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>54</td>\n",
       "      <td>184</td>\n",
       "      <td>5</td>\n",
       "      <td>673</td>\n",
       "      <td>2</td>\n",
       "      <td>-1</td>\n",
       "      <td>0</td>\n",
       "      <td>yes</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11157</th>\n",
       "      <td>33</td>\n",
       "      <td>1</td>\n",
       "      <td>20</td>\n",
       "      <td>257</td>\n",
       "      <td>1</td>\n",
       "      <td>-1</td>\n",
       "      <td>0</td>\n",
       "      <td>no</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11158</th>\n",
       "      <td>39</td>\n",
       "      <td>733</td>\n",
       "      <td>16</td>\n",
       "      <td>83</td>\n",
       "      <td>4</td>\n",
       "      <td>-1</td>\n",
       "      <td>0</td>\n",
       "      <td>no</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11159</th>\n",
       "      <td>32</td>\n",
       "      <td>29</td>\n",
       "      <td>19</td>\n",
       "      <td>156</td>\n",
       "      <td>2</td>\n",
       "      <td>-1</td>\n",
       "      <td>0</td>\n",
       "      <td>no</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11160</th>\n",
       "      <td>43</td>\n",
       "      <td>0</td>\n",
       "      <td>8</td>\n",
       "      <td>9</td>\n",
       "      <td>2</td>\n",
       "      <td>172</td>\n",
       "      <td>5</td>\n",
       "      <td>no</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11161</th>\n",
       "      <td>34</td>\n",
       "      <td>0</td>\n",
       "      <td>9</td>\n",
       "      <td>628</td>\n",
       "      <td>1</td>\n",
       "      <td>-1</td>\n",
       "      <td>0</td>\n",
       "      <td>no</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>11162 rows × 8 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       age  balance  day  duration  campaign  pdays  previous deposit\n",
       "0       59     2343    5      1042         1     -1         0     yes\n",
       "1       56       45    5      1467         1     -1         0     yes\n",
       "2       41     1270    5      1389         1     -1         0     yes\n",
       "3       55     2476    5       579         1     -1         0     yes\n",
       "4       54      184    5       673         2     -1         0     yes\n",
       "...    ...      ...  ...       ...       ...    ...       ...     ...\n",
       "11157   33        1   20       257         1     -1         0      no\n",
       "11158   39      733   16        83         4     -1         0      no\n",
       "11159   32       29   19       156         2     -1         0      no\n",
       "11160   43        0    8         9         2    172         5      no\n",
       "11161   34        0    9       628         1     -1         0      no\n",
       "\n",
       "[11162 rows x 8 columns]"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "numerical_columns= ['age', 'balance', 'day', 'duration', 'campaign', 'pdays', 'previous']\n",
    "numerical_df = bank[numerical_columns + ['deposit']]\n",
    "numerical_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "178947e4",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>age</th>\n",
       "      <th>balance</th>\n",
       "      <th>day</th>\n",
       "      <th>duration</th>\n",
       "      <th>campaign</th>\n",
       "      <th>pdays</th>\n",
       "      <th>previous</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>deposit</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>no</th>\n",
       "      <td>39.0</td>\n",
       "      <td>414.0</td>\n",
       "      <td>16.0</td>\n",
       "      <td>163.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>-1.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>yes</th>\n",
       "      <td>38.0</td>\n",
       "      <td>733.0</td>\n",
       "      <td>15.0</td>\n",
       "      <td>426.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>-1.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          age  balance   day  duration  campaign  pdays  previous\n",
       "deposit                                                          \n",
       "no       39.0    414.0  16.0     163.0       2.0   -1.0       0.0\n",
       "yes      38.0    733.0  15.0     426.0       2.0   -1.0       0.0"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "numerical_df.groupby('deposit').median()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "ce7d7b63",
   "metadata": {},
   "outputs": [],
   "source": [
    "#check the percentage of deposit=yes category within each of the job caegories passed within the different categories in column job\n",
    "#which job categories which look similar for which can be combined"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "id": "b6f58d72",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "management           :       1301\n",
      "blue-collar          :        708\n",
      "technician           :        840\n",
      "admin.               :        631\n",
      "services             :        369\n",
      "retired              :        516\n",
      "self-employed        :        187\n",
      "student              :        269\n",
      "unemployed           :        202\n",
      "entrepreneur         :        123\n",
      "housemaid            :        109\n",
      "unknown              :         34\n"
     ]
    }
   ],
   "source": [
    "# Creating a list 'jobs' with different categories\n",
    "\n",
    "jobs=['management','blue-collar','technician','admin.','services','retired','self-employed','student','unemployed','entrepreneur','housemaid','unknown']\n",
    "for j in jobs:\n",
    "    print(\"{:20} : {:10}\". format(j, len(bank[(bank.deposit == \"yes\") \n",
    "                                               & (bank.job ==j)])))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "15b9045b",
   "metadata": {},
   "source": [
    "step1:We are initiating a loop where j iterates over a list of job categories presented by the variable 'jobs'\n",
    "\n",
    "step2:We are printing the job category 'j' and the count of yes values in the deposit column for that job category\n",
    "\n",
    "step3:print(\"{:20} : {:10}\". format(j, len(bank[(bank.deposit == \"yes\") & (bank.job ==j)]))) this is a string formatting statement that prints the category j in a field of width 20 characters and the count of yes values for that job category in a field of width 10 characters.We are calculating count of rows where both deposit column is yes and the job category matches the current job category.\n",
    "\n",
    "In summary the output here provides a summary of how many people with each job category have made deposit yes in that data-set\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "id": "130f962f",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "management           :       1265\n",
      "blue-collar          :       1236\n",
      "technician           :        983\n",
      "admin.               :        703\n",
      "services             :        554\n",
      "retired              :        262\n",
      "self-employed        :        218\n",
      "student              :         91\n",
      "unemployed           :        155\n",
      "entrepreneur         :        205\n",
      "housemaid            :        165\n",
      "unknown              :         36\n"
     ]
    }
   ],
   "source": [
    "# Creating a list 'jobs' with different categories\n",
    "\n",
    "jobs=['management','blue-collar','technician','admin.','services','retired','self-employed','student','unemployed','entrepreneur','housemaid','unknown']\n",
    "for j in jobs:\n",
    "    print(\"{:20} : {:10}\". format(j, len(bank[(bank.deposit == \"no\") \n",
    "                                               & (bank.job ==j)])))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "id": "314242d8",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "11162"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "total_entries=len(bank)\n",
    "total_entries"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "id": "433a2591",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "management           :       1301 : 11.66%\n",
      "blue-collar          :        708 :  6.34%\n",
      "technician           :        840 :  7.53%\n",
      "admin.               :        631 :  5.65%\n",
      "services             :        369 :  3.31%\n",
      "retired              :        516 :  4.62%\n",
      "self-employed        :        187 :  1.68%\n",
      "student              :        269 :  2.41%\n",
      "unemployed           :        202 :  1.81%\n",
      "entrepreneur         :        123 :  1.10%\n",
      "housemaid            :        109 :  0.98%\n",
      "unknown              :         34 :  0.30%\n"
     ]
    }
   ],
   "source": [
    "#calculate the percentage of deposit=yes count for each of the job categories\n",
    "jobs=['management','blue-collar','technician','admin.','services','retired','self-employed','student','unemployed','entrepreneur','housemaid','unknown']\n",
    "for j in jobs:\n",
    "    # Count the number of 'deposit=yes' for the current job category\n",
    "    deposit_yes_count = len(bank[(bank.deposit == \"yes\") & (bank.job == j)])\n",
    "    \n",
    "    # Calculate the percentage\n",
    "    percentage = (deposit_yes_count / total_entries) * 100\n",
    "    \n",
    "    # Print the result\n",
    "    print(\"{:20} : {:10} : {:5.2f}%\".format(j, deposit_yes_count, percentage))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "id": "550380c2",
   "metadata": {},
   "outputs": [],
   "source": [
    "bank['job'] = bank['job'].replace(['management', 'admin.'], 'white-collar')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "id": "7b2dc67c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "job\n",
       "white-collar     3900\n",
       "blue-collar      1944\n",
       "technician       1823\n",
       "services          923\n",
       "retired           778\n",
       "self-employed     405\n",
       "student           360\n",
       "unemployed        357\n",
       "entrepreneur      328\n",
       "housemaid         274\n",
       "unknown            70\n",
       "Name: count, dtype: int64"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "bank.job.value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "id": "fc6a7920",
   "metadata": {},
   "outputs": [],
   "source": [
    "# housemaid is a type of service provider, so we can combine services and housemaid\n",
    "# 'retired', 'student', 'unemployed', 'unknown' all of them come under unemployed only, so we can combine them.\n",
    "bank['job']=bank['job'].replace(['services', 'housemaid.'], 'pink-collar')\n",
    "bank['job']=bank['job'].replace(['retired','unemployed','unknown','student'], 'others')\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "id": "610cb3e5",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "poutcome\n",
       "unknown    8326\n",
       "failure    1228\n",
       "success    1071\n",
       "other       537\n",
       "Name: count, dtype: int64"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "bank.poutcome.value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "id": "6954735d",
   "metadata": {},
   "outputs": [],
   "source": [
    "#combine unknown into other in poutcome\n",
    "bank['poutcome']=bank['poutcome'].replace(['unknown'], 'other')\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "id": "ccd5a55d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "poutcome\n",
       "other      8863\n",
       "failure    1228\n",
       "success    1071\n",
       "Name: count, dtype: int64"
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "bank.poutcome.value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "id": "3e8fa56c",
   "metadata": {},
   "outputs": [],
   "source": [
    "#drop day,month & contact as day and month signify respectively the last contact day of the month and the last contact month of the year, which is not important for the bank to understand if the customer will do a deposit or not.And we are dropping contact column as every participent has been contacted either via cellular or unknown or telephone.\n",
    "bank.drop(['month','contact','day'],axis=1,inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "id": "0f63c300",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['age', 'job', 'marital', 'education', 'default', 'balance', 'housing',\n",
       "       'loan', 'duration', 'campaign', 'pdays', 'previous', 'poutcome',\n",
       "       'deposit'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "bank.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "id": "a493bd58",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th></th>\n",
       "      <th>age</th>\n",
       "      <th>job</th>\n",
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       "      <th>education</th>\n",
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       "      <th>balance</th>\n",
       "      <th>housing</th>\n",
       "      <th>loan</th>\n",
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       "      <th>campaign</th>\n",
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       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>59</td>\n",
       "      <td>white-collar</td>\n",
       "      <td>married</td>\n",
       "      <td>secondary</td>\n",
       "      <td>no</td>\n",
       "      <td>2343</td>\n",
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       "      <th>1</th>\n",
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       "      <td>married</td>\n",
       "      <td>secondary</td>\n",
       "      <td>no</td>\n",
       "      <td>45</td>\n",
       "      <td>no</td>\n",
       "      <td>no</td>\n",
       "      <td>1467</td>\n",
       "      <td>1</td>\n",
       "      <td>-1</td>\n",
       "      <td>0</td>\n",
       "      <td>other</td>\n",
       "      <td>yes</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>41</td>\n",
       "      <td>technician</td>\n",
       "      <td>married</td>\n",
       "      <td>secondary</td>\n",
       "      <td>no</td>\n",
       "      <td>1270</td>\n",
       "      <td>yes</td>\n",
       "      <td>no</td>\n",
       "      <td>1389</td>\n",
       "      <td>1</td>\n",
       "      <td>-1</td>\n",
       "      <td>0</td>\n",
       "      <td>other</td>\n",
       "      <td>yes</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>55</td>\n",
       "      <td>pink-collar</td>\n",
       "      <td>married</td>\n",
       "      <td>secondary</td>\n",
       "      <td>no</td>\n",
       "      <td>2476</td>\n",
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       "      <td>no</td>\n",
       "      <td>579</td>\n",
       "      <td>1</td>\n",
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       "      <td>other</td>\n",
       "      <td>yes</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>54</td>\n",
       "      <td>white-collar</td>\n",
       "      <td>married</td>\n",
       "      <td>tertiary</td>\n",
       "      <td>no</td>\n",
       "      <td>184</td>\n",
       "      <td>no</td>\n",
       "      <td>no</td>\n",
       "      <td>673</td>\n",
       "      <td>2</td>\n",
       "      <td>-1</td>\n",
       "      <td>0</td>\n",
       "      <td>other</td>\n",
       "      <td>yes</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11157</th>\n",
       "      <td>33</td>\n",
       "      <td>blue-collar</td>\n",
       "      <td>single</td>\n",
       "      <td>primary</td>\n",
       "      <td>no</td>\n",
       "      <td>1</td>\n",
       "      <td>yes</td>\n",
       "      <td>no</td>\n",
       "      <td>257</td>\n",
       "      <td>1</td>\n",
       "      <td>-1</td>\n",
       "      <td>0</td>\n",
       "      <td>other</td>\n",
       "      <td>no</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11158</th>\n",
       "      <td>39</td>\n",
       "      <td>pink-collar</td>\n",
       "      <td>married</td>\n",
       "      <td>secondary</td>\n",
       "      <td>no</td>\n",
       "      <td>733</td>\n",
       "      <td>no</td>\n",
       "      <td>no</td>\n",
       "      <td>83</td>\n",
       "      <td>4</td>\n",
       "      <td>-1</td>\n",
       "      <td>0</td>\n",
       "      <td>other</td>\n",
       "      <td>no</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11159</th>\n",
       "      <td>32</td>\n",
       "      <td>technician</td>\n",
       "      <td>single</td>\n",
       "      <td>secondary</td>\n",
       "      <td>no</td>\n",
       "      <td>29</td>\n",
       "      <td>no</td>\n",
       "      <td>no</td>\n",
       "      <td>156</td>\n",
       "      <td>2</td>\n",
       "      <td>-1</td>\n",
       "      <td>0</td>\n",
       "      <td>other</td>\n",
       "      <td>no</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11160</th>\n",
       "      <td>43</td>\n",
       "      <td>technician</td>\n",
       "      <td>married</td>\n",
       "      <td>secondary</td>\n",
       "      <td>no</td>\n",
       "      <td>0</td>\n",
       "      <td>no</td>\n",
       "      <td>yes</td>\n",
       "      <td>9</td>\n",
       "      <td>2</td>\n",
       "      <td>172</td>\n",
       "      <td>5</td>\n",
       "      <td>failure</td>\n",
       "      <td>no</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11161</th>\n",
       "      <td>34</td>\n",
       "      <td>technician</td>\n",
       "      <td>married</td>\n",
       "      <td>secondary</td>\n",
       "      <td>no</td>\n",
       "      <td>0</td>\n",
       "      <td>no</td>\n",
       "      <td>no</td>\n",
       "      <td>628</td>\n",
       "      <td>1</td>\n",
       "      <td>-1</td>\n",
       "      <td>0</td>\n",
       "      <td>other</td>\n",
       "      <td>no</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>11162 rows × 14 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       age           job  marital  education default  balance housing loan  \\\n",
       "0       59  white-collar  married  secondary      no     2343     yes   no   \n",
       "1       56  white-collar  married  secondary      no       45      no   no   \n",
       "2       41    technician  married  secondary      no     1270     yes   no   \n",
       "3       55   pink-collar  married  secondary      no     2476     yes   no   \n",
       "4       54  white-collar  married   tertiary      no      184      no   no   \n",
       "...    ...           ...      ...        ...     ...      ...     ...  ...   \n",
       "11157   33   blue-collar   single    primary      no        1     yes   no   \n",
       "11158   39   pink-collar  married  secondary      no      733      no   no   \n",
       "11159   32    technician   single  secondary      no       29      no   no   \n",
       "11160   43    technician  married  secondary      no        0      no  yes   \n",
       "11161   34    technician  married  secondary      no        0      no   no   \n",
       "\n",
       "       duration  campaign  pdays  previous poutcome deposit  \n",
       "0          1042         1     -1         0    other     yes  \n",
       "1          1467         1     -1         0    other     yes  \n",
       "2          1389         1     -1         0    other     yes  \n",
       "3           579         1     -1         0    other     yes  \n",
       "4           673         2     -1         0    other     yes  \n",
       "...         ...       ...    ...       ...      ...     ...  \n",
       "11157       257         1     -1         0    other      no  \n",
       "11158        83         4     -1         0    other      no  \n",
       "11159       156         2     -1         0    other      no  \n",
       "11160         9         2    172         5  failure      no  \n",
       "11161       628         1     -1         0    other      no  \n",
       "\n",
       "[11162 rows x 14 columns]"
      ]
     },
     "execution_count": 44,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "bank"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "id": "c3717b6c",
   "metadata": {},
   "outputs": [
    {
     "data": {
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",
      "text/plain": [
       "<Figure size 2000x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#check the overall distribution of age vs deposit using crosstab\n",
    "pd.crosstab(bank.age,bank.deposit).plot(kind=\"bar\",figsize=(20,6))\n",
    "plt.title('Deposit frequency for Age')\n",
    "plt.xlabel('Age')\n",
    "plt.ylabel('Deposit')\n",
    "plt.savefig('Deposit according to Age.png')#how to save an image in python\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "id": "a7ba6ea0",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/souravghoshhansda/anaconda3/envs/notebook/lib/python3.10/site-packages/seaborn/_oldcore.py:1498: FutureWarning: is_categorical_dtype is deprecated and will be removed in a future version. Use isinstance(dtype, CategoricalDtype) instead\n",
      "  if pd.api.types.is_categorical_dtype(vector):\n",
      "/Users/souravghoshhansda/anaconda3/envs/notebook/lib/python3.10/site-packages/seaborn/_oldcore.py:1498: FutureWarning: is_categorical_dtype is deprecated and will be removed in a future version. Use isinstance(dtype, CategoricalDtype) instead\n",
      "  if pd.api.types.is_categorical_dtype(vector):\n",
      "/Users/souravghoshhansda/anaconda3/envs/notebook/lib/python3.10/site-packages/seaborn/_oldcore.py:1498: FutureWarning: is_categorical_dtype is deprecated and will be removed in a future version. Use isinstance(dtype, CategoricalDtype) instead\n",
      "  if pd.api.types.is_categorical_dtype(vector):\n",
      "/Users/souravghoshhansda/anaconda3/envs/notebook/lib/python3.10/site-packages/seaborn/_oldcore.py:1498: FutureWarning: is_categorical_dtype is deprecated and will be removed in a future version. Use isinstance(dtype, CategoricalDtype) instead\n",
      "  if pd.api.types.is_categorical_dtype(vector):\n",
      "/Users/souravghoshhansda/anaconda3/envs/notebook/lib/python3.10/site-packages/seaborn/_oldcore.py:1498: FutureWarning: is_categorical_dtype is deprecated and will be removed in a future version. Use isinstance(dtype, CategoricalDtype) instead\n",
      "  if pd.api.types.is_categorical_dtype(vector):\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<Axes: xlabel='age', ylabel='balance'>"
      ]
     },
     "execution_count": 46,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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MZ2S8EjQJIYQQF6i4sKBa7VcTt912G6+++ippaWkMGTKE5ORkALp27cquXbto0aJFtV9rNpsZPXo0o0eP5u6776ZNmzZs2bKFrl27npGxStAkhBBCXKB6No0i0RJEptXuN69JAyRY1PIDZ8r111/PQw89xPvvv8+nn37qvf7f//6XUaNGkZyczNixY9FqtWzevJktW7bw9NNP8/HHH+N2u+nVqxfBwcHMmjULs9lM48aNz9hYJadJCCGEuEDptBoev7wdoAZIFZV9/vjl7c5ovabw8HCuvvpqQkNDGTNmjPf68OHD+eGHH1i8eDE9evSgd+/evPLKK96gKCIigvfff59+/frRsWNHli5dyoIFC4iOjj5jY9UoilL7KfEXKJvNhsViwWq1Eh4eXtfDEUIIcQGw2+0cOHCApk2bEhRUs2W0hVszeHLBdp+k8ERLEI9f3o4RKYm1NdRqDR06lLZt2/LGG2+cscc40esU6Pu3LM8JIYQQF7gRKYkMbZdw1iuC5+bmsmjRIn799VfefPPNM/pYtUGCJiGEEEKg02ro0/zMLW3507VrV/Ly8nj++edp3br1WX3smpCgSQghhBB14uDBg3U9hFMiieBCCCGEEAGQoEkIIYQQIgASNAkhhBBCBECCJiGEEEKIAEjQJIQQQggRAAmahBBCCCECIEGTEEIIIUQAJGgSQgghhAiAFLcUQgghBHjccGgFFB6F0Hho3Be0uroeVb0iM01CCCHEhW77fHgtBT4ZBd/eqv73tRT1+hk0aNAgpk6dysMPP0xUVBQJCQk88cQT3vbU1FSuuOIKQkNDCQ8PZ9y4cRw9evSMjulEJGgSQgghLmTb58NXk8CW7nvdlqFeP8OB0yeffEJISAirV6/mhRde4KmnnmLx4sUoisKYMWPIzc1l2bJlLF68mH379nHttdee0fGciCzPCSGEEBcqjxsWPgIofhoVQAMLH4U2I8/YUl3Hjh15/PHHAWjZsiVvvvkmS5cuBWDz5s0cOHCA5ORkAGbNmkX79u1Zu3YtPXr0OCPjORGZaRJCCCEuVIdWVJ1h8qGALU3td4Z07NjR5/PExESysrLYsWMHycnJ3oAJoF27dkRERLBjx44zNp4TkaBJCCGEuFAVBpgfFGi/GjAYDD6fazQaPB4PiqKg0Wiq9K/u+tkgQZMQQghxoQqNr91+tahdu3akpqZy+PBh77Xt27djtVpp27btWR8PSNAkhBBCXLga94XwJKC6mRsNhDdQ+51lQ4YMoWPHjlx//fWsX7+eNWvWMGnSJAYOHEj37t3P+nhAgiYhhBDiwqXVwYjnj39SOXA6/vmI5+qkXpNGo2HevHlERkYyYMAAhgwZQrNmzfjyyy/P+li8Y1IUxV/KvKgBm82GxWLBarUSHh5e18MRQghxAbDb7Rw4cICmTZsSFBRUs5tsn6/uoquYFB7eQA2Y2o2unYHWsRO9ToG+f0vJASGEEOJC1260WlZAKoKfkARNQohzky1D/eXusKk5GcGxYLbU9aiEOHdpddD0oroeRb0mQZMQ4txzdBt8fi1Yj++q0Wigw3Uw7Mk62eUjhLgwSCK4EOLcYj0Cn1xeHjABKAps/gJWvwfu0robmxDivCZBkxDi3HJ0OxTn+G9bMwMKMs/ueISoJ2Rf14nVxusjQZMQ4tySu6/6NkcBuB1nbyxC1ANlFbWLi4vreCT1W9nrU7kC+amQnCYhxLklrl31bcFRoK/hlmshzlE6nY6IiAiysrIACA4OrrNjRuojRVEoLi4mKyuLiIgIdLqa7wiUoEkIcW6JaQkRjSA/tWrbRf+AsMSzPyYh6lhCQgKAN3ASVUVERHhfp5qSoEkIcW4JT4JJ38O3t0LaevWaPgj6ToWO46SujLggaTQaEhMTiYuLo7RUNkNUZjAYTmuGqYwETUKIc09UM5jwjZoQ7iqBoAgIi5elOXHB0+l0tRIcCP8kaBJCnJtCotUPIYQ4S2T3nBBCCCFEACRoEkIIIYQIgARNQgghhBABkKBJCCGEECIAEjQJIYQQQgRAgiYhhBBCiABI0CSEEEIIEQAJmoQQQgghAiBBkxBCCCFEACRoEkIIIYQIgARNQgghhBABkKBJCCGEECIAEjQJIYQQQgRAgiYhhBBCiABI0CSEEEIIEQAJmoQQQgghAlDnQVNaWho33HAD0dHRBAcH07lzZ9atW+dtVxSFJ554gqSkJMxmM4MGDWLbtm0+93A4HNx7773ExMQQEhLC6NGjOXLkiE+fvLw8Jk6ciMViwWKxMHHiRPLz8336pKamcvnllxMSEkJMTAxTp07F6XSesecuhBBCiHNHnQZNeXl59OvXD4PBwM8//8z27dt5+eWXiYiI8PZ54YUXeOWVV3jzzTdZu3YtCQkJDB06lIKCAm+fadOmMXfuXObMmcPy5cspLCxk1KhRuN1ub58JEyawceNGFi5cyMKFC9m4cSMTJ070trvdbkaOHElRURHLly9nzpw5fPvttzz44INn5bUQQgghRD2n1KFHHnlE6d+/f7XtHo9HSUhIUJ577jnvNbvdrlgsFuXdd99VFEVR8vPzFYPBoMyZM8fbJy0tTdFqtcrChQsVRVGU7du3K4CyatUqb5+VK1cqgLJz505FURTlp59+UrRarZKWlubt88UXXygmk0mxWq0BPR+r1aoAAfcXQgghRN0L9P27Tmea5s+fT/fu3Rk7dixxcXF06dKF999/39t+4MABMjMzGTZsmPeayWRi4MCBrFixAoB169ZRWlrq0ycpKYmUlBRvn5UrV2KxWOjVq5e3T+/evbFYLD59UlJSSEpK8vYZPnw4DofDZ7mwIofDgc1m8/kQQgghxPmpToOm/fv3884779CyZUt++eUX7rzzTqZOncqnn34KQGZmJgDx8fE+XxcfH+9ty8zMxGg0EhkZecI+cXFxVR4/Li7Op0/lx4mMjMRoNHr7VDZ9+nRvjpTFYiE5OflUXwIhhBBCnCPqNGjyeDx07dqVZ599li5dunDHHXcwefJk3nnnHZ9+Go3G53NFUapcq6xyH3/9a9Knosceewyr1er9OHz48AnHJIQQQohzV50GTYmJibRr187nWtu2bUlNTQUgISEBoMpMT1ZWlndWKCEhAafTSV5e3gn7HD16tMrjZ2dn+/Sp/Dh5eXmUlpZWmYEqYzKZCA8P9/kQQgghxPmpToOmfv36sWvXLp9ru3fvpnHjxgA0bdqUhIQEFi9e7G13Op0sW7aMvn37AtCtWzcMBoNPn4yMDLZu3ert06dPH6xWK2vWrPH2Wb16NVar1afP1q1bycjI8PZZtGgRJpOJbt261fIzF0IIIcQ55ywkpVdrzZo1il6vV5555hllz549yuzZs5Xg4GDls88+8/Z57rnnFIvFonz33XfKli1blPHjxyuJiYmKzWbz9rnzzjuVhg0bKkuWLFHWr1+vXHLJJUqnTp0Ul8vl7TNixAilY8eOysqVK5WVK1cqHTp0UEaNGuVtd7lcSkpKijJ48GBl/fr1ypIlS5SGDRsq99xzT8DPR3bPCSGEEOeeQN+/6zRoUhRFWbBggZKSkqKYTCalTZs2ynvvvefT7vF4lMcff1xJSEhQTCaTMmDAAGXLli0+fUpKSpR77rlHiYqKUsxmszJq1CglNTXVp09OTo5y/fXXK2FhYUpYWJhy/fXXK3l5eT59Dh06pIwcOVIxm81KVFSUcs899yh2uz3g5yJBkxBCCHHuCfT9W6MoilK3c13nD5vNhsViwWq1Sn6TEEIIcY4I9P1bfxbHJISoS45CcBaCPgjMEXU9GiGEOOdI0CTE+c5ZBDl74ffnIWMjWBrCwEcgqQsER9X16IQQ4pxR5wf2CiHOIEWB1JXw3iDY9SPY0uDwavjsKlg3U519EkIIERAJmoQ4nxVkwPx7QfFUbfvtGSjKPvtjEkKIc5QETUKcz0rywJbuv83jhmN7zu54hBDiHCZBkxDnM43uxO06w9kZhxBCnAckaBLifBYcCTGt/LcZzBDV7OyORwghzmESNAlxPguNhytnqAFSRRoNjH5LbRdCCBEQKTkgxPkuoSPctQI2zVF30kW3gB63QWQTMATV9eiEEOKcIUGTEOc7nV5dhhv4CLjsoDNKLpMQQtSABE1CXCi0OjCG1PUohBDinCU5TUIIIYQQAZCgSQghhBAiABI0CSGEEEIEQIImIYQQQogASNAkhBBCCBEACZqEEKIecbk9uNx+DlgWQtQ5KTkghBD1wJG8YnYfLeS79UdQFLi6WwNaxYfRMDK4rocmhDhOgiYhhKhjR/KKeey7Lfy555j32o9bMujfIprnrupIwygJnISoD2R5Tggh6tjag3k+AVOZ5XtzWHMwtw5GJITwR4ImIYSoQ8cK7Hy++lC17bNXp5Jls5/FEQkhqiPLc0IIUZmzGAqzoDhHPdQ4OAbC4s/IQ7k8CvbS6hO/7aVu3B7ljDy2EOLUSNAkhBAVFWbDX6/D6nfA41KvxbSEcZ9BXJtaf7iYEBPD28ezJc3qt314+wTiwky1/rhCiFMny3NCCFHG44bNX8HK/5UHTADH9sAno8B6pNYfUq/XMqpTEkmWoCptiZYgruichE4nv6qFqA/kX6IQQpQpyITlL/lvK8qGo1vPyMM2iQ5h9uTe3NS3CbGhJmJCjdzYpzFfTO5N4+iQM/KYQohTJ8tzQghRxmWH4hPsVju6HVqNOCMP3TQmhEcubc0t/ZqgAHGhRswmwxl5LCFEzUjQJIQQZfRBEBxVfeAU1+6MPrzZoKdRtPxaFqK+kuU5IYQoE5YA/R/w3xYSAwkpZ3c8Qoh6RYImIYQoo9VBp+ug992grTDjE90CbvwRLA3rbmxCiDon88BCCFFRSCxc/E/oOfl4nSbzGa3TJIQ4d0jQJIQQlZlC1Y+opnU9EiFEPSLLc0IIIYQQAZCgSQghhBAiABI0CSGEEEIEQIImIYQQQogASNAkhBBCCBEACZqEEEIIIQIgQZMQQgghRAAkaBJCCCGECIAETUIIIYQQAZCgSQghhBAiABI0CSGEEEIEQIImIYQQQogASNAkhBBCCBEACZqEqEcURcHt8dT1MIQQQvihr+sBCCEgv8hJam4xn69JJb+klCs6JdG5UQSJFnNdD00IIcRxEjQJUcfyi53M+GMf7yzb7722cGsmzWNDmXVrT5IiJHASQoj6QJbnhKhjafklPgFTmX3ZhXy84iClbncdjEoIIURlEjQJUcfmrk+rtm3OmlRyCkvP4miEEEJUR4ImIepYgcNVbVtJqRtFUc7iaIQQQlRHgiYh6tjlHZOqbRvcJp4ws6QeCiFEfSBBkxB1rFV8KF0bRVS5bjboeGh4K0JNhrM/KCGEEFXIn7BC1LG48CDeuaEb8zak8cmKgxQ6XVzcOo6pg1vSOCq4rocnhBDiOI0iCRO1xmazYbFYsFqthIeH1/VwxDnG41E4VujAoyiEBxkINsnfNEIIcTYE+v4tv5WFqCe0Wg1x4UF1PQwhhBDVkJwmIYQQQogAnFbQ5HQ62bVrFy5X9VumhRBCCCHOBzUKmoqLi7n11lsJDg6mffv2pKamAjB16lSee+65Wh2gEEIIIUR9UKOg6bHHHmPTpk38/vvvBAWV52AMGTKEL7/8stYGJ4QQQghRX9QoEXzevHl8+eWX9O7dG41G473erl079u3bV2uDE0IIIYSoL2o005SdnU1cXFyV60VFRT5BlBBCCCHE+aJGQVOPHj348ccfvZ+XBUrvv/8+ffr0qZ2RCSGEEELUIzVanps+fTojRoxg+/btuFwuXn/9dbZt28bKlStZtmxZbY9RCCGEEKLO1WimqW/fvvz1118UFxfTvHlzFi1aRHx8PCtXrqRbt261PUYhhBBCiDonx6jUIjlGRQghhDj3BPr+XaOZpp9++olffvmlyvVffvmFn3/+uSa3FEIIIYSo12oUND366KO43e4q1xVF4dFHH63RQKZPn45Go2HatGk+93viiSdISkrCbDYzaNAgtm3b5vN1DoeDe++9l5iYGEJCQhg9ejRHjhzx6ZOXl8fEiROxWCxYLBYmTpxIfn6+T5/U1FQuv/xyQkJCiImJYerUqTidzho9FyGEEEKcf2oUNO3Zs4d27dpVud6mTRv27t17yvdbu3Yt7733Hh07dvS5/sILL/DKK6/w5ptvsnbtWhISEhg6dCgFBQXePtOmTWPu3LnMmTOH5cuXU1hYyKhRo3yCugkTJrBx40YWLlzIwoUL2bhxIxMnTvS2u91uRo4cSVFREcuXL2fOnDl8++23PPjgg6f8XIQQQghxnlJqID4+Xlm6dGmV64sXL1ZiY2NP6V4FBQVKy5YtlcWLFysDBw5U7rvvPkVRFMXj8SgJCQnKc8895+1rt9sVi8WivPvuu4qiKEp+fr5iMBiUOXPmePukpaUpWq1WWbhwoaIoirJ9+3YFUFatWuXts3LlSgVQdu7cqSiKovz000+KVqtV0tLSvH2++OILxWQyKVartdqx2+12xWq1ej8OHz6sACf8GiGEEELUL1arNaD37xrNNI0ePZpp06b5VP/eu3cvDz74IKNHjz6le919992MHDmSIUOG+Fw/cOAAmZmZDBs2zHvNZDIxcOBAVqxYAcC6desoLS316ZOUlERKSoq3z8qVK7FYLPTq1cvbp3fv3lgsFp8+KSkpJCUlefsMHz4ch8PBunXrqh379OnTvUt+FouF5OTkU3ruQgghhDh31ChoevHFFwkJCaFNmzY0bdqUpk2b0rZtW6Kjo3nppZcCvs+cOXNYv34906dPr9KWmZkJQHx8vM/1+Ph4b1tmZiZGo5HIyMgT9vFXvTwuLs6nT+XHiYyMxGg0evv489hjj2G1Wr0fhw8fPtlTFkIIIcQ5qkbFLctmaRYvXsymTZswm8107NiRAQMGBHyPw4cPc99997Fo0SKfQ38rq3wsi6IoJz2qpXIff/1r0qcyk8mEyWQ64ViEEEIIcX6oUdAEapAxbNgwn6WxU7Fu3TqysrJ8imG63W7++OMP3nzzTXbt2gWos0CJiYnePllZWd5ZoYSEBJxOJ3l5eT6zTVlZWfTt29fb5+jRo1UePzs72+c+q1ev9mnPy8ujtLS0ygyUEEIIIS5MNQ6ali5dytKlS8nKysLj8fi0ffTRRyf9+sGDB7NlyxafazfffDNt2rThkUceoVmzZiQkJLB48WK6dOkCgNPpZNmyZTz//PMAdOvWDYPBwOLFixk3bhwAGRkZbN26lRdeeAGAPn36YLVaWbNmDT179gRg9erVWK1Wb2DVp08fnnnmGTIyMrwB2qJFizCZTFLhXAghhBBADYOmJ598kqeeeoru3buTmJh40uUyf8LCwkhJSfG5FhISQnR0tPf6tGnTePbZZ2nZsiUtW7bk2WefJTg4mAkTJgDqMuGtt97Kgw8+SHR0NFFRUTz00EN06NDBm1jetm1bRowYweTJk5kxYwYAt99+O6NGjaJ169YADBs2jHbt2jFx4kRefPFFcnNzeeihh5g8ebJU9hZCCCEEUMOg6d133+Xjjz/2qXV0Jjz88MOUlJQwZcoU8vLy6NWrF4sWLSIsLMzb59VXX0Wv1zNu3DhKSkoYPHgwH3/8MTqdzttn9uzZTJ061buUOHr0aN58801vu06n48cff2TKlCn069cPs9nMhAkTTimpXQghRD3iOV6rT6s7cT8hTkGNzp6Ljo5mzZo1NG/e/EyM6ZwlZ88JcYYUZoPbCYYgCI6u69GI+qzgKGRtg3WfgEYD3W6GuLYQWnUXtRBlzujZc7fddhuff/55jQcnhBABKc6FbfPgk1HwZjf47GrY9xvYrXU9MlEfFWTC3Dtg1pWwfR5smwufjobv71GDKSFOU42W5+x2O++99x5LliyhY8eOGAwGn/ZXXnmlVgYnhLiAOYtg9QxY9lz5tfQNMGsMXPE2dLwWdDXeyyLORweXw/7fql7f8wscWQNtLz/7YxLnlRr9xtm8eTOdO3cGYOvWrT5tNUkKF6I6+cVOcoqcHCtwEG42EBNqIjZMamNdEIqy4c9q8gp/+Sc0HQARUoVfHFeSB6vfrb599bvQdCAESeqEqLkaBU2//eYnkheilh212fn33K0s3lE+rd4iLpQPJnWnSUxIHY5MnBX5h8Hj8t9mz1ffJCVoEmU8HnDZq28vLa7+50mIANUop0mIM63Y6eKlX3b5BEwAe7MKuXHmGo7aTvDLUZwfDNWfFACAVpbmRAXmCEi5uvr2DuPAHFl9uxABqPFvnbVr1/L111+TmpqK0+n0afvuu+9Oe2Diwnas0MHcDWl+2w7lFJOeX0J8+EneVMW5LSxRfZMryavaFt0CgmPO/phE/aXVQco1sOY9sKX7tlmS1XwmSR8Rp6lGM01z5syhX79+bN++nblz51JaWsr27dv59ddfsVgstT1GcQEqdrpxeaqvhpFplZmm815YIoz7FHRG3+umMLjmIwiTLeSikohkuOUX6HOPWmIgNB76TYObfwJLw7oenTgP1Gim6dlnn+XVV1/l7rvvJiwsjNdff52mTZtyxx13+JwTJ0RNhRj1mPRaHC6P3/aGkeazPCJx1ml1kNwbpqyC7d9D5mZo1AdajVBnDoTwJ6IRDH5cDZwAQmJAZzjx1wgRoBoFTfv27WPkyJEAmEwmioqK0Gg03H///VxyySU8+eSTtTpIceGJDTNxQ+/GfLj8QJW2dolhJFhOLWjKKXSQV+zE6VKICDYQHx6ETitT9fWe3gjRzeGiB0BRZHlFBEZvhHD5A17UvhoFTVFRURQUFADQoEEDtm7dSocOHcjPz6e4uLhWByguTEEGHXcObI6z1M0Xaw97l+p6N4vipbGdTqnswJ6jBUz7ciPb0m0ARAQb+M/IdgxpF4fFbDzJV4t6QwImIUQdq1HQdNFFF7F48WI6dOjAuHHjuO+++/j1119ZvHgxgwcPru0xigtUbJiJxy5ry20DmmEtKSXEpCc6xEhEcOCBzpG8YsbNWElecan3Wn5xKQ9+vYlPbu7BwNaSF3NeKckH62HY9KValqD9lRDfHsIS6npkQojzQI2CpjfffBO7XU3EfeyxxzAYDCxfvpyrrrqK//znP7U6QHFhCzbpaWyq+dbyVftzfQKmip5buJOUBhaiQ6VY5nmhJE+tIP779PJrG2ZBUhe47nMIT6q7sQkhzgs1Xp4ro9Vqefjhh3n44YdrbVBC1JY1B3KqbduZWVBtork4B+Uf9g2YyqRvgPWz4KKHQCcn3gshai7goMlmswV80xOdECzE2dQqPqzatgYRZvSSDH7+2HiCQ8TXvg9dJ0lysBDitAQcNEVERJz0XDlFUdBoNLjd7tMemBC1YWi7eF5YuAunu+qM0r2XtCROCmSeP0pyq29zFoIis4pCiNMTcNAk582Jc1FiRBCf3NKTO2b9jc2unjul0cBNfZswpK0kgZ9XUq6GzV/6b2s+GILOjcK7JaXqz6nZIMfECFHfaBRFqb7ssjglNpsNi8WC1WqVJcp6xOX2cLTAQVpeMcVON02iQ4gONRIWJAXvziu2DPjsasja5ntdHwS3L4O4NnUzrgAdtdnZdDifz1enogDjezaiS6MIOS5IiLMg0Pfv0wqaiouL/Z4917Fjx5re8pwmQZMQdcyaBms/gHUz1SW5FkNh8H8huiXo6u/MzVGbnXs/38Cag75LjD2aRPLmhK4SOAlxhgX6/l2j3yLZ2dncfPPN/Pzzz37bJadJCFEnLA3g4n9Cz8lqBfGgcPWsunpu1b6cKgETwNqDeazYd4wru8i5aULUBzU6sHfatGnk5eWxatUqzGYzCxcu5JNPPqFly5bMnz+/tscoRGBKS+DYXlj+Ksy/D7Z9D9YjdT0qcbbpDGpNJkuDcyJgspWU8umqQ9W2z1p5CGuxs9p2IcTZU6OZpl9//ZXvv/+eHj16oNVqady4MUOHDiU8PJzp06d7z6UT4qwptcPepfDVxPJdUus/Vk82v/EHiGpap8M715Q4XeQUOXG6PIQY9cRbZHnoTPEoCi4/uzvLON0ePJJ5KkS9UKOZpqKiIuLi1J1HUVFRZGdnA9ChQwfWr19fe6MTIlCFR+Gbm6tuK7cegYWPgj3wOmMXugxrCU8u2M4lLy3jkpeXMebtv5i/KR1rif/K6uL0WMwGrupa/fLbNd2SiQiWTQtC1Ac1Cppat27Nrl27AOjcuTMzZswgLS2Nd999l8REKR4n6kD6BnBXs4Sx5xcorr4yuCiXXeDgrs/WM2ftYW9tqwyrnalfbGDZ7uw6Ht35SaPRMKxdPE2ig6u0NY4OZni7+JPWyBNCnB01Wp6bNm0aGRkZADz++OMMHz6czz77DKPRyCeffFKrAxQiIHZr9W2KAh6ZJQlEWl4xGw/n+22b/tMOejaJIkGW6mpdYoSZzyf3Zt6GNL5edwRFURjbrSFXdm1IYoS5rocnhDiuRkHT9ddf7/3/zp07c/DgQXbu3EmjRo2IiYmptcEJEbCG3atvi2wKJikBEYiNR/Krbcuw2il2us7eYC4wSRFm7hjYnLHdkwGICjGik2N+hKhXarQ8B/Dhhx+SkpJCUFAQkZGRTJo0iXnz5tXi0IQ4BWEJ0Ha0/7bLXlTbxUklnKAekFGnxaCr8a8MEQCdVkNsmInYMJMETELUQzWaafrPf/7Dq6++yr333kufPn0AWLlyJffffz8HDx7k6aefrtVBCnFSwdFw2UuQ3AtWvAFFWZDUBYY9AwkXZrHVmmifZMFs0FFSWrXW2pguScSEGetgVEIIUT/UqCJ4TEwM//vf/xg/frzP9S+++IJ7772XY8eO1doAzyVSEbwe8HjUnXSKGwzBEBxV1yM6p7jcHtYdyuPGmWuwl5bvROzQIJz3JnaX/BohxHnpjFYEd7vddO9eNYekW7duuFyS8yDqkFYL4bKDs6b0Oi3dGkey+P6BbEu3kmm10zE5guRIM7FhkgAuzk/5xU5KnG60Wg2xoSa0sjQqqlGjoOmGG27gnXfe4ZVXXvG5/t577/kkiQshzj16nZbkqGCSo6pugb/gFWRC/mHI3gWRjSCquVp5/FS4XVCYodYQK7VDZGMIiQNT6JkZ83ms1O0mp9CJokCYWU+o6dTqWRU7Xew+Wsj0n3aw7lAeMaEmbh/QjMs7JcofCcKvgIOmBx54wPv/Go2GDz74gEWLFtG7d28AVq1axeHDh5k0aVLtj1IIIepa/iGYPQ6yd5ZfC42HSd9DXNvA7uFywKGV8PWk8jIZWh30ux96T4GQ6Nof93kqI7+EmSsOMmdNKiWlbi5pE88/hreiSXQI+gA3LGw6nM+ED1ZTlqSSabPz1A/bWbU/h+eu7kBUiOkMPgNxLgo4p+niiy8O7IYaDb/++utpDepcJTlNQpynSvLh6xth/+9V2ywN4dbF6nl3J5OzD97uBW4/dcOumQkpV53uSC8ImdYSrv9gDfuyC32umw06FtzbnxZxJ5+1O1bg4Lr3V7E3q9Bv+09TL6Jdkvwev1DUek7Tb7/9VisDE0KIc07RMf8BE6jLbAUZgQVN277zHzABLHsemlwEobE1HuaFYkNqfpWACaCk1M3/ft3D9Cs7EGw68dtbgcNVbcAEsOZgjgRNogopuiKEECfjKjlxeyDH9CgKHN1WfXv+oeqPAhJeHo/C95vSq23/bWcWNvvJTwDQazWcKN87LEjO+xNVSdAkxDmsxOnC6apaU6nY4aLU7fHzFaJGgsLBcIJyCxGNT34PjQYa9a2+Pa7tiR9DAOrLGGGuPqAJNenRBnBWX2SIgaHt/Be91Wk1dG8cWeMxivNXjXbPCSHqVmpOEetT8/lhczpBBh3jezaiVXwYRQ4Xy3Zn8/uubGJCjUzo1YgGEWbiTlDp+0JWUFLKsUIHu44WYjZqaRYTSny4CaNe59sxNAH6TlWX0CprdSmEBLik1mo4/Pp/4LBVbRv8hNQVC4BGo2FCr0bMWXvYb/ukPk2ICT15AneoycA/L2vDliP5pFvtFe4Pr4ztRFyYJIGLqmpU3FL4J4ng4mxIzS3i9k/XsTOzwHstJtTI55N7M/HD1Ry1OXz6Pzi0Fdf1TJYt1JXkFDp4+/e9fPTXQe/uqSCDljeu68JFLWMwGyv9TVmUDes/g79eVXe+6U3QeSIMfAjCAqwN5vFA1jb49la1bAGAORJGPA+tR0CQpfae4Hksv9jJe3/s5+3f9/lc79Iogneu73ZKh0qn55ewPjWP33Zm0SAymCs6JZFoCTppTpQ4vwT6/i1BUy2SoKl+yCl0UOpWCA3SnXLdlvqu1O3hjaV7+N+ve32uvzy2I4u2H+WXbUf9ft0v0y6idYL8TFY0f2MaU+dsrHJdq4HF9w+kub8dWGU1lpzFoA9SSw4YahCMFmapeVAelxo0hSWqpQdEwPKLnaTnlzB3Qzo2eymXd0qiVVyozKqKGjmjFcGFqI9yCh38tfcYb/22j+xCB90aR3D/kFY0iw0lyHB+vCFl5Jfw7bojVa63SQzn4W+3VPt1v+3MlqCpgmMFjiqBZxmPAt+uP8LDI9pUbdTpwZJ8+gMIjVM/RI1FBBuJCDbSLklm58TZI0GTOC/kFzt5edEuPl9TnueweHsWv+7M5ovJvejZ9PwoGqgADlfVBG9FAben+knjklI53qiiUo+HjAp5LJXtyy7E7fGg08peGSFEOfmNIM4L2QUOn4CpjNuj8K+5WzlW4PDzVeeeqBAjg9tWnaE4Vuiga6Pqd/sMai2zGhUFG3S0P0ENnn4tYiRgEkJUIb8VxHlhQ2p+tW17sgqxBlC35VwQFmTg9gHNsVTacv3Cwp38a2QbjH6OjxjSNp4EyfPwYQk28oi/5TfAYjZwSZtaCjLdLrClQX6qWiBTCHFOk6BJnBdMhhP/KOsCqNtyrmgWE8x3d/Xluh7JxIQaaRhpZnj7BJIsZubd3ZcR7eOJCjHSPDaUp65oz5Oj25EYIfV/KmsdH8YHN3b3CSg7NrTw1R19aFAbr1dBplqi4O0+8FoHmHUlHPwTHNVXoRZC1G+ye64Wye65unMop4hLXl7mN6+nT7Mo3p3YDYvZWAcjO3MKHaXkFDrRAA0izOiOzzLlFTmxlZSi02poGBVct4Os5xRF4ajNgc1eil6rISLYUDuHtBZmq2UFDiyr2nbDXGhxyek/hhCi1sjuOXFBiQ0z8eyVKTxSaQdZZLCB/xvT4bwLmEAtzuevpEJkiJHIkPPv+Z4JGo2GBEtQ1bo+hVmQdwgOr1ILWyb3VMsC6AN8Xa2H/QdMAD//A276GcIkz0yIc40ETeK8EGzUc1mHRDo1jGD26kMcybMzsHUsQ9rG0TBSZlvEKbClw5cTIe3v8ms6I4yfox6oG0jglLqy+racveAsACRoEuJcI0GTOG+EBRlok2jgydEpuDwKRv0FmLJXkAnHdsPeXyE8EVoMgfAkOdMsUC47/PGyb8AE6kG6X1wH96yFyCYnv0/wCUpcaPXqhxDinCP/csV5R6vVYDzR8eXnErcLCjPV5SJQzzgLSwCdn0rn1iMwe5x6TEcZjRaumameeSaB08kVZsPGz/y3uZ2QujqwoKlRbzUw8vipj9X+KgiJOa1hCiHqhgRNQtQD+cVO0vJL+PrvI+QXOxnVKYnuSUFEZK6EeXdCSZ7a0RQOl78OLYeBqcIxH6V2+OMl34AJQPHANzfDvesgqtnZe0LnKnepOttUnYL0wO4TmgBjP4GvJ4HHXX49ujkM/g8YQ05vnEKIOiFBkwiYrcTJsUInDpeHYKOOBhYz+gtxCayWWUtK+fivg7y2dI/32s9bM/nzpgSYM14NfMo4bGoQdPvvkNSl/HpRNmz63P8DKB448IcETYEwhqivU+5+/+3JvQO7jyEIWgyGu9fCnkVgTYNmAyG+vbpcKoQ4J0nQJAKSmlvMK4t38+PmdErdCrGhJqYObsGQdvEkWmTZ53Sk55f4BEwAI9vHEL7pfd+AqaI/X4YrZ5TPWHhc4DpB1fOy5b264PHAuVJdOywehj+r5i9VltDx1AJPg1mdWYq+q/bGV58V56kJ7hqtmtMly8HiPCRBkziptLxipn6xgY2H873Xsgsd/Od7dSlofI9GMuN0Gr5bn1blWnMLBKXtqP6LsneCs7g8aDKGQlw7yNruv3/TAbUw0lNky4DMzbDpCzCFQdcb1aAjOOrsj+VUNO4L47+Ahf+EvAPqzrmO18GgR9Wg6lxVcBQKMqAoC8IbqgcG10ZulcsJ2Tvg50fUXYNlr9fAhyGiFg43FqIekaBJnFSmzeETMFX0+tI9XNQqlibR51eORl6xkwK7Cy1q3aMQ05n7p2ItcVa5tjdfwR7VjqC09f6/KLYtGCqUUgiNhUufh09Hq6f3VtSgO0Q1rcURB8CWDl+Mh4yN5dfWfwo9b1eDjxPtLjubinPVQCJ1pRqAJveCkHhofRkkdQNnoZp0HxJ7bs+c5O6Hz8fBsQozmo36wtUfgKXB6d372B74YLCaDwZqwvyGT+HQcrjxh9O/vxD1iARN4qS2HMmvtu1YoZNih58dQnWlKAeKs9VZmOAoCIkDo/86TYrHgzM/DU1xDigKSnAMhCawO7uYxxdsZf2hfHRaDcPaxfPopW1ofIYCw1Edk/jq7yM+137ansNDE2+lwdbPqy7RaTRw0YNgqjSeBl3hxh9h4aPqDI8xFLrdDH2mQOgpzpC4nFB4FDylYAg5tRkWjxs2zfENmMqseQ86Xlt7QVNJvvomHRQO+lM8X68wC375J2z5uvyaRguj/wftxhx/zufwzFKZgqNVAyaA1BXw00PqMm9QDU8wsNvg16fKA6aKcvfDkbUSNInzigRN4qROlLOk12ow6XVncTQnkHsAvrkF0o/PzugM0PNO6HefOhNTQam9GOXwakzz71RrGwEER+Ma+Tpztsay/lA+AG6Pws9bM1l3KI+5U/rS4AwUymydEEbHBuFsTrN5rzlcHl7528nz181B//1dUJyjNgRFqLvnoltUvZExFJr0g4lzwVkEWp0aNAZaxbqMLR3+egPWfwKlxeqS2rBnoHE/MFtO/vVF2bD2g+rb//6IkthO5Ntd6DQaYsNMaE71bMCiY5C2Dpa/qi43NRkAfe6GyKagC/DX2q6ffQMmUAPU7++GBt0gru2pjam+KsysGjCV2f2z+v2qadDkLFQ3GVRnx3xod4Ua6AtxHpCgSZxUq4Qwwkx6CvzMKF2Wkkh0aD04ssOWAbPGQN7B8mvuUlj5PwiyQP9pvrWNrKkYv7jGt45OcQ76byZyz7W/MH+77/PNKnDw595jXNejUa0PPT48iPcmdeervw/z6cpDFDpcDGodxx1DWkG0Ge74U31jQ4HgGPU4jxMFBiExNc9VKcxWA8+KFa1z96u7+K6dBW1Hn/weikcNtqrjsDFr5UFe+3UfUSFGbruoKSM7JBIbFuBMUUm+Wl5h9Tvl13L2qblTty6CxE4nv0dhFvz1WvXt62fBiGcDG099V5RdfZuiqIFPTWm0YI6s/vsdmiABkzivSPauOKmGEWY+vKkHYZXyejo0sPDQ8NZEBNeDoCl3v2/AVNGKN8pnkwBXqQNWv+e/8KCiELP+f4ztVDVZedG2o5S6qtnN5kemtYS/D+by/cY0NqbmkVVQff2fBIuZCb0aMef23iy4pz//GdmWxlHB6PV6dXkjqbNaYiAiOfCZlJqwHq7+CJBf/qXm/5yMORJaXVZtc1bTK/lszRGKnW6O5JXwxPztPPrtFnIKT7D7r6LCo74BUxmXHX54QM1TOhlP6YmDCWuqb32lc1n4CZbHdAb1j4qaComDXndW3955Qs3vLUQ9JDNN4qQMei1dkiOYf29/dmbayMgvIaWBhaQIc/051+3Y7urbHDafv4Rd9iKCsjZX292Qs51WKVX/nogPN6ENsNL4/uxCJn20hiN5Jd5rLeJCmXlTD5KjfF8zt0dhe7qVu2av9/YPMmh5aFhrrunW8OwGpUf+rr4tPxUchRB2knsYzDDgQdi5QH3tK/DEtWevoRWpuak+15fuzCI9v4ToUNPJx3jwr+rb0v4Ge/7Jd+gZw9Sk771L/Le3HKEub54PQuOgcX81Mbuyrjep7TWl1ULHcerrWPmA4uHPyu45cd6RoEkExKDX0jQmhKYx9XSX3Inq5xhDfHY+6U3BlEa3xnBkrd/ursgWHLQqVa5f36sxugCCpuwCO5M/XecTMAHszSrk/q828v6k7kRWCITS80u47r1VFDnLZzbspR6e/nEHjaKCGdY+4aSPWWtO9Aaq1avbyQMR2RRu/01dRtv1E+iDcHe7mR0JY7jzq1S/X7J87zE6NIyo2mA9Atm7IXefmmeU2Ek9SqbC7KEPj0edefS41ArqYX5ev6BwGPxf2P9b1Rml0Hi1EOX5IjgarnoPfn4Udv2gLp/qjNDtFhjwgO8uzJoIS1B34eUdhN2/QFCYuvswNKHmuVJC1FMSNInzQ3QLNdfH3/JRj8k+u8f0xiBKe90Fm2dX3Z4PFPScxpw5vks8/7ysLY2iAntzOVboZF+2/zyRvw/mkVvo9Amaft+V5RMwVfTiL7vo2jiSmEBmYGpDUhd1F5q/o0RSrla33gdCq1W/J6NeUY8NQUsu4Yx5fhkud9XXHCA0yM95elk74JPLfZfSIpvCFW/Dd7dVXYpr0l9NTP7xfvXzqGZw2cvQqKeaKF9RTCt1S/yPD6iPo9FAi6Ew4rnzb4bE0gDGvAXFT6qzhUHh6r+J2iqjEBqnfiT3rJ37CVFPSdAkzg+WBjDpe7WSc9kRGBoNdLgOek8BvW/Q4YlogvOqTzD+cE/5EpIhGMfwF9DHtWH2rTr+2JON2ahjQMtYYsNMhPl7U/fDZvez/bqCYqdvLlVZDaz2SeGMTwkmRA+/H3bx47Yc9mUXnlIe1WkLS4TxX8LnY9Wt/GXi2qkzM9WUb6iWIdg7kxHscDGifQI/bK4a2Go00L9FpeT1ggx1q3zl3KO8A/D7dOh5h/rfMqZw6P8AzL29/Frufph9FdyyGJJ7VBqbWS1kOWm++jOg1YM56vydHQkKP3+fmxBniQRN4vwR2xpuXqhuQXcUHq94HOv3jcIUHIqr9QgcScvRFGapSxah8WjD4gkzmWkfAe0b1CxBNvYEs0J6rQaL2Tf46pwcyZiWetoVriZ664fgKGR402E8cPNN/N9fRRh0Z3G/ht6oBhL3rIXUVeqZaY16q8eB+FvmOpniXDUg0WgJCY7mkRFtWH8oj3Sr70zW/12RQlzl163gqJpH5c+RtTDyFTi6Td1S3+wSaDMKfnsGLv6X+n1XPIAGNsyCJY/DdbPVJPXKymZJznNHbXYyrXaO2uw0jAwmLtxUsxnMkjx1ByOor6c5ojaHKUS9JkGTOKvS80tIyy/ht51ZmAw6hraNIy7MREyg281PJiw+4EKMeqMJfXRjiG5cO499XHSoictSEvhpa9Wcm+t7NyImzPeN6qrWBozz/43h4G/ea8Eb3qfxjq9548ZFBIedpaW5MnojRDZRP2rK5VADmp8eUuspaXXQZjTJQx5n7pR+rD6Qwy/bMkm0mBnbvSFJEWZCgir9OrJbT/Igipqr43aCIVQNmLpOVAtWlu2kNIbCRQ+oQXRpsf+g6QKwP7uQm2auJTW3fENE52QLb1/fjaSIAJfoPG71+J6fHi5PKm/SHy57EWLanDvnCwpxGjSK4iepQ9SIzWbDYrFgtVoJD5dp8MrS8or597yt/LbLd7nlnotbMKlPY+LCaylwqgeO2uz8b+kevvr7CE63hyCDlpv6NuHW/s2IrRQEKQeXo/l4pN/7KB3Gobn8tfIz5s6i/GInpW6FUJMOs/EU/746ug3eG1i1UnR4Ety6GCwNURTlxEUtj+2BN7v7b9MHwd1rILJCwHvkb/UYGWdR1f5j3lXzlUJr4ay1c0yWzc61763iwLGqr8slbeJ47drOhJsDWHrOPQDv9q9a18kUptYSO9tH9QhRiwJ9/5aZJnHWLNmRVSVgAnjzt70Mah175oImj1vNj7Glq2+okY3V5RvTyfbO11x8eBD/HtWOOwY2p9jpJsSkIy4sCKOfg401W76p9j6aHd/DkCfOatCUU+Rgw6F83v59L8cKnfRqGsUdA5vTODo4sKVCR4E66+PvaA1bOhz4EzqPP3kV8JBYSBkLW7+u2tbnnqpHwxxe4z9gAlj1FrQcevKxn4eOFjj8BkwAv+3KIqfIefKgyV0Kaz/0XwjTUQDrPoZL/u1bQFaI81CdzqdOnz6dHj16EBYWRlxcHGPGjGHXrl0+fRRF4YknniApKQmz2cygQYPYtm2bTx+Hw8G9995LTEwMISEhjB49miNHfM/yysvLY+LEiVgsFiwWCxMnTiQ/P9+nT2pqKpdffjkhISHExMQwdepUnM6qh6mKU3ckr5hPVx6qtv3zNak4XWegmKCrVM3Nebc/fDhUrRr+v26w7AX1KI4zKMigIzkqmNYJYTSMDPYbMAFVktR9aA1ntaKytaSUN5fu5bZP/2Z9aj6pucV8ve4II9/4kx0ZtpPfANQ30YN+agKV2bnAf2HRyswRMPwZ6HtfedBojoSh/6cWVDRUCrLT1lV/r2O71SXDC1BuUfW/wxSFwM6OtNvU8gzV2f9blZpcQpyP6jRoWrZsGXfffTerVq1i8eLFuFwuhg0bRlFR+V9FL7zwAq+88gpvvvkma9euJSEhgaFDh1JQUODtM23aNObOncucOXNYvnw5hYWFjBo1Cre7/E14woQJbNy4kYULF7Jw4UI2btzIxIkTve1ut5uRI0dSVFTE8uXLmTNnDt9++y0PPvjg2XkxznNuj4K1pPpf3rlFTpynukusxKrW6rGf4Je17Qh8dqWavFpG8ahVwvcsOrXHO1M6Xlt9W+cJap2d4hz1MOIzvJqeXeBg5oqDVa47XB7+OXcLuUUBBB4a3YkP5A1LUvsEIixencGYshru+RvuXA69765yliBw4uNTIpsGXmPqbCjMVvODMreqdajOYPXxREv1M7hGnTawpTm98cTlJkJiQXeWc++EqAN1ujy3cOFCn89nzpxJXFwc69atY8CAASiKwmuvvca//vUvrrrqKgA++eQT4uPj+fzzz7njjjuwWq18+OGHzJo1iyFDhgDw2WefkZyczJIlSxg+fDg7duxg4cKFrFq1il69egHw/vvv06dPH3bt2kXr1q1ZtGgR27dv5/DhwyQlJQHw8ssvc9NNN/HMM8/4XeN0OBw4HOVvIjab/KVVHYvZQK+m0fy4xf8xHBe3jvNfp8efknw4uhV+mw55+yG2LQx6VP1vUKUlt90Lq59hWPY8NB8ccOL4GRPZGLrdDOtm+l6PaAS974LNX6pLI4oHulwPbS4/YyfHrzlQ/REkW9NsWEtcRIWc5M0xLB76ToUfpvlv73bjqc2e6Y2B1U1qe3z3nL8aUxf/y3+gdYoyrCXsPlrAliNWmsWE0DE5giSLOeBK8SgKZG2H7yareV+gBpiXvqguH56BkgAxoSb6t4hm+d6cKm3X925ETCBnR5rC1IOvq5tt6jsVTKH+24Q4j9Sr7Q5Wq7pbJipKPQLhwIEDZGZmMmzYMG8fk8nEwIEDWbFiBQDr1q2jtLTUp09SUhIpKSnePitXrsRisXgDJoDevXtjsVh8+qSkpHgDJoDhw4fjcDhYt87/tP/06dO9y30Wi4Xk5POsIF4tigg2cu8lLTD5WaJKCA9iYKsA39BKHbD1W/h4pLqDx5YO+5aqS297F4G70lLD0W3+7wOQf0g9g6w6Z2uPRHC0Opty049qQNR0IFz+hlp36seHYP69kLERMjfDz4/ArCvUUgBngF534jf/QGMDWl8GrUdVvX7p82oweCaEJ8PEeb4zIjojXPJfdZfXaTp4rIir317BjR+t5aVFu5ny+QYufe1PtqZbCXg/TX4qzLzU9+eyOAe+vQUyNp32GP2JCjHy4thOXN4pyVvR3qTXMvmiZkwZ1CLgJP/i6PaUdJ9S5XpJz3spjmpbq2MWor6qN4ngiqLwwAMP0L9/f1JSUgDIzFS3bMfH+84ExMfHc+jQIW8fo9FIZGRklT5lX5+ZmUlcXNU6LHFxcT59Kj9OZGQkRqPR26eyxx57jAceeMD7uc1mk8DpBBpHBfP1nX149qcdrNqfi16rYUT7BB4Y1oomgR7PUpgJi/7lv+3HB9XzxCwNy6816qPW6fEntq3/JYWCDHXZZNMXai5N1xvVnUEnWnI6XSExENIfGnQHxa0+7sY5akBY2bE9sGO+mtdTG/lOziIoLQFjMD2bRKHR+I8XezeLqlJjqlph8TD6dbD9A/b+pj6fFpeoR2ucqRkJvUH9/t+xTF3+cjnU2lIhsadelLOSvGInD369qUp9qQKHi1s+XsuCe/uTaDn51n1lz2I01ZRSUJY+iWbCVyc/N68GEi1mpl+ZwkPDWlHsdBNq0hMbZiLIEPj5er8ccHJIuYqrxo8jNG05aDQUJPXj292ltDjkYnRErQ9biHqn3gRN99xzD5s3b2b58qoJpJV32Zx0q7KfPv7616RPRSaTCZNJ1vEDZTbp6dgwgjeu60Kx041Goy7bndKBtIVH1Td4f0ry1OTuikFT04vU5OGKOU1lhjxZdcnGlgFf3qAe/Fpm/afQ/Va4+J9qcHMGWEucpOXbmbv+CHnFTqb0jafZ+o+r/4INs6DD2NMbj92mJkj/+bJ6rlt8Bxr1m8aHE9pzy2zfGbrwID1PXZFyat+rkBj1I7Fzzcd4qrRaCG+gftSi3EIn6w75+RlCPTYnw2o/edCkKGj8HZp7nObo1up/tmtBaJAh8CXwSqzFTmatPMT61HzeXKmhZbyaP7Z7cQZuj0L3Iy4GtYoNLD9KiHNYvQia7r33XubPn88ff/xBw4blb3gJCWoF4szMTBITE73Xs7KyvLNCCQkJOJ1O8vLyfGabsrKy6Nu3r7fP0aNHqzxudna2z31Wr17t056Xl0dpaWmVGShRc4X2UoqdbvZkFWDUa2kWE4rZoMMU6F+8Jzt5vnK7JRlu+gm+vVXNJQEIilB3YFU+VsPjgS1f+wZMZf7+EDqOI8sdSoHDhUGnJTrESIjp9P8JWUucfLriEC8v3u29lhjk4kHNCVbPNVrgNGaZXA7YsQC+r7Dckr0L7bbvGHTdHBbd1583f99Pps3ORS1iGNOlAQ0ja+mcsnOQ/SQ7O20lJz46BwCNBkdUG6r9M8uSjN2j41QKb7g9CsVO9efxVGaNTpVHAadb3ajh8ijsyCjwaXe6PXg8UvJPnP/qNGhSFIV7772XuXPn8vvvv9O0qW9xtKZNm5KQkMDixYvp0qULAE6nk2XLlvH8888D0K1bNwwGA4sXL2bcuHEAZGRksHXrVl544QUA+vTpg9VqZc2aNfTsqR4ouXr1aqxWqzew6tOnD8888wwZGRneAG3RokWYTCa6det25l+MC0BekYMPlh/knd/3Uvb71aTX8sq4TlzcJo7gQHIrQuPVoMeeX7UtPAm3IQTX/hUoRcfQxjTHExxHUHw79XyxkhxwOdXlj9AE0FV6vKJsNTiqhmftR7ytu4ePVx1Gp9VwaUoCj13ahgaRp7f0k55v9wmYAH7aXcSkvjcQe+gv/1/U9abTW8YpPAo/+dkZqnjQLriHVrcv46WxnXC6PJiNOm8uzKlIyyshPb+EX3cdJcSoZ0jbeGJCjbVX/f0sijAbCDHqqj1YOZDDnBVFwdr8cuL+etFvyYWc7vfj0UUEFDQpisKRvBK+35jG77uyiQ0zcWv/prSICz212cAARQQbuLJLQ7ambffbfnXXhliCZZZJnP/qNGi6++67+fzzz/n+++8JCwvz5g5ZLBbMZjMajYZp06bx7LPP0rJlS1q2bMmzzz5LcHAwEyZM8Pa99dZbefDBB4mOjiYqKoqHHnqIDh06eHfTtW3blhEjRjB58mRmzJgBwO23386oUaNo3bo1AMOGDaNdu3ZMnDiRF198kdzcXB566CEmT54s1b1ryd+H8njrt70+1xwuD/d8sYFfpg2gVXwAxSZDE+Cq92HOdb7btHVGlDHvoFswFd3BP72XXc2H4rz8dYwRDU6+e0pxV18cEdA6bZTo1BkFt0fhh80Z7Miw8fnk3sSfRmHOeRuqJnXvyy5kT1BHIhv0QJ+21rcxrj20ufT08pls6dUvBRVmQfExjOFJ1deWOokjecX8c+4W/thdXgvrpUW7eXBYK67tnnzOVX+PCzdx/9BWPP3jjiptV3ZJCugMN41Gw7bCcDSjZxH78+1qPSsAjZaCblPYbOxM5wDPGdyXXcjV76zEWmGG6+etmdw/pCW39GtKWC0vk2k06h8JH684wOFc35+bRlHBDG0Xf/JipUKcB+o0aHrnnXcAGDRokM/1mTNnctNNNwHw8MMPU1JSwpQpU8jLy6NXr14sWrSIsLDyN9hXX30VvV7PuHHjKCkpYfDgwXz88cfodOXT1bNnz2bq1KneXXajR4/mzTff9LbrdDp+/PFHpkyZQr9+/TCbzUyYMIGXXnrpDD37C0tekZPXl+7x26YoMGdNKv8a2e7kMxo6vZqndNdKtQrx0W2Q1AVPp/Fof30KKgRMAPp9iyn94xWcw/4PY9BJZgPMkequr/Wf+G0uaHUVa37zXZbYl13E3qzC0wqa8or916+aPPcIH1z1Gj17bke37njJga43QrOLayFnRwOGYOg5WU2WdznUIpv7lqqv62ks/Xk8HhZuzfQJmMq8vGg3F7WMOeeCJoNOx1VdGxJuNvDKot1k2uyEm/Xc1r8Z1/VMDjiXp0VSNA/PjWDqqJ9JULLQlJZQFNqYL7aVMDginsiQk88S2UpKeXLBdp+AqcyrS/ZweaekWg+aAJIizMy5vQ/f/H2Yb9YfQYOGa7o15JpuDQM/v06Ic5ycPVeL5Oy56mVa7Yx8409yqqlOPKhVLO9O7HZqeRluN7jtoA/CsftXTHOu8d9PH4TzztUYY5pUbVOU45XBFXV3XNZ2tZxB5R1Osa2xXz2Ljv/b783tKHPf4JbcP7RV4OOuZNmuLG6cudZv25C2cbx+XWdClBJ1rGZLjR/HR/5hyNoBa2bA3iXqNY0W2o2BDtdAUhf1nLgaOJxbzPUfrPY5HLai8T2SeebKFLTn4AGviqKQZXNgd7kx6LTEhZnQBzg7VPb129JtPPjVJg7lFmHUadHrtPx7ZFsubh0XUNCUmlvEgBd+r7b9qSvaM6lPk4DHdKrcbg85xU40QFSwEd0pPH8h6is5e07UKyEmHR0aWPh9d9Wz5wB6N4v2W8PphHQ60KmlCjx51R/RgsuO4m8pypqmbt1f/4k6i9PxWrWez7hZ6rU9i9XZl5SrofnFKBtmM6DVKJbs8H0OSRGnN2vSJjGclKRwtqb7Fkc16bX8Y3gbQkwGoJZnDvRBsOJ13+NOFA9s+w60emg6oMa39igK+Seo/p5T5MTlUTDWl/fa4lx1J6FWC+ZoMFVf/kKj0RB/ggrbJ5Nps/N/P2zn6m4NaRIdjMuj4HB5+GptKu2TLAEFTSf7M9flPrN/B+t0WuLOwbw0IWqDBE3irAgLMvDAsFYs25Nd5Zd+qEnPZR0TTysnQpvQvvrG4KiqB97a0mH2WMiqsLV+6VMQ2QQufUENKkY8qx5UuusnWDMDc8thxAVf4XMbg05Dn2anV78pPjyI92/szherU5m16hBFDjcDW8fwj+FtaBp9hg7qLc6p/ny4rd/AxY/V+EDjMJOeXk2jWLw9y2/7JW3iMOrP3E6vgLkcamX5n/6hnlun1UO7K2Dwf9WfgzNg02Erqw/kstpP5fWXF+3i5XGdCDtJWYDwIAPdGkdWWwKhf8szUxZDCCFBkziLWsSF8tGNPfjn3C1kHC8S2DYxjJfHdqLh6eZERDSG2DbqeV6VOPr9A31EpRyg/ct8A6YyeQfh8Gr1vxtn+zQVJvVn5/by4oYmvZb3J3Un4TRmHsokWsxMHdySCb0aoygKYUEGQoPO4D/PIv8zfoA64+TvPD97ARSkw86f1N2LrUZAdHMI9S0cGxVqYtqQVizbdazKUmaDCDO9TzPIrDXH9sJHw9XAGNQdbVu/Vb//t/ziW++rFiiKwg+b06tt/3PPMQodrpMGTZEhRp66oj1Xv7MCe6nv63tD78bEhUntOCHOFAmaxFkTbNRzcZs45k3pR36JE51WS2SwgegAdh6djCkyidLrvkT704Po9i9V1zBMYTj6PICScrXPpgDsNtjwafU32/mjuiRnPaIes2JLh6AIgjqM5qmWkWw8nE9MqJH2SRbiLSaMutqZNdHrtLUSgAXkROUKNJqqVbvtNtj4OSx8pPzaX6+pSeTXzITwRJ/uTaOD+eqO3jzz0w7WHszDoNNwaUoi9w9pGXj19zPJUQC/PV0eMFVkPQIH/4JOJzhIuQY0Gg2xJwhoLGYD2gBnW1vHh/HT1Iv44M8DrNh3jKgQE3cNak7XRhFnpOSAEEIlQZM46+ItQaeVF1IdQ3QT7GM+wF1yTM1hMoWjsySiN1R6o9Jo1aWY6mj10HKYukSnN6nHrYQnoY9qTIpGQ0qDWkrGrkshcZDYyf95Zy2HVz3R3nbEN2Aqk7pSnZHrf79PYdFgk4HOjSJ5c0IXCh1utBo1adhSX97QHbYqOy197FygJsSfrJjqKRrbLZmZfx3023ZT3ybEBvgHhF6npVlsKI+PboetpBSjTlt/XlshzmMSNIl671ihA6fLg06jIS7cdMLcp6CwSAiLrLYdUGdRut8KB/7w397xOvjhfkhfr36u1cPoN9VzzEpLwFmolj4IiQV9EB6PwtECO/ZSN0adjtgwY/3I2TmR0Fg14f2rSephwGWaDICRr0BQpcBw05fV32vNDOh8fZXZJoD4cDP1sp6+Rq/ulnQU+G8Pa1DrARNAw0gzj45ow3MLfZeRezaN5MquDdCeYhFRk15HbFg9/1kT4jwiQZM4YzwehdxiJ4qi+GxNLnV7yC8uVWcfQozVBkG2klI2pObx7E872XW0gLgwE3df3IKRHRMDKibo5SyEgkzY+yuU5ELzwdCgKzS5qOpsQ0JHiGxUHjCBmusy706IbQ0LpkHmJjCYofMNuPpNY95ehecX7iK70EGwUcf1vRox+aJm9b8WkbsUuk6C8EfVmZegCDWXy+On6nVB1WOIvEry1Tyoc0loHPS5B356yH9714ln5GHDzQau79WIS9rG8fOWDArsLoa1j6dpTAixsiNNiHpP6jTVIqnTVC7DWsKPmzP4Ys1hPIrCmM5JXNMtGQ0we3UqP2xJx6jTMrFPY4a1iyeh0mGnHo/Cgk3p3Pflxir3vrZ7Mv8c2RZLIAX8HIWwbS4suNd3r3bzITD8WUhdAdu/V6uBlx2AO/fOqnWaQC0sWZQFu34uH2dCR1b2eZ+DucW0jjaSVeTi3XVqscsXru4Y0BbymjpW4MBqL0Wv1RARbAzs9ShTcBRmjoDc/ernOiO4j5cJaNQHrvvcN+9p548wZ4L/e7UYAtd8VHV2qr4rOAoL7oPdP/tev/QF6HTdufd8hBA1JnWaRJ3JsJZw88y17MwsX/p4dckevvr7CNOv6sBbv5cfpfLf77fx3fo0Zkzs5lNV+6jNzlM/+D/n6su/D3P7wGaBBQm2NJh/j/r/5kg1R6kwC/Ytga1d1OCpHYAGErvArCv8B0wABRlVcn20yT3pHXSIfruehqNbILwhfbrfxypDL7ILHWckaLKXutl0OJ9/zdvK3qxCAPo2j+b/xqTQPDa02q8rsJdS7HBj1GuJLDxaHjBBecAEap5S8THfoCmpC0S3gBzfY3DQGWDIE+dmgBEWD1e8CdbD6iykKQyaX6Jer2G5BSHE+U2CJlHrVuzL8QmYyqTll/DX3mP0axHNX3tzvNc3Hs5n8xErQ9uVB01We2m11cMB9hwtrBIguD0KmdYS9mYVkmmz071JFE23fou2cV/oPUUNhpxFENVUzWda9zFENIIfpqk36HWnGhyUVciurEFX37bkXhDbGt0X48qvWQ8TufQhBna6mfSkfwC1/+a7L7uQCR+sxl3hVPkV+3IY++5K5t/Tj4aVDhAudrjYk1XIK4t3sy3dSpPoED4bUnrig2Gdlap5hyfBxHl4lr+KduNstWBo434w/Fk0MTWvhl7nQmLUj6QudT0SIcQ5QIImUasKHaV8/ffhatuX7Mjiyi4NfIImgC/XpjKwVaz3gFjjSY5mCKtUw8jtUdh8JJ8bP1qDza6eID+4bRwzmoei7ToJ5t7hexhvh7FwyX98i15umgNXvgv7fq2ao2OOhKSu8Ptz5de63wyL/u13fMGbP6Zhn7tO+BxqosBeysu/7PIJmMrkFjlZuiOL3s2i+HPPMeLCgujZJJJtGTae+mE747onM7Z7Q3IKneRqC6j2kBSdEcwRPpc8HoUdReHMVyYx5PKbMepgw1EXIenRDI3QECG/ScQpKLS7OFbowGYvJdSkJyrEKKUSxDlBftWJWqVFg/4EZ4oZdBrcftLoKgdJUSHGaqseh5r0NIn2nU3JtJYw6cM1FDjUgEmn1bA7swCGDoIPLlaTuSva8jXEp/jW6bHnw4ZZat2hX5+CnH3q9eTe6hLUD/dVejLBx8+t80NRMNhSISIJXMVgCKla+6gGihwu1h/Or7b9zz3ZbDqSz3fr0wB4aWwn9mcXMnVwSz788wA7Mm00iDDTYUxT4tuOQbdjXtWb9JgMIb573tLyS7j2vVUUOlzM+KtiSzof3dSdS9qc4h45RVGDWJ1BXTKtTaXFUJSj5qmZwk9ck0qcdUdtdqb/vIP5G9Mpi/0vahnD81d3lIN/Rb0nQZOoVcEmPZP6NGb5Xv/BxGUdElm6o+pOrOt7N/bOMgFEBBt58ZqOXDtjFdmFDu91o07LZ7f0IIls2Lwajm6DpC6ERXekfVI4l3VMJNFixlHqJtEShGbfJ1UDpjJr34cBlWoP7fwR3C6YOE8tL6DVq7NMbickdIbed6uzMBothCWqM1UVZ7DK9JyMVm+EryepuUNx7WDgwxDd8sTBU0m+Ot6gCLWsQSV6rZbYUBP5xX6KMgKxYSayCspfryRLEAX2Uh78qrwe05G8Eq79dAe/3PZfmoTFo13/CbjsYAyFPndDj9vA6Pvm9fuuLAod/l/HFxbuolPDiMCLlOYdUs/82/2LWi+q953q6xJ8klIRgchPhd+eVY+CcZdCg25w6YsQ3x4M1SxIetxQkqeWGDDXwhgC5PJ4UDxgONUzF89hRQ4XLyzcxbwNvpXR/9xzjHu/WM/7E3sQFSozTqL+kqBJ1LrOyRFc1CKGPysFTilJ4bRPCueVxbt9rg9vH0/r+Kq5P81iQ/n+nn6sT81j1b4cWsSHMbRtHEklu9G8e7m6Tf648OAoPr5+ATf9kMGq4+d6DWkbxwzLvuoHakvzH8AkdVbr9FSqIq60uRTNon+rFaO1OpQ2o9CM/xK+vlE9y61Mq+FgaYjm09Hl1/IOqru0xs2C1pdVrQFUcFRNwF71lrrjr80o6HIDRDb26RYTplZ+fqBCEFTRNV2S8NjSeaxbLB6diUiLh8fmHiQm1Mgt3aJoG6MjvcDDB+ttDPtgF3/c/xhJfe5WA0RjiFqLSlc1wX59an61L+O+7EKcrgBLDhzbCx8N8329tn0LF/8Let1xegnl1jT45HL1tS6Ttk59vMm/QWLHql+Tn6rWoNo+Vy0j0esu9dDmsISaj+Mkcosc7M8uYtaqQxTYXVzVpQHdmkSSaDn/Z1mOFTqYtzHNb9u6Q/lkF9olaBL1mgRNotbFhQfxyrhO5Frz0dnzQQOl+jCiIqNBo/DVHX2wl6q1gCKDDSRYzMRUc7xEUoSZpAgzozoez8CxZahb3x2VzkYrziVo3i2M7/weqw6olw4cK0Lp2Bc2zvI/0ISOkFspqAqNV4MVnQ6X24NWA1qtFs/Bv9B+fVN5P48bzfbv1ZmuS5+Hb28rb+syCb67jSoUBRZMVR/XXQquEjVIMATDjw/Azh/K+2Zth78/hNuWQFQzn9sMaBXLtd2T+bJC7phOq+G7m9rSInM+IX/+HxSrgaPS7GK+ue4FlIKjRK9+HN2mLWBpyLAB97O0uDk7s50kNY9Rl7SMIX4DJoD2SeHM3eD/za5RVAiGk+SgAepRLIv+7RswlfntGWg35vSCpiNrfQOmMh4XLH2yalmEvENqQFWQWeEet6o76Ma8S74ukhKnG+3x409OtfCkP7lFTl5ZvIfZqw/RJiEMk17Hw99uJtESxKxbe533y1MFdpfffLwyWQUOWp+5eFWI0yZBkzgjYl3pxK5+BrbPU5OqWw5HGfokB5Qk/rd0D8v3HcOg1XJ5pySmDm5BXrGGo1Y7OzJsRIYYaREXSkJ4EPrKb8ZF2eoMkT/Zu+iboPDGdZ3RabUoKGiiwtUyAf4OqL3kP5C5VS106HFDyjXQ527SiGXzlgwWbE4nyKDj8UExWBb/y/9j5uxFMUeiGfsx7FmiHhpsjlRnbvwpzoXsXfD5WPVzYwj0ux8a91VnOlpfqi4JZm2HdZ/AHy/CZa/4LJfFhJp47LI23HpRU9YfyiPYqKdr4wgi9y8g5Jf7fR5O47ASm726vOwCgD2f2J9u5cre09BGjIXv7lfztxI6QL9papBm9M0ZG9ounhd/2YXDz4zSA0NbVhv0+ijJgz2/VN++71eIPY2deBWDzsoOLldn8MqCplIHrHzLN2CqMA5n5nYeWRXG0h1ZRIYYua1/U67s0uC0C5am5hZRaHfx0Y092HwknyKnm9sHNONIXglzVqdy75CWgQWg56hQkx6NxrdkWkWnVLRWiDogQZOoffmp8OEw30Bl989oDi1HP3YhK/fnqHnAbg/frj/Cyn3HeHlcJ8a/v9rbPdio48Mbe9CtSYTvgbillbbCV6LzOHnk2y2UlLoZ3zOZy8Lmqzvilj0Ph9eonULjYeAjav7SoMfUQoYoEBzNYZubuz77m61p5TNZ/+kbXJ4U7kfpodUYB/8L2l+pXjiy9sSvj7s85whnEax+B274Vg3g5t0FLgc07AEjX4L1s6A4G4yNfG4REazuNmpVtqxZkAl/Pl31sXrcBkue9DsM0+o3oHk/9XUAyN4JW7+F8V+qBSsrJPQ3iDAz+7Ze3PnZOo4VqqUgDDoN917Sgj7No0/8fL2UE1cOdzsodbkpcroJMugIMpzi8SDh1e4HVAPZikuiJTmw9etqu+s3zsKgnYLLo5Bd4GD6zztZsS+HV8Z1Oq0Dpndm2GgZH8otn6z1CRz6t4hhQq9G5BQ6z96hzXUgOtTIpe0T+Glr1WC1XWL4CQ80FqI+kKBJ1C6PB7Z9739mx1FAzPaPuazdWOZvLc93Srfa2XTEStvEMHZkqPWdip1ubpq5hsUPDKRRVPmshyckDq1W5/+oD4OZjNIQSkrVe288nE9elySi594B3W9RZ1E8LvVYlb8/Us8d02jUhG7A5fIwd8Mhn4AJwKPVq0nSzkK/T9kV2gC300VesZNgo56IsER111blJURQ39jt+b7Xypb3KhaOPLIWvrpRrcwdSNH+0hI1WK3MGAqF1RyBonjAlq4GFCV55dfm3w2Tl4GlPAjR67R0bRTJgnv6k13owOHyEB8eREyokWCjXv2+F+dQFnz6PbfNZFGrjaeu9P8UmlzM9J938vehPBpHh3D7Rc1oEhNMWFCAlc47jYe/Xvff1nuKmnRekab6GR2NVsvE3k24rGMD9FoNq/bn8NXfR0jLL6lx0KQoCo2jQ3n0u1VV2pbvPUa3xpH0bHL2EtHrQliQgf9c3o6SUje/7Sr/HZHSIJx3ru8mM02i3pOgSdQuZyHs+rHa5uCDS+nfcTzzt/peX7U/h5QGFm/QBOBweVi1P8cnaMrTWDB3vZ3gv9+pcu+Cnvfz3vrywGZHRgHHhvcl2uWEZS9U6e8aOwt9aPkbabq1hK/+PlKl33ZbEF0730zImv9VfUI6I57G/Xn1l138tS+HJIuZh4Y2o82Yd9F+dYPvzIrOAMOehj9fKb8W0UhdNqpcaRvUAG/lWzCm6nOtOg6D/0DtBIEBAPqgqrsLC7Og5JhP0ASg1WpIjDCTWDnvxpqGsm0umg2zQPGgdJ6ApsNYsDT07RccqR5dM/NSdbdeBUrKNeyyGfnoL3VGb/MRKws2pfPCNR25olMSpkBmncIbwsiX4ccHfa83uxg6jsONhtwCO6AhSGdB33YsZj8/RwDFHSbyz3lb2ZddhEYDg1rF8daELmxIzadjw4iqX2A9oi675h2A2LbqEmelA4w1Gg2Lt/tZDjzum3VHuLZH8smfZwXHCh0UO93otRpiQssPij5qteNwezDoNMSFBaGrhXys2pJoMfPqtZ05Vugkr9iJJchAdKjxtGbwLlTFDhfZhQ4KHS5CjHqiQ42B/5EhakSCJlG7dIYTb9s2RzCwWRh/3OAArY59xWam/5lHZLCB8T0bMahVHEa9hiN5Jbz/x35Sc3yX4w4WaEiPnsBFFzciYu1r6iyKpSH2/o/yQ1EK32/13cr8wKIcPrjqWxJ/uaP82BBjCNY+j7GyuAX97KXeXzIKeBPUK3p56X5mjLkNw9HNGA8tK2/QB5Ez+lP2FoTw4V+7ANiZWcCvu7KYeX0KA+78C92a9yF7h1pxutN4WPI4HK0QMcanqDMvlmToM0UNojxu9WP9p3DwDzyKh2M2OwoQFWLAoPMTQITGQ8/b4c+XfK/nHVTLHWT5OZLGGKJ+OKpWb4cA32StafDZlWiyd5V/5ZIn1LHfuMA3cCrJh42fq7Nnm76Aw6vVWalO49FotIRrijDqtDjd5YHmf+ZtpU+zaJKjfHOs/AoKg47XQrNBauX2Equ6zBjRiHRXKN//sZ+vjifPX9W1AXf2vh32/KAeo1KB0voydjhj2Zet7ihQFPhtVxaHcop49drOVR/36Hb49HLfml1RzeCG79Tq82X3VRSybI6qX39cbpGTas6urqLAXsqG1Hye+mE7e7MKMRt0jO+ZzC39m7J6fy4vLdpFhtVORLCBOwY045puyfVq6atseVnUXJbNzsuLdvHt+jRcHgWNBoa3S+CJ0e2qnOUpao8ETaJ2Gcx4ek9Bu+snv82e7rcTv/B2SN8AQKPwBrQf/g7ZYclM/ORvco8fndIyLpRnr+pAsNE3QCh1Kdz7/WG6N+7ElIFfEx0EHq0Rmz6ax76tmku0Lb2QCQsU5oyfC8U56JRSjnnCeH21jUW7DvJny6beoCkqxMig1nF8s853tmnjYSvf7E5g8IDXCHNmY8raiNscQ3FUO4qMMTz4xZYqj3v7nG38Mm0AzS57QV06MwSrZ9dlbPTt6CxS85dSrobF/1FnK0BNWL7oQWjYnYO5dq7/8i88isIVnRswqU/jKkel4HaiNBuI5ugWtf6Rd/BfwOWvw2dX+85CabQw4jlYPaPqNyksUQ1mAuDesxhdhYDJK3c/7m3z0PW5G28kYM9Xa2NtmAXtrlCDPIcN1rwHOXuxXPQ471x9GVFGF8eKXLz9dyEbDls5lFtcNWhyu6AgHbJ2QmGmWk4grAGExqrnxkW38HZNzy/hhg9Ws/9YeT2t+RvTmRhpJeKyF9Wfxb1L1ACy/VVogDijC51W47PTa/+xIoqcLnVm0FmoztKVFqtJ/ZWLnObuVw9+Hv+Ft7imRqNhSLt4ftiS4fe17NUsilBTYL+S/z6Yx80fl/+8l5S6OZRbzHfr03xKeuQXl/L8wl0cOFbMv0e2JfxUDnUW9VaRw8WLv+zi6wq/qxQFFm7LpMjp4o3rupzRw8IvZBI0iVpXENYSXZfbCN3wgc91pd2VaN0l3oAJAFsacXPHYpn8F/kl5WfN7ckq5IGvNvHdXX197hEXbkKv1fD3oXxuOZQPqAUcP7mlIWaDjhI/M0X3XxSH2ZWPbvd3GB15uBpfyoQOTdicUYyrwqxGWJCBOwc2Z9G2TDo0CGNIEyMON3y3o4h5G9K4snMPPCSQF9UOvU6D2wM3v+dbfLNMqVsh02qnWVAReErV5ShLQ5g0H2ZfU75rK3UFyqXPo/lohG+uk90Ki/+LMvYTvt1VSoZVXc5674/9zN+Yzrd39aFBhcBJKcpBM3sc9LtXzd/KO6gWyNSbYPV7uCfOp3jPMsIyV1FsaUlhm7GEOI8Rcmi578C1ehjzrjfPqyJHqZvsAgfbM2wUOVwMaGQketPsKv3K6DZ9gafjdWhDY45f0agBlMsOm79UPyoIM2oYvOdZ2PE9hCfRvdt9rOreDx2ogUpZLSmdUa2/NPtq31myxn3h6g+rJIQv3XHUJ2ACuK1rGBErH4GsHeoZgo37qQVMl78K+YeI6jWN3s0u9TnuZ2zHKDoZ0mHHNrVQpssJcW3UpTl/Dq9S87wqVCRvFR9Gw0gzR/J8d1fqtRomX9QMzwm245fJKvB/mPVVXRry8Df+63d9ve4wdw1qLkHTeeJYoYPvqikB8ueeY+QUnZnDwoUETaKWlbrdvPLXMRJN4xl17XVYDv2MxuPClDIafc5OtU5RZS4HyuavuK3flbz35wHv5dwiJzszbTSJKT8fLsESxB0Dm/HWb/toGGkmLiyIvCIn9lI3L1zTkYe+3uSzLf6/QxtwqX0hhpnlO8gSNs8mLq4D31z/CbpKuTLNYoJZe08b3Ju/JnjXd6A3MbH/bWibD8JcaXZna5rVb8AE8PLIBnTPXwg/vAD5h9SZj8GPU5zUhyOj5xPqOIqmJBdTYjsiU5dXTQ4/TvPHi9xw+We8VWFVMNNmZ+6GNG7s0wSbw4UGiHG5MLpK1NwtrU4ts+As9s4uuaJacaj1HeQlXYvBYCQ9r4T9GaXcd8cKDNnb1WBJ8ag1pCwNqbxOVFLq5o/d2Uz9YoP39Z3aP54HtCf4FaLV43B58C4UmCOg+eBqD0TWxreF347vALSlE/nbIwy+/G30hiL49lW1plZ8B+h3H6x5v+qy4qEV8MfLMPwZb/Xv/GKn90iZihJDdeXBzuHV6kcFoda9xIaUBxgdGoTzeC8NIdZdsPY9dUkuIlktFzHs6WrPIKS0BLdHweXxYNBq+GJNKs9e2YEv1qSyePtRXB6Fjg0t3H1xCz5dcYDHLmuH5STLVkUONwcqBYHeNqefDRKosxCHc4tpWuHfkjh32U5S7+pYgZMWcdU2i9MgQZOoVfZSD1vTbXxyKI9X9Vo6NhyCTqvhnW5xRC651v+uNyDo6HratrquyvUtaTZGpJTPegQb9dzWvymXpiSw8bCVA8eKaJsYhkeB33dn8eOdnYlUbCilxWhMFkJ0bgzvVt1yr83aQtyOWdgv8j1GRWs7gunTS31qQYWkTYEmA+Dq930qRYcYdTSJDuZgpbyrSd1iGV7wLcZ130Pn8RCWpO5s+/lhPN3v4ZWD3fhtTxFmYzDPjgnl0lTfN2wfWduJC/KweGICKAq7bHpeXpHP/E3pxIUH8ci3mzHqtCy7uyMJSV0hfb36GleqP+RsMpBrZ6z0vqkmWYJYMrkl+sy/YcX/1Jmp2NYoA/4BphA0Bt+Zpoz8Eu76bB0Vf0//uKuQif0mEXtohd+hW1MmoQ2KVHfWaTTqkuPw6XDk76pBYvdbYf/vvknprUZgtB+DmVPUvCxLI8jcBO9fDFe8Cbl7fWctATZ+Bv3vU3PDUIt+6nVVE4W2H3PRP64D2sP+x54X34cD28png54eGk9ozjL46aHyTsf2wPdT1LIOXW+E9Z/43kQfRIkulP+bt5UMq51JfRqBopBb7OQfw1tz16DmuD0KISYd9lIPl3ZIpNTt4ZvjeVedG0UQFWwkqlKCtEGnqZL7BWDUnzghqvIh1+LcdbJl3IgQmVE8U+RfkahVJr2OVnGhrDuUh8PlYe1BdSu71dmIyMim1R5w64hpz/5cZ5XrreNDOZRTyFGbg1CTnviwILIKHVz/wRqsJeXnrzWLCWHBpCYYlz6EYfdP6qxJUARK//th8H9h6VNV7q1bP5OQ3reD+fhciLsU1nzgv3jmwT/UBO5Kx2s8PSaFmz9eS6m7PJq4oYOZ0HSTWgNqzXvqjqqYVjBiOqGHVzOl20AW7sjF4fKQaXNATMvqX9DIJuicBbRc/zSgoWVJDj0ufYp3d4ejxcPTQ+NxujWU6MMpGDydsNmXVQlMnU0uQQlvRJGz/LX/1yWJBG2ZjWbZc+UdD69GM/salFGvQ6dr1WKbx/2wOZ3Kf9juyy5ip7EDkcl90VcKPtyJXTC3vgTjoaVq0rcpDLrdBJFNcU3+Hc2mL9DtW4ISHAu97kBzZA38Pt33AbpOghVvwvg56jJa/iFoOURNml/5FvScDPOm+H6Ny06p086uNCsuj0JsqJGJvRt7fw7LfLgujyvHPEr84dFUERRBcZMhbPqlvDZXa4sb5j9btS+o5SsmfFklaHL2upd/Lslm7mb1dT+SV8y7N3RlfWo+I1770yfo+fL23qTmFvPAV5u8r7NWA1MHt2R8z0bEVyiqGR1qZEyXpCo7PXdlFtKpoYVNR6xVhhgbajqv6z9daKJCjAxsFcuy3VVLu7RNDCMmpP4k/Z9vJGgStcqo13JTvybqER8aGNAyFq1Gw/Tf0nnjoscwfXFV1S/S6vF0msCMtw74XA4P0tMiLpQdGYUcySsmNsyE2wN3frbOJ2ACeOSiKMzf3oD2aIWkbHs+miWPq0HTRf+AmBZqjk/6RjUZ2Z7vWwOp6Bhs+ar6J7fuE2g6yHuQrsuj8NeeY8yb0o9PVh5ke7qN5KhgmoZ5QGeCeXeWf23ZEtDIV4gPLn/Mlxbt4qZ7RqL54wW1qGVFGi2M/h+U5EJkUzUQ7DqR+JJ9/Lv/RdjXzyJkz/dgMOOKmsz+8J4UXreI2FXT0R/+C4Kjye90Ozuih5J+yMWwtnEs2pEFwNDGOrTvv+z3aWqWPoGnyUUc8MTjcHsIMeqID/P/hnv73CO8d+UL9Oixj6CNH4PiIbv1eEKa9sQ8/y7fmkwbZuHpcTuLY29izoGL6dlgKDl2DZM8RppUDph0RnXJsM8U+OYW36KmwVFw5Qz87vALT2JDppNxn6u5WhazgU9v6UnPplGsOX4mIUB2gYP5GXHcOm422p8fLJ+ZS+iIe8w7HCuN48MbI9mXXURksAGjO8t7NE0VikfNuQqJg6IsMEfi7v8AP2sGMndpeWDTqWE4dpeHR77d7BOANo0OxuHy8MriPT639Sjw2pI9dG8c6RM0mQ16pg1pxeYjVnZmli9Rzlp1kJk39eDmj9dytMIuvVCTng9v7F7t91CceyxmA9Ov6sB9czb4/EHQOj6MGRO7B1ahX9SIBE3ilLjcHkrdCkEGLZpq9kc3igpm2b3diNFaUQ6uBE8pmib98QQl4RzzAUaPQ90ertGC9QiOuA6syg32WaNPsgTx9vVdeef3fSzYXL7b6MMbu5OW75tEG2LUkRKS7xswVbTyLbhmpnpmXWkRNLkIxryDcugvNKYKBwVrqJLL48u3TV360bJi237+1SsIU6kOl8GAzmiGP6rWhQJg6ZNE3lye01PsdPP3MQM9xs1CM+/O8jdmrQ6u+0Ldor+xQrL1upnQ5nJ0+iBCVr7ovaxfcA/NkvuyossLbG73LJ0HKhS5NLyyqpAfFh1gyqDmdG4UyaIdWYSadOgK09SZNX9K8lCKcxj+7i51tibMxP1DWvLexG7cPmudb9dSN6+usvHcVYPZ3LINoKFdchzROz9E46eIpXbte3S8ZjQPpxazbI+6FNciMokmiZ0r7SxU1DIKs6+pWgW+OBcWPw5XvF3l/nl9/8mzf+Z7P7eWlDLls3V8dlsvNh2xMm9DGgpwReckujWORBvVARp2VYt7ag0owZEs3Ofi7s9XYtRpiQs3UehwceVtiZyo4pUSEodj4o9o3HYUvZndJRbue9d3N2ffFrF8v6HqjN09l7Tg478OVnvvD/48QPski09ib1KEmU9u6cmB7CJWH8ghPjyIPs2jSQgPYu6UfuzIsLH5iJXmcaF0SY4gKcJcK2fnifojKcLMuzd0I6fQyVGbnegwE7GhpnpVWuJ8JEGTCIjNXsrh3GI+WXGQ9Hw7/VvEcFnHRJIjzVWDJ7uVuN2zMS37P5+ZnNLLXkcfkageTluWhJvQAeMVb9O7YSKbHtSjKS1Go9VTbIjkjT9SfQImnVZD8fGcnDCTnms6RtPEoiHPZcCU47/KNKDuYCrJK6/ofeAPSF2JcssiNEHh5f2CY1A6TUBTudZRme43e2eZAMxGHZM6mIj642l0H85XL2r1MHGuuhPLH7sVvdN3+WTcp7tYdEcKLW76CY31MBpnMUpMSzTWw74BU5mdC6DZAIhu7nO8i/7wCvr2O0LpgVWYV8yH4Cj+2/EuhjdtiSHcwrwNag0rh8uDojvxrINHo8d1/N09u8DBP+duZfpVHRjXrSFfVSrJ8NhlbWmVEEaTGPXcOGNJFqz7qNp7x+36nIta3spPW9VK5RtytIy89C0sc0aXH+areNRyDP4qywNkbUfBg6asOnxoHPaB/+XLnLZsPOxbAf2/o9tzyyd/4/J46N8iBg0aXl+6B4+iMOf23miIJM8dgl7RYrbr+HC5mifldHu8u9yKjdGEV1fvyhQOhiCCPuivzhaawmjd817eGXMpd80rrwFlLXZWCfhBnTU4WmCvcr3M0QI7JaUuIvFNEI8PDyI+PIjelY6xKTvkenDb+GrvKc4P0aEmokNNtEoIO3lnUSskaBInVex08cOmDP45t3wmZ/neY7y9bC/f3Nm3/Pyz43S5ezH+XimHyByJITwWvrjWd0kscwuajy/DfMsi+PYWNXdFH0RQpwmMb3M78zbosdnVGQk1aVbPTd2iuT1FIXbj6xh278WZ1Atd42HVPwG9SZ1BSuqi1tbJ2gH2fJSl/4ft8vcJjzy+JV5nQOl8PZqt36iJ0RUozQfjimxOxfTK+CAP/PYi2p3zyy9qtGrgdAKaSlW6uzaKINUeRGqJid922Ik06xgdGkHL1f+pvsTk5q+g7Wh1i3zFp7rpc7XKeURjcNiI+/k2hra/DnvrJ7nzM3UJqtStUGSMxlLx+JSKIpuS5a66y+q1Jbv58MYeOFxuQkwGdmZauefilrRLVANPo/7481I8Jzwj0OAqIC7UyICWMRQ6XFzcOo7r5u7hhVHzSSzaQVjBAYrNCYS7HJyoDniB3cPhq39H73ESGh7B17tcvPa77xmBnRpa2JVZ4N1t9sWa8iDm+l6NWL7nGNN/3kl+sTrr1jw2hMcua8vrS/bQo0kknRtFUGR3cbAkmKRhbxLzzZVqOYgyOgPKmHfR/PJY+fKqowDTn88yoGchYztdzteb1MBvyY4sLu2QwI+V6jTtySqkc3IE29L9HLsDdE6OJEJKBfiVW+Qgr6gUl8eDxWwgPjyo2hnw6qTnl7D2YC5LdxylUVQwV3RuQFKEmZAAa2aJC4v8VIiqnCVqboazEIyhFBPB45XPPQFsJS7+OXcLH0zq7q3uW+ooRre66rIJna6DtR/4P0fNUQBbvlbrCgG47GjXfUTrI2t5beTb3PJt+Rvd4ex8Hmp6gNAvy/OFjFnbodXFvmeoVXTpi+puqnZj1DymgQ9D0TF0C6Zh9JS/udsdLvQHlqMd/qya9L1nkRpktb8KjVaPYjsKUeUH52qLsmDzHN/H0mjVrfXVjSWiMRhDef7qjuQUOmgWG0p6fglPzt/GfUNaMnutOhvUr3EIrfydXed9zWw+idqA+pjdblQrb3vcau7PoEcJ2jAbT3EmEcEG8otL0Wk1fLNPwy1XvY9m7QeQcpX6PJ2FsOVbXAMe5r6fc2gRF0p4kIGDOUXkFjnp2DACk15Lz6bRFDpcjO3ekLgwU9U3F3MktB6p7mTzw9nuGh4wHMO89yfcIUl4oqNZ0zCYvBKF+KAonEWZeEwRlIY2RBfXHrK2Vb2JKYwDJUFc8Zl63t4NvUzeYzhMei29m0UTbNTRLjGcX3dmVfnyhPAgejeL5t4vfHff7csu4r4vNvD9Pf2YvymdWSsPERVipEvjSF5erePfN/+Obv8STEdWUBrdDn37y9Eu/lf5YdAVhKyfwU1XjePr46WT/tqXw2OXtSU6xEhOUflM5OtL9vDdlH58s+6IT7mMsudyc98mBJskaKpIURT2ZBXy4Feb2JKmBrEJ4UE8PSaFPs2iCQlwp+ChnCKunbGKTFv5TN9bv+/jlbGdGJ6SoJ6reLqKstUZVI8LgqLUzSTaEy32ivpMgibhqyBTrfWzYZa6xKQPIqLbLcy85gZu/PpQldogfx/MI7+41Bs0eUodGIr8nK8V1VwNjKqTuVmtZVQhD0Z7dAtdgo8RFWL0VgrvEukgdP5DVb9+2QtqcvC8u9St7UERkHcAZdBjaDwueG9Qed/lr6oHx06ci1JhLsdTlIV+xatqNeeGPaBRX/U1WPkm5O7H0GkCpQ06YzAcfwOzW6ue2+ayg0YPl72ojqVi3pDBDJc+j1NjpJOlmIg4D5uP5fP274e5c2Bzn91Qyw7Z6dV6JNojVaucA9ByGKStL/9cb4Kr3odf/gXZO8uvazQw4nmC8vfy472XklvkxGzUER1iwu0KRZfUBc1P/1CfS0gMykUPsa80hukX24nMWo2xKJ2ivr3IC27B5nwzI17/0+dnYEjbOJ4Y3d63QrnBDBc9oN6z47jj30wtFOeh7P4Fo16Lcc6ValeANa/zn0k/oPt6UvlRNwDmSJRxs9D8+IBaUT0sHvIOQfZOPBf/G21YAj2aFJBT6ESjwMiOiTQMcjC6VRCGkmw0ihO3Uc+Ylg249MNCCh3l36trujdkZjV5REVON/M3pbN8zzHWp+YD6mzg8A5JjJq1jdsHjKB596vJKXLSPXMTsXuX+v8euRw0DHLSKj6UnEInXRtHUuxwM+vWnry6eDcxwRqC9bAv34Pb4+bzyb3597wt3vMX2yaG8X9XpJAcWf+PxMgucLA/u5BftmUSHmTgsg6JJFiCzlgxzSN5JVzz7gpsJeXf00ybnds+/Zvv7upL18bVHOVUnHv8oG4tdmMkLyzc5RMwgfp33UPfbKZr40gaR5/GW6THoy7nzr0djh4P/EPjYeQr6lE/ptCa31vUGQmaRDm7VX3T3fqN+sYXnAgluehXv033jgXc2uNG3ltd9a92d4XZI31QKEqjfmhSK53kXnxMPVC1mpIDWBqq58hVEpbxF81jB5Nb5ESrgRhNfnluUkVHt8LWuXhuXohyeA0a6xE8yb3RhcbAu/2q9k9dibJvCeaL/uG9ZPTYy48aObJW/ahAU5KrBmBli3QGs7rLq3L+UuYmCImH6+bAviXqUl9Ma2h6EYoxDIPGTRsOQLGL+BAtQ+5oSaEpkkSLGZdbocjhonV8ODQYAqvfUV8XY6g6i+WwqTM5KVf71gxqfyVs+cY3YILjZys8iva2pTSIDC6vIm63wZLpsOHT8r5Fx9AsfJSWQ5xoo5tDXCK4YwjXe0g48g36hlcRGWygV9NIgg1a1qXaWLIji26No5jcvwl6fflimt0YganFJWjm3VX+/QpvAFe9B3++5jvG7reh++VR34AJoCQPzcq3UMZ9imbnD2p7k4sobTKQLbYw2iRE8uGNPXC6PYQF6XHYcmnZzIXmrxdg2zxwO9En96ThgIdZPqUtXV7b4p3obBQVzMdHDwLQOdnCwMZB2F2wYIeVdKudXZkFNIwM9gZN3RtH8fvubO4b3IqP/jrArswCkiODmXfViY+bsbkNjOncgHCzgZ0ZNm77dC1PD0vgnUEedFnb0ODB3a4hurAiXKFJzJjYjcLjy9GhJj2JEWYMuvo9K3HUZuee2etZe6h8ZvW1pXt4ZHhrJvRuhMVc+5Wpf92Z5RMwVfTCLzuZMbE7looBm8uhBi4/PwJH1oDOiLHDOO7vcQ+rDxg5Vuj7b9jtUfj7UB6No0PweBS/SfTVXfeypqqHU1ecMS48Cl9eD7cswpXUg6MFdjQaDQnhJrQy+3ROkKBJlCvKhkN/weVvqLM1BRnqG11RNkHLnmfsqNt5r1IdxpZxoT6/nHSuYmhxCfz9gW/ux+avoO9U+GFa1cfVaKD1ZfDlDVWaSoPjaRQVzNqDeei0GoKqO+0+uRe0GoZ2xkXqbA+gbdIfJbFztU9Xs/ZD6HCt+pwVD7rkXnDJf/1XLQdoMRh9hURwtAb1gNgNs3z7/fJPGP8VOKwQ2+b4klwIis4IoQlo/3xBDXBcdjQRjdD0m0Z4ck9GduxA/xbRuBWFYKUY7c+Pw7Wfg9uhFsdUPBDZRF1K++MlPIOfQPvb06A3o/S+B80Hl/gft+JRjx1p0LX8WlE2bJxVtW9EY7QJKepzKAvAtDo0ncbTItTB0nHBBG9+H0Opjez+ozkY3IH/rc1mYscQQpXjVarNEWgzN6H54X7fe9vS0Hx2NVw3G/YvKV+qbdC16kHDAAkdoPN4NO8N8n5PAQzmSBKu/Jbc4nh1hsvjAq0OgzsPzQ/3QdMBcM1H6vMuPIpm8eNYRr7Ezb0b8dFKdTkvy+agU7KFx/qGkZT1O1H7vgdDMLcMvZl1pU3YkGtga4Uco2CTnqgQI5+uPMg/B0STEJaAzeFhX4mbThGN1RpSlbgb9WfhgVJe+LX8bL5nRyRyWVQG+ux02PYdlBaha3YJ6I3YnR5Gv72T/OPlNMJMep67uiODWsfW2/wat0fhu/VpPgFTmed/2cXA1nG1HjS5PR5W7Kvmjy9ga5qNYqfLN2g6ths+HFo+M+x2ot34GS1SV/DO6FmM/dz3+9c+KZxWcaF89NcB/j6YS7vEcEZ2TCIu1EhWoYNNh/OxlpSSEB5EuyQLDY9viCl1eyh1ef6/vbOOs6rM//j73J7uZGaYGbq7EVQQCRFFQMBAsbt1XXd114611u4CBRUDUEpKEOnuzunO2+f3x3fu3BzF/Rm743m/XvNSzr333POcc+55Ps83CTcbYM98f8Hkg7r0YbYNfJm/fnsCnQ4u6N6CUZ1TyUrQKrb/t/Pf+UvU+FUprJKV8/J9RSRFmjm3UyqpMZbgB3F9FVzwqlgwfLKySGoPF7xGREBwr0Gn8Pj4LiT6VizWm1H3zEe56B1xgx37QbZbYiUQu/9NYj1RG2I3DBbU0f9C2T472GKj01OXMYRrc9OZNjAbVQWXUiKFEgNbaAy6FeZc4ze5YopE8WRjhaK+HCqOStCyokNZ/ihqTBbKOY/CkoC2GLEtUTP6oBh8HsT2anGTuV0S2+QZU3wrcDtQw+Ip0yWgd1TjMESTGB2O8sVV/hasiuOSTTjmOQhPJiZaMp5sNXUiBIt3y/XwBBnrjTD8YWh1Nt8WxLI/6TGsLphUpdC6qYw9CK4xVJUXOr7s7L/B1zeJYPbgdsGWGegiU4gp2CGxXkDSvm9JSmjNe1Nmoy/fC1UnZZ9p3TCtfCz0cTitcGSVuEc9lcQDXZweBt4C8+/wv6YA9eWkL7kR16QZsPBdOYddJqLozTDsIalwvvtrsQIqOhhyN8qRVdw7eCqHy+pBhU7pUUzrpCfqk3F+gifl8HKGtRlLz7OeYOBqb90wvQJpZiufnh+O4eRKOLoLYrNwtRqGbcrnmGeM9a/CntiWo4Of5qVPvC7XaIuBC9qYMKx817+p8qnNsO1jwifNYHBuLPN3SeB4tc3JzZ9sZv7Ng+nUIib0OfqDKamx8f6aI02+/tmmEzyU3ulX/U69TkerpEgg2DINkBZj8bfOWSvhu3+Evs/KDtPSuoeWCYkca6jsnxUfzh3ntGXym2sbK+h/u6OAVQeKuefc9lRbnWw8VsGp8no6pEURZjKAqlJSa+eT9ccprrZz1cAMBnuefSFQCndQXVXFvkJ5ju3J38uczSd5+/LemnD6L0cTTc2cvIp6rnx/PfsKvC6tpxft45kJXRndJc1fOFmiYd4t/oIJxOKw8kkSx75K1wwnhVVWumXEct3QXFomBHSfN1pQOp4Pn14GPa+A/jfIRFqyT+okTXhX2k4U7QGDGWd8a4rq3CRveNf/ZtTpKR31BsXEsefIKSJ1NqqdOmLaZGEY/hxxa5+C7lOlsWx9OdSWBmdsFe6CIXdLRepQtBwk/dnWvCSCp91olDbnyPF2nSyFLvUmCZTucjHOsCQOF1RRWiNxQd0iItGZoyE6DSZ/LMLGYJY4htpSiM+lzmHD6AQlPAKqTgS5/BpZ9S/IOQMQ0WSOjEPN6Iny9nD/97kcsOh+1Gnz+HJlHUv3yoq7TVw6rVM6i5syFK3OgqoCcNaL8IpKFQufr3AKT5DmwtX5ofex4W0Y/a9G0QRA6UH0G9+GilPgySI8/2VpMRKRCD0uE7Hsdsnndn0pq/6YDO8+VFXcj75uV50eDGHeEgSBFO9FX7of1r7a+H6l77Ww+UPofz2UHZH7IbmjtJZJ7Y5OdZEabUFRYMXuPIZGzAlpITIfmEd8/+vJSYwgKcpMtdWJWedmREoNuk8m+7mY9d//C93FH2GdtgRr0UEMVSewxbYijyQ2FZuos7kY1DqBKIuRrLhwwqqP+AsmD1V56Da/z039b2sUTZ5T89rKQzwzsSthxv++x7VbVZt0kwGUVNtQVfUXZ7T9HBf1zOD1lYeCal4B3DKstf9Czl4DR1cHv7GBhGOLuPOcf2LS66i1OdHpFB77Zk9QH7/7RrbnQFEN93+xo/F7l+8r4oM1R3n3ij7c9dm2xhIVTrebnmmtCQ/8Mg8xmeTV+O9/f2EN3x8o4VJNNP1X89/3K9T41bA73by28pCfYPJw75zt9G4ZR06STzCis77pSffEeuw2K2e1SyY23MiBwhouf2c9941qzyX9WqL39e0ntYd+N4hbyYOih07jxQoTmQQJuYDcgCerStnR/QW6DCwjIm8NjrBkKlP6s7vCwDD3CVoffApD4VaIzsAWexflqT1xnfV39Kuelsmx9TmoLQcFp+dXnpAA4sQ2Mon7otOLu/DrG7yT4MZ3YN+3cNHbIvYG3gxuN66KE9RYWrC92MQNM39sDCg+dHd7WPkUHA/RvywsDq5YQOaCaRKL0+NS1OSOoc8tiOXH6WMpstfD2teafv/qFxjT9tFG0fTSugrOHP0ISXPGB1mQ1Mz+Ykl7Z5jUx7LEova/HmXiB1Jt27MCj0oNKrXgh7VSRGEg22bBiEe8oqnqJHQcB+3HiCBd/byIz47ni2vu1CaxNulNItK2z0YdcBPKyqe8+9QZ5X78KXwtB5ZYqcqd0UfcvL4B+B3GQlp3XCrM2iCZmN9d3Qbdl02IacC47UO+GH8D+t1zsIenEqdPR/n2ruCYPKcV5YtrMU5fwpAvXYSZsiiprqHWXsnbl/Viwx3dUWuKUR31RKSlwHcvNj2ePfPI6nNL0OZDxTXU212/r2jy9C+sKZTFQHSaVDw3+cuAKIuBM9oksnh3aKvPmK7pv7pgAkiPk8KOt87agtUhFl5FgSsHZjOoVWLAu3Xye2xiMaDEpFNnc/LuhhMkRpq5clA2D4zuwNUfbmx8T5hRT7jJwD/n7Q4SarV2F3/7aidXDsrmkfl7AFhzqJSSwReRtfGVkP02S3rdxtvfB7vuvtxyinM7pZB0mtXbVVWlsMqG3eXCqNeRHGXxfxZr/OpooqkZU1Jj47ONJ0K+pqqwbF8RV/mKJnvTtXUAqqqqeHFphd+255fsZ3iHFNJjfTJ8LLHQbhR8c5dktQGkdRcXlCXYzdA5PYYtLpVbl9TiVs/E4XJzbmcL16UfwfjBJK8IqCnCvHsOyYlt0fn2TDu4GKXrxNAHvfZVuHimTNy7vpAJoEVvaa3y40vBk2B1PhxYjJo9BOXjCaA3oXaaSGnnXFwqfpljitMqgskUIYIwobWUatjxucTS1BR4H5iHlqG0Pqfpk6sz+JcQsFWi/ISAUSqP0ydVz/S+KVhdsOxABe7YXNSLP0ZZ9Yy4fMzRYunpOgnlvVFeS5y1AmXFk6idxqOccTd4zmV1AaR0afoYLTEQqrG6o14EkIddX8MFr0gQrMet6LLLeTmxAfXSLyBzgJwfUySqOQolPEH2se41uSY6vfTr8xSvDMQcLZaoxmOoE7ftgnuC3Y575kFaN0rCvW6i1Giz150aCpeDmI3/lnsGIC0H8reFfm99OUr1KVxuGl08941qT/+4CiLnXCOZoQC9rkQNeQIbUFX0vu7fBtqlRFFtdbJ0TxERZgOd06NJjrY0Hd/3/8Vlh+Pr4LNpXkuf3ii9FHtdKaUsGog0G7lrRDtW7CsOaiCcmxhBt8zfxq0YZtRzZrskvrtzaIOodNMuNYrECBNRgRl7kSmyiPvuwZD7yms5joc+3N14/Ev3FnHDma14ZkJX7vlcrl1KtJn8Smtjcd1ADhTV+GWQutwqz6yr4x/nvUfCwhukUCuAoqOu760srW/L4ZLgHpd6nYLuNEVmWa2NRbsKeeG7/RRW2YgLN3L90FZc1DNDa6PyG6KJpmaI0+VGUeSH61mFhaIsIGOEiJ/IBNLpqdVHARV+mx0uFYfLzfGyOpwuN5EWA8mOfHh3pH8QZP5WeH8UXL9aVn3WSrE+hcVT4zTz1eZTDO+YQmZcOG5VJddSjfHbO4InwK4T0X06Tf7fHCUupdpicbNkDfDvdQZwxl0wY7y8dsFr8p3hCbDudTiwJPRY9y9EadFbgtNdDgx7viT36EqMF37Jkxd1IcpioN7uwuUsQtduFPS9DipOiBUmPAHOex5ObpQMtRGPyuSsusXiZQwPXfixw/moejMl1fW4XJBgCsOQ2g3l1Kbg9wKkdCE9Eh6sfQyXPoz7xl1JVN0BlIX3iVAadLuImeh0lI8uCPmdyq4v4NqV4uKsOCqiL6md/LumEDJ6i1Aq3i/nt9d0cIWoXN3mHPDNlmw5AFa/ENxLD2Q/x39E+fEVKJZVuRKTIYK69DCc87C46txOuVY9rxALYCCDb5eOth4iUxvchgq0PVesSwaLuEO3fQKbP8ScexHX9U9Bp4DBZJFaUoFB/J5z0+YciYPx0FTclQdbLTRU7DboFKZ1MhL+4XliQfSwZy7KBa+LZS4Uncfj1vkHTSsKXNwnk5EvrKLeIRO2Ua/w7MRuDO+QIgHHvzYVJ+Q34xsj53JI0+vEdtDhPL+35ySG8/XNg3j82z2sPliCxaBnUu8Mrh3airSYX6dcQo3NQVmNHbvLTURD426TQU9GXLh/uYtQ6HTSgPrQMjiywn+/w57gg13OIMH32opDfHPrYAw6cLrB4XI3VshvisByLPN2l1NhTeCta9dgqT4uv8fENuwo0nPf+6Et+pN6ZTTWG/spbA4Xn6w/zjOL9jduK69z8MSCvZwor+O+ke2Jsmi1vX4LNNHUjCissrL9ZCWfbzqBxajnxjNb0TUjhu0hup4DDG2X5L8hPElS13d9GfRea6cpfLbXfxJMiTbzzIRuPP7tHpbsLsStwqSe6TyeuBBDqKwRpw1+eBGyh4KjBlwO1KpTmNpPZuPxcj7b7A2aXXpZsrfVigdFBw4rRCbDWQ+IZaLqlHS9d9SJQDqwGLbMkH/nDJVCj5UnpUaUp05Un6tDly3wYLBAdDpkD5bv7H0lnNpMatkGduR34/v9pVgMCude2hL63ygC8MgKsYiobqnB0nIQxGXB28Pl9cy+0PtqCbQ/sESEBorEYx35HrpPwW2t5K+LC6ixOXl+bBapfa+WApEhAuQZdDv6Iyvh0DL0QMz+b0Qo9L0WFvsEsU/8IHSRTQ95m2H9WyL4jq+VB/ukD+X8FeyUf3e8QDIAawrk3PhiihTrw6eXNfTtUyCzPyy4t8mvVPYvgNTOjaKJypPwxbUw7mVvBqXeJCI3sTWc84jEU1UcE1HX/yZxeTp8BFzVSTmWqZ9KAVNDGKguCabvOA5sNcSVbuL+Simx4D4+TVywBxZBTUAZjezBMm7fEhhul1hQrRUhBqRAQms+vyGDWpuT5CgTnFrtL5hArDYRCdDqbJnAfYlMhr7XYnV6LRlx4UYeuaAzM9cdbxRMIAuV22ZvZfHtQ2gTUI3/V6GhXENIVjwBWf0lXq0Bk0FPh7RoXpnakxqbEwVp79FYIf7/yanyOh79Zg+LdhXgViEpysxfR7dvCBU4zcy8qFS46C2p83VgMVhisLc6h399X877m0Jn4q07XMqQtsks21vEqQorLRPC0euUIHEEckz1IaxQtU6FCmMKqTnZjdvSqaVTenRQBfgembH0y/3pEhYeimtsvLTsYMjXPl53nKsH5zYpmmwOl4hPk0HrR/gfoImmZkJBpZVrP9zA9lPeH+L6I2U8Mb4L09/fEOSH754ZS05iQMBhWAyMfBIscd7J2mDG3Ws621pM451Z/lkyfz+vI3/9ckdj8CNAapgLw9EVTR/osTXQ+hxY9CgYzCidxhNbf5J3xuew4nA1PVKM1DlUws0hLGSqW6xL578sk31aN5kodQYoPQhR6RLAndVfrBSmcJkAAjmwGIbcC4dDHKcpQiqIlx0WF5fBIvtN7ogxKo0b4pxck1CA2xKL3u2QVvQRSbL6PrlRYrZa9ISTm6U8gMcFVHJAxE5Ygrg6vrxessJiMkQAntqMvX1LBqcrRBiN2BwuqNwr8VXf/cNbwygmE4Y/JFWyA4vj7ZknAiG5o8Sg2Grku34KSwyUH2lcBWOJERGhN4josFZAbKYIiIhkcSHGthTRmXsmDL4TNrwjAgfEMpjYVqqihxIYINa48IC4E2uFfEdMpsSioYpgXXi/uHYH3izfX3lC3KolB+Bin4rjRbuluKe1SspbHPxOzn1SOxj6F9S4HIzH1oqwRkF3aBnq7i9Rps2H7bNh73yxAva5Wr43sFxCTSEMvQ8W3R88nu6X4DCEk6MvB50dsGCzhWpPky0uvo4XQNuRUg/NXiciKucM1LWvYxzyEF/fJHXFYsKMzFx7lPk+/Rc9qCp8sfkk943qEPoc/6e43VJnrCnKj4S2IALRYcaQxSyr6h3U2pzodQpJUeZfFONUWGXl8nc3cKjYu8gprrZxx+xtvDK1B2O6pp/2vohMlr/MPgAcK6zm/U2hhQeIJzo3KYIfD+nJig/H6XJz45mtQoqVv4xsz0dr/ZMKTHodfxvTkdQAa1tmfASvXdKTNYdL+XLzKXSKwoReGfTNiScz/mesZg2U1zma9CK4VSn0mR3wfK+os3OwqIZ3Vh+hpMbG8A4pjO6SdtrfqSFooqkZ4HarzN+e5yeYAPIrrcxYe4xZ1w7g6UV72Xi0nCizgcsGtOTyAS1DBxtGpcLIx2DwbeKHN0VCRDLhxXaiLScob+jRlRkfRo3V6SeYAEqs4AhPpcmpOjIZ8rZ4YyV+fBkOLSXn4o/JyciX4pEmE66wNMgcACcC3G3hCTLBH18n7gKQlX6HcdByoASffz5dtmcPlokdJA6j7UixQJzaKAHIrYfL5OrLpI/EUnPSpy3Glo+g91Womf1I+PJa7/a4HJm4Z07wDzI1WGDqbECV82mrFmtPXDYsvE/ElYfKk1I5/PyXMVrLmXbwbrDV4Mh4UVyI9eUw4CaJy1BVqC+T+Cy3CwbcHHxuwhNg8B1ivUpJEFE25B74/pngaxEe39AnruEalh6STLdTm2DuLf7WhqT2MPYFse70u07E08kNsH8havYglK9v8paByB4Cva+CJX8P/k4Q0XBqM/S5Bqzlkk1mq5Yg9PQekNS2QbgZRcTmb5U/X2IysIenYIrPhdoSXGk90Sl6lC+v9bfwFO+DlU+jTJ4pVquiButWQhuUbpPlXqyvgM4T5N5R9LD6ueAyB3EtxRI39t9yz5bsFzdmn6sgPAFT5VH49HK5PpHJmM64B4b/E757yLuPtO4SAH9gkVy7EQ1lGcqPwjd3o9SVcLT1dYz7UBYnaTEW/jJKLCrL9wUXlT1aWvfzBRZ/KTodZPSVkg2hSGwX3L6nCeodLvYVVPH0wn1sOlZOYqSZqwbnMKZrGinRpxfofKS41k8w+fLEgr30zo4/7X0FEmUx0CMzli0nKkK+PqhVIinRFq4anINRryMx0kx8hImOadG8suIgeRVWOqRGcdvwtqTHWqi3u6i2Oqmos9M7O47bhrUhOzC7uIGshAiyEiI4p6Gp8um45Hyx/IwVL7CcTFW9g7dXHeHl5V7Bt+FoOa+vPMScGwaSmxQZuAuNJtBEUzOgtNbGjIBVjofv9hSRHG3hjUt7YXW60SsKCZGmn64ybAyXCb4BHdApzcI3t57BsdJaCqtt9MiM5fWVh4I+On9XKTdPuIr0vV+F3nf3S7zB4R5aDUM5shLWvy6xKbZq9OZo1POeQ/n08gZrR5bUGjKYxfJyZKX386oKuxu+b4hPlWydUQodDntIxMPur8GeJ9aYiGQYer+4s7bMEKtK3+vEEuArmDxsfAcle7BYYjxFO7tOhG/vCs7KcVrh02lw1WIY/awIH1OECANfweTLiscxnHk/FEhTZOO++SLwStZLQH0g6T38M90MZmkjs+Be/0zBNS+KZa7XlbDpPe92U6QIxCU+k3pslgjlebcGu2eK98K6N6V20qK/yrawONTJH6O8P9o/9uzo99IDL/dsOBzghhr+iBRMLdoDpQfESnfxx1KSIqu/9HDL3yYCODZL3IsfT/Tfv95I3eiXuPcHPcN6vEt8mI70tHRan5gb7BIDmPi+XCNrmTeeqMtEuZ/Se4qwWt4gYLL6Sxzax5MaSjOkyXVzOcS1nNhWxhadIdd160w4uQFl7L/FqmWKgPKjKAvuEStch7FiAYSGEggdxP258T0pM+F2icV0xKOwZx75Vd7znl9p5c5Pt/HOtN6cLK+jU3oMDpebVQeKqbI6ObNdUpOCyeFyUWd3YzHqMPtUaj8t2p8HKx73Bi/7MuxBv0Dwn2Lb8Qqmvr220cp9qqKeh+fv5sdDpTxxUWcSI39e7Gw50bRr+WR5vZ/b0pfyWjuVVlngxYYZQ7rxUmPCeOj8Tkx+88cgq820gS2JjzARF2EK+syoLmF0z4zF7nITbtI3Lj4vHdCSM9sl4XKrRIcZgz4bil8qljx4xNvu/OAwiNRoC0mR/t9dWGX1E0weyuscPLlgL89N6k7kafbr+7OjnaVmgFvlJwO+8yrqiTAbSIj8z7NtdDqF9Ngwvyy5zBABmFVWJ3OOhXHFGQ8StfoR/8mu95UyUVX6ZPQld5Q/VCkBcHKTBIpn9ZOK3VM/FWuEyyFuE1X1F0y+7PlamvGOeFwe7I46sNWJuFj6T+/7Dq8QN9DU2fDZnRK4rNND2aHQQcce9n0DrYb5ZFR1DxaAHqwVUHkK5t8JFUfEUtP14qb3XZXnn1l4YDEMvqvpsXaZ5N8Qt9N4cTMFllZQVbEaXbsC2o8FR7WIMZ0BTNFw9gMiUsqPythKDjTpfmHPXNQz76f+yuXgsqOPzcC44hGUUEUyv74RLnxT4oYOLhHrRM5Q6R/3wRj/rMUdn8FZf4UD38HSf3i3r3xGShRcPg82vy/HmNQBOl2AWlfNpA6pZFqs6F11xEfqvIVUfel+iQRxf3uvf+Pfgh0igCa8J9mUHrIHy/m4eqlcw6I94lKNbyXnrGS/tBoKJKWjWC4rjom7La4lrHgKRj0tljVzlIiz1sNFBFaehPhcsUoW74XPr8B92VzmrPIXq5FmAwadjqvPyGHpniLCTHoev7ALpyrrGdw6MLUe7E4XJ8rq+eDHo2w/WUl2QjhXn5FLdmIEkacbNB6bCdO+gc+vFHccSLbiuY+JWD8NCqusPDR3V8g6Skv2FHJrRWsizUbq7E4sDen8Hipq7dQ5XERZDGTHN12zKMyox6j3F41ut5tDxbU8u3gf3+0R69y5nVK545y2tE4OtqZ0SI1k3s2Dee+Ho2w4WtZoDevUkKHYFGmxoa1tGb+Tqysh0sxLU3ow+a21FFd7f6/RFgPvTOsdZH1bsb84cBeNfLenkPI6uyaaThPtLDUD4sKNjOycwvtrQlubJvTM+E3Sk0d2TuVfi/cFPRifXVVE10supNOVo1GPrUVxO4htNxjD3rlSB2n8mzJZKDoJPled8O09EpfiQVHg3MdFTHz/tIiH6BZw4U/ULlJVieMp2C5ByzlDwRwBmz8Ifm/lCfjxFcjq5w0Qz+gTenXtwVbjv8r+uYyq+jKpGK645bMhyi00ojeCb6Wp8qNi/Wo9DAIbwmYPFrdfoY8IaHuuBFOHQnXDsR9FBBxYLIKx60TYMRu6TZWJMLGdWN9CWdl8xqu6HBTUqihuBbOuhvTAwHAPTptYK7pfKtfVZYf4XNT1b6KE6j+4/HHJ5Fv2sDcOzGmFz68StyCKZECWH4P5dxAx9kWGLLtZXIkA7c5DTWoTvN9uk+HEOn/B5KFkv7gxswZ6t6V2B9yw7FE4vNy7vc+10G1K6Gy79J5iQVz2iHdbeDxM/Vxiv0zhMv6k9iKq0ntKhmXRXnk9rRsU7kL54UU6JtyHp/SlosCzk7rx8Pxd7C/0uqi+3prHpf2zpBJ1AFtPVHDJ2+twuNTGf3+1NY/nL+7OmC6pmE7H6qTTQ4seMH2huNFdDnH7RqX+fIxcA5X1jsZq14Gc0zEFu8vN/V9sZ1deFS0TIrhhaCvSYiwcL6vjze8Pc6K8jo5p0VwzJJd7z21LQZWNQa0TcbpU6h1OPll/gkGtE0gxWqE0X36LlhhOOaKZ+MaPVNR563R9syOfHw+XMueGAeQk+gsns9FAm5Qo/jamPeV1DixG/X9s/fm9aZUcydc3DWJ3fhW7TlXSOjmSbpmxpMeEBcWNOV1NL6p/JilQIwBNNDUDTAY90wfn8NXWPL+HBUDblEh6Z5+eOf2XkhZj4ZVLenLrJ1saH9IAwzsk4zJEcMQWRoeOI1CACiWMuJSu6COTJbjXE9PU73qZYCuOQa8rILWrTCQ7vxA30JWLvOKkpgBMP5MtZLRISxOQ+BRfq1Ygu7+SyWvnHPl38V4ROVtnhn5/7pn+liiXXaxiTWWnJbWHwyuh/LCIksx+TZccaD/Wv9I2SM2hkU9J7M+2WSKiOk8Qq010hpy7fd82ZPu1aDrjCSS77PhaERmnNsnYRzwKBhOsf1ssI5Y4yOrT9D5is9DpDeR+2tDjLiwO9ey/o/S9RjLwAlCTO6N2Gg+5Z6HoDKA3BPej8+XoarHS+MaZWSvEhbV3vjf26sLXYd5t0j7Fw4GFKANvhh9e8LduhiXAtoeb/s7ts71xbyDZbTvn+AsmgE3vSlC+2yVi01M7KmcoDLpNrDKmSBFLtcVSqNRWLed52yciAONz4ey/Q49L4JPJ/vWn2o1C6TKB3i6vW2VomyTWHCzxE0weZqw9zkU9M4j3cQEVVok7z+FSSYgwkRZroaTaTkGVlfu/2E6f7LifT8/3odKQQLkhCpeiEqU3kHyagglApyjolOAJuVN6NKO7pDHpjbWNWWj7C2volhFDhFmKR3rYX1jD/O35vHtFH4qri7hhxqbG7Lm/jW7PmBa1GD69DI41VPuOzyVu+NOcnRvJFzv9f5NltXa+3HyKW4a1CRmaEG42Em7+bVL0XS43JbV2VBViwgwhxe5/isf6P7whNsqDqqoUV9twulXMBh3DO6bw1MJ9IfcxIDee6DBNCpwu2plqJmTGhTP3pkG8teow3+4owGTQMaVvFhN7Z5Aa858FSv4cYSYDZ7VLZuldZ7L1RAWV9XZ6ZcXhcLlpb6lAv/YlDDs/BbcTS7dL0XUZD7OnBBx4X9j4vsR5bHpfYkvC4sSt0u86sXx4rDtul6Sit+gtwdyBtB7uP2HqdP6VoQNx2SXuKfesBqtXInQ8A/bMDe5tl9Bagth93V+bP5RsLN8Ufw9dJoh1LKkttDpTJvjVz4u7adZUrwAAKSjZ41LZ7nd8DnEVFeyQbDbFJOJk5BPwxTWQO1RiptxOcR2ldmmMiQqiRS/YFGBxW/aIWKhiM8QN1eUi0FtE3J1YF7yPwXfiZw2rL0f55k6xHHqCuRu2Y4mFoXejO/SdWMqMFnE3/lRBSacVolr4bzNHSfmHS7+Q1z2WLd8+byDnYOPbMOZ5iTPzCJL6Urm2TaHo/LP8VDV02x23E+ZcDZfPlbgua6VkLyo62T7ySbk3q/MlFisqFRb+RQLePZQdFnE19t8iqH0thfsWiKUzulvjpnM6pvDCdwHuVh9mbzhBlMXIsr2FDY1jo+naIoa7RrRDAYqqrMSGG4kKM/LCkgOcLK8/bdF0uLiGB77cwY+HpWdhdkI4j17QmZ4t4/xcaU0RadZzVvtk9hVUc1O/ODok6Kl1gBqRxD1f7ApK2x/WIYVxLwe7V51ulfu/2MHNZ7duFGDF1TbaWCowvH++f5udssNEfjaReyctYMURE2W1/ouIFfuLubhPJioi6uIjTL9dgdAG8ivr+WzjST5edxyr08WIDinceFZrsuLDf7N0/5IaG4t3FfDysoPkVVppnxrFfSPb88xFXblnzna/94YZ9Tw0ttOv3lS5OaOJpmaCoihkJUTwt/M6ctNZrVEUhcQIE/qfCvj+FXC7VZwuNzaHC9UNdQ4XXSKrMX8wRmooNaA3WaTGC8hEE9tSMtiMETDwJhEMnliaulKJQWo9TCr57v3G+4V1pTDs75I551v4MXuwxET51nY6uRHGPNtkAUPaj5Xg3eQOMpm3HwML7msQcB+I5cdghs4XQc9pIkjC473Nb1Wg3RjUyGSUFU/KpBiegNr/JpScIVIWYcNb0uolqZ2MpeIEXLkQTqyFmmKJhXG7UC2xKIPvkh5xOh2UHYV1b0iq/YyJUnwSxBJzYDGceZ+48H58SY6x3w2SrTVzQrAwyewn2WiBtbOcNsjfDls/FhFwdBVct1rO48HvpPeevRYSWsGgOxoCom0SWG70FI6cBXnbJGC7cJeclOh0iG2J8vmV/iLujLtEJJ9owgWY1d9fZJij4dLPYdlj3tguT6bkxTPEdZvQWsSl3ijHe2gZXPu9uOMcVrHIdZkYWgSCiNvihhW4Ti9/Tblo3U4pnrp/oYw9IhEuekdayMy7zXvvmaPggtf9x+LL6uclI/Lbu/23b/qAqJHncftwcTPGhBmxOlyYDTou7JLA2S2N2Fwwc2c9a4+USz2vJfv5ZockIpgNOl67tCdGvY5wk57UGAsGnUJ5nZ2Hx3UiJkxPYZUVp1vFpFeabNVxqryOSW/8SIlP8dujpXVc9u56vrpxEN0yY0OPy4fUmDAeHZWDqWg7CT/cJuLfEoOz97W8PPYips46is0p92lipIlT5fVBBSYbj6einthwrxWoU3o0qcU/hO5LqKokrX+SK3s9yLPf+wvraIuBBTvzefSbvZgNOib0zODGs1vTIjYMh8tNrdWJyahrFIX5FfWU19kpqraREGkmPtxIi19gqSuotHLV+xvYne9dgH266SQLdxUw9+bBQSUB5PBVCquk8rhOgdhwE7HhJpwuN0XVNqqtDswGPQmRppB1mKqsDl74bj8z1nqtsHsLqrny/Q08O6kbn1zTj6cW7qO8zs7gVolcPSQnZGyqRtNooqmZYTbog+qC/GJqS6QdSG2JxLlEJEFEAjVWJyU1NqxOF1FmI5EWPasPFJOmr2Z0ih3FZcdq0qHu/dZPMAGSjbdtlkwWuWdKPIfBJPE1Oz8PHXx8cKlYJ3xdbPYa+PomqXg99F6JFTJHSUr6rKli9fBQXy5lAXKGigm/RS8Jgi7cAS6nTOKzpnqz0CwxYgGadQl0nQTnPScT8tFVUqdn5xwY9S+pQYQi21c9g5LRR6xiUVIbSUntIi7CH1/xHkvhLph7s2Qf2evkNUusiCprJUqnC8VS9uW1Igyi0uDcR+H4Bjj7b+J2UnRSNiGhjZQuSGgtn3E5YPmj0HYUXPaVxOOcXC/773M1pHcXy1QoDBYarUfmSKgrkfNRVyoWEb1RrCfbPpbaVo56KSVgr5XreOmX8l3vjvBa9XQGGWfOUH/RVFMk1qrPpgVf704XSmB632ukerPBLO7CFU8FZ0rmbZGK4EseFJeq5zt7T5dsSL1R6g25HGCvFotcek8Rsb6kdhUr44HFkkmIKtlvjbWiQpDS2RsDV1siljFPYLfBIvdQTIYcY1OUHxGrZSC1xeTGW3hrs4iBs9sncVn/TCa3cZG87TUsqxaBMYIhnS/n8ODRHLJG8dxib0Vom9PNDTM289VNg3hk/m72F1aTERfOZf1bkh4bhqoqnP/yagqrbOQmRnDfqPb0y4kPyiz74WApJTV2OqVHM6JjCiaDjs3HK1i2t4inFu7ltUt7ERNQj6mizk5JjY3tJyuJNBvo0iKGlKpt6D4f732TtRLD6mfomrWWJ0c+yh3zfZ4RP2N0UXze0DY5nPj8VU2+V5+3iW6dgqe2sd1a8PyS/Y3naub646w5XMp7V/Rh9oYTrD5YQnK0mRuH5pIUZeHeOdtZ22BpAxFrL03pcdrp+dtPVjQKppzECIx6haMldVRZnby64iAPj+vsZ+mqsTooLCrCbCshonQPqjGSsuhcrAkZLNxbynNL9lNV70RRJATiH2M70yLO/1lfWmNn5rrjhOKxb/bwzS2Def/KPjhcbqLDjL88s1JDE00aAVQcl3R53wkmayCOC97g4aVlOFVZAeeX1/GXke0ZFl+C5cvpUlwSCB94i9cClNBKJnVFJ5PYiEcl8HamT584nQGG/0PccaFiiQ4ulUmo8qRYAZxWEUOe+jc6g39Atq87LralTNpn3CnCY98Csbb0vQbic2DtWxLgW3FEjvHkRugzHb6+WVyFm96X/ZxxlzTPbX22CL2TG8TilNkHfnxVLFRzrhZBodNLptq6N0Kf39XPw/SRMp7aYq+bbs9cOc7+N4gQTO4gVpNWwyA6FcIfkYklvrXEyYx8UuKZNr4rk3Xni6Q4pc4E570gwfUoIpw+OM/fHehBb5RYJo9lKioNTq4TF2SPS8TlVlMk7VQG3CwFH8/5pzeG6/hasFWKG8oXt1MEzdRPvfFlLqdYDI1hMPkTudbH10qAcY9Lxcq05t8iTD3HkzUQhtwl96Jv3Ni4VyQmyNdF53bKsVYchxkX+t8HI5+BsS9KYdU9X4vw6nAetBwsYti3ltJlX4v7LVRF84TWIiyNDStznR7qisTNe/5LYhmrLZJMu1ClDzzEZokF7OIZ4kZ01MHGd1CNEVQ4DcxrKGa56mAxa6/LwfLeSG+ZC0qIWf0IXdO/IXX0u9xd6V9PyuZ088PBEk5V1FNSY6ekxs7WExXcdGZrLurVgtIG69Hhklqu+2gTj13QmYv7ZGJosEi73SqrDxbz4uTu5FXU8/XWPOodLs5ok8R7V/ThiQV7qLY6qGtoWh0bYaTW6uLphXv5dJPXyvvK+S0Ysyl0VXjj8VX06VdHuElPnd1FSY2d7IRwzAZdo/XJl4y4MCn22oDdBe6ELJq0oUelYjT6i7px3dOpsTkoqPI/X0dKavnhYAlut8rUvplU1DsorbXzyvLDfoIJYFdeFTd/vIW3Lu/1sxYnl9vNnM2nmNAljht6RxFdvBGdo5661D4sOanj5bWF3HlOO1JjvKLFXlVE2oYnCd/xoc/JCqNu3Lus2x9DVb2cc1WFJbuLOFa6nhlX9fPL8jtcXBPUecpDWa2dynpHk5l/GqeHJpr+xNidbgqrrNTYnIQZ9aSbajF+Ph0lcEV+fA36ebdyw9kv88z3hZwoq2NS70ySKcEyY6zPAx2Z8MPipF2IvRZ2z5UA5pQuEpgdKIzcTokJmvJJaIuT3uANcHa7JBBc0XknVl/BZDDL+z0kd5DPnFjvrcMD0pKjRU8Y+5Jkdh1YJMfYdiQktBWBtfIp78Sb3ksyzNa+KhOv7/eNfRGqCsQCcXIDoEBtadOZdbZqsTRduUD2r9OLu+fHV6DsmAg7g0UsUOk9JF1926ewbaY8Lc97AdK7SVZZXDZ0OF/cnFs/FiE39C+w8S1vcHvu2dLX7ZNJwY1vz7hLKmibIkRM1pVJSv93/5TmurlDRXTtnCPi0xztH2zeZUJwnJQv698SS1TBDjlXMRnijp19CWQPEqujrVrEzvo3vBYcD8fXwHc1YlFc2FCJOyJZrEeBMU06vQi9jycF9ytceA+Me1Vat7To1XAdqmDxA+KKjG7htYzW5ItAO/dxifOKaynnqK5YgvArjjfUDWsp56KmUATTN3f61826/GsZc+D9nNReXMbf3OFtAByZDGfej5LSGaPqFXtjO8SjW/Uv/99XA/q8zeiLdpEWE0Z+gHAqq7UTFZA+/uaqQ4zrnk5smIGSWu93fLXlJANbJ5JfWU9ZjZ1umTFc3CeLF77bz6Zj5XRIiyY+3MSXm0/y7Y58Xpnag115VdzysVjSzuuaxvTBOaw/6i8wsqPcjQupUEQUrCc7oVdjnSG9onDPue149Js9fu8z6hX+OroDbp8YKL0OlPajYd2rcq1NkbIA8AjrXlfSqUUsD4zpgA7o2TKO1QdLeCxg3yCxWm1To7A6XSzaXUhylIWBrRJJiw3tutydX0Vprf1nRZOCwsg2EZzp3ELcrHsan1cJwJT2E2h93l34JrhV1TswHFjgL5gAHPWEf3Ep9128nIX7Kvxu7f2FNRwvq/MTTYFFLQP5yfp8GqeFJpr+pJTU2PhgzVHeWnUYq0Ma/K6/JoOkkxtCvl93ZDnUFvPtDpmsLu6dif7YquAH+v4FYklY97r0X+t3nTzYIpOkrEBT7P1WrFK+8Ut6I2qXSdI4t7ZYJu3IFJQBN4tVIpD+N4ulJb2nTGZp3SRm6N1zg997arNkThXt8rp/jq6WYOrR/5LvLNwlD+OUjlIF2lcwgUyIc2+BKxaItar9aCg/IQLsp1AUESU1RTKJtz4HOl0EzjrJsLJVS0d5nR72L4YDC6VaN4jA2fWliLWC7RJ7ZLBI7JPOIBO6b4uSw8vEnXjVdzKGgh3ifupxCeRtFUvPJZ/LBO9yiiCJShVREtjQuOflctweMvs1bVEDqDwu1jBPLJsxTKxE49+Aoz9Ihlp4gvSSe+us0Pso2A6xf/FaFLP6QmHw5EfLQXBoebBg8rDyKRhwo8Ss+aIzSMX0+bfLv7d/JuI5paOc8+2z5Xz2vkoyPNN6iGW0/Jicp+ROMGuKv2ACWPWsxDV9eZ2/0Bz5lPTX87Wc1RSJFW/yJyQkJbHtWsmE0oXFYFqUL2Kz15ViSQSpM7Z9NokH59Ar6ybm7/AXTR3SooOK3TpcKifLZYL1iKauGTFcO7QVH609St/sBHQ6hc3HKkiLtdAhQcczZ2UQfWolekcNNWcOZW1pBHO35REbZmqMP/piyylW7i/m2UndeHT+brpnxWF3umXxEmgF9iEiLplx3dPonR1HbJiRo6V1HCyq4c3LevHFllOcLK+jfWo053dL561Vh7msf8vGz7pVUPO2oUx43xuD5rRKLF3FCXluuJyN1imDXofbrQZl8sWEGfnn+Z24/qNNlPoEjX+68QR3j2jHuO7pfL012GJYGtjoPAQ6ncLoTAfmt4IL04bt/Zw+OUMwRXpb4Bjriwnb+FLonbldxB9bQJ/soaw/4i9Od+ZV+mVHZ8aHE2U2UG0LPu9dM2JOq+Cmxk+jiaY/IXanixk/HqOo2sb7V/bBrFdQ0WGxh6hl40OUYuW5Sd2wGPW0TYlCvyZEBpsxXOJh2o2CxX/3xoac/3KwdcCX6nxxD3nQGWDafJTlj8HuL72TYVo3Sf1O6SQPZHOUCDe9RVwlC+8TC0hYnLgE9y8K/X0gAeIjHvGvg1SwQyxTsS1FEKkuuG5V053po1uIsEruJJ+Lz5HPRiT6F3D0EJ/rbZILUqU8MlmqYm983+vO2j5brFfn/FNqTXmCp43hEiA/73aZxD140vV7Tw/+zl1fSGXqrheLINDpxSrVorcEnZ/aJnE2yR3l+Ma/LZYQT6agopN6S+k9Jb5tyN0SBB8WA2ldg2OFPKR29fbMA3ERzrkarv5OLGrJ7b3tU36q5lVNkVzn+nKx4iW2Dn5PWJx/g92gfRTIe0LtO7alxF+57GJdOvMv8OE4n9IQByQAvPd0UAxSMd1zXi6f6xW0vhz5HlCk9tShpXIv5AyR2lBNlahY/RyGgbcS8+ll8u+IRBj3urhllz8mcVI6vQj6SR/BweXYA7qrdEqPpqreQZU1+HxaTHqOlXmD3O8Y3gaLUc+wLD0t9KfQuaxYw2IwG2K4P30LYZ943a4xPMXYnOFk9HiEozb/AObSWjtbjldw14h2fLbpJBEmPYQloHa8AGVng8jW6b2WTp0BV4s+hB8xoqr1xEWYUBSYteEE87fnM6pzKme0TuJ4WR1Xf7ARu8vNPee248mLulBR66BjehRuiwtdxVH4+nZv4L6ig37X48oewmd76jhSJNs/+PEY53ZK4Z5z2/HMIm/a/aTemby16giltXY6t4imfaqcu5X7i3luyT5mXNWPvtnxxEWYqLU5+WLzKX48XEpK9GnUcVJVTNuaSEABwja8BJ1GNca26XEFx4H6EFF1iLjwc4K2BwZxp0SZeeOyXlzx3ga/wPqECBPPTermV6JC4z9DE01/QoqqbXRuEcXktmA++jlxBT9QG52LufvEpj+kKGCJ4aGPd1Ftc3LL2a25Ibmz3ECxWdJ6wRgmPdKMYbL69o0rKdgu7qbAWkQesodI7E5tEYTFy8p6y0fe6tse8rdJJpTDKj3CqvJk0hvaEKRcdthbpbvtyODGsL5YK73xKb5sny2VnMe/JaICVQLQkzvKxBmRJOfj2Br5jo8n+U/Yu76Ci96V7b49zMxRUi5gQUAMUE2RFIIc+hevaAIo3CnWnh6XNwQqI66IfQv9BZOHQ8vEGhTTIvi1U5skYNsjvhJaScXud0f6ZyHFZIo1aMDN8kB32uV6RqVLnJQpQlqA2Gvlmg+4WQRQoGDRGcT99u5I/+2qW85PdLq0rwGJZ/OdVAOJbektZ3ByPSQ857WGeSjeJ7FRHrdkIJ52Kb4YLJJxqNNJvJIxTFruHF8bupbWxnel9IHeKPe2Mcy/VlQgR1ZKq5hN70mAeG0JOH6ieGrhTv/YM7cLcMMnU72i0u2Stix5W+CSOdTPFUuv2aDjwh4tOK9rGtfPCBax8REmMmLDeeHiHjhcUrsnOyECS/VR0n680esqNMfgnvIJuiV/CdqH6ch3tG95JpWJwc+JpXsLcbjcLNsrKs6ic/HU4BtQElqJNdVeI+eruhDMUdjdBlolRNAmOQJVVYm2mDDpddTYnHzmExsFkB5jIdyk55mF+4i0GOiUFkX/YZHw1fX+94zqhrWvoqT34pKMEswFkhRSNGoKW2xu3BHhJEeZUQGb00WvlnEs2VPAW5f3Zm9BFVuOV5AYaeblqT1ZtreQIyW1tEqOpLTGTmKkmWvOyOHqM3KIC9GSJQi3C6XqZNOv1xT7LRRMlghZZPhmBPtQnjqIIxv863VFWwy0T/WvW2fQ6+idHceSO4ewdE8RB4qq6ZcTT5/s+F+U+afRNJpo+hOiqiq9wouJm3V+46o3AsBsQm01HOXQd0GfcbQfx0c76hvNvq+tOMT1t54tdYL0BolHsVVLcUi9WSZfXyvD9tmS2XZoafDkGBYnVoelD4vlwlYjMSTbQ1h3elwqbqUNb3u3VRyTjLqzHvDv9VW0G859wr/diC/ZZ0BdiFW/6hIz/6Z3JV7orAcl/ic+B75/VqwyeiN0uMA/5spD3mbpU3btSonTKj8q8VW5Z8P827ytKXw5vByGPxS8fftsCQbfvwBQROzs+Tr0eECCxFsNC96e0Frceh4GNhRkDEzbrjwhvf06XShtSSwxkuk46mnY+IHEWvl+V1IHmDZPXE4lDZlccTkw6ikRpek9xErji9spQe+dLxLxGRYHnSeGvt7JHaSO1FVL5P6yxICzXkoczL3F+52lB8RyFpkSLOAURQTcN3f6b7/wDXEHZ/SWTECdpxWLKu/3zX70ULBd6inVlkimYWSyCMTOF4mIRJXx7fhcXKc6vdcSZQyX1jtNEZMhiwYP3aZIrFcoK1zlSZwnt3DLsBFM6ZuFG5WMuHBKaqxBNZAiTHpmXN2PZxbtZcuJChIiTBRX2/hkciZpX030D1rP6ofOYx0KdYhb36T1uNHMv1bqSb23sYQ5m08RZZbyCB6mdDSjKz/QUNH/GW8MYlwOnP8S4dWHCbf04mBxNWkxYYSb9Px9bAdeW36IaT1jyYjSsafUzcdby/jr6A7EhBlZcE1H9DgxhMfj/vHZJkW2bvWzhHWZCHvld5K692uG5Qyj9KynWXVVJra8nSimCAwp9aRN6MRVM7b5lVf4dOMJ7jm3HbHhRmauO47T5aa4xkak2cCtZ7cBVWXVgWIq6+wkR1vIjAsnLaohy7T0kNwX6T3F7e4bbuBLZj//Qr3h8biG/xP9B+cFvzcikZq0/uwv9Fo048KNfHBlX9JCZEqbDHpaJkQwfXBO6O/W+H+hiaZmRH1FIUrlSdT9i1GNFvTtRuKKSCE82r8ieLRaTczi24LdBD++hDLxfVRjGMq++eISU3S4O41nZ4e7+XxeHlcNziE6zMihohqOVSt0OP6j1wQPsmLd9J5YaXyLOForJRvtonclPd7j+snqD+c8Kun4xXtltQ3iYgtVmLLDWNlvTAZ0nQzRaSJwtn0ilqfpi6Uvm+oWq4gpUoJvPanpHnQGsZLUlwV/R5dJkNJBJlODRSxgGX0kY8uDywE7P5PyBSMekYw7X46uEmuFJ3tQdYkg8YwvEFWVmKJAbNXipqmvkDGpatNxOyABH7UBfaYik0WYeMz/iiKVr5uykmT1k3pKeVugcLeIPUetv2DyULwHNrwjgdMGsxxbVb6UJRj2DzjnEZlMdDr5vtoSEVqfXi7/1hulb96g26U4pDlCXIQ1RZC/A/pdK65AnUEsdYoC1cWQkCs1kupKxOoYHi9CfcK7Ymn0xKnF58qxxWZL4HxNgyjpdYUkJmT0FiufRzik95A+eHqTWNR8LU4RiZDZXyyOxfvEWhbdAq74VmK3iveKFbI6X8RRn6tg7wJp5WKOls+0OgvWvRZ6wu9zrX9T5dTOoVsAASg6DO562hqLySQPtzECkz6FNzaW8vqlPfnhUCn7CqrJiAtjSt9MPlx9mGu7GUlpXYap4jD21J7EWA8HZ/mZo7x1yEJRV0ZLYyX6JX8D4LHuV/DPswaw9JSOfy3eR3qMBbvLTWKkGQ5uDa6RVn4EPr8C5eJZPPDVDnblSSB4Vnw4X17ZgUmxbsw/3A8VxxmZ0oWbL78Pe5gT88G5mNf9G2pLcA++UwRZU1SekBhKH0xHlpLSdS26Vc9i9izmjOHknPcWg3MSiQ/T0zvVQKlV5YOt1Ty7eB/L7z6T9pF1GFUHTiWaCl08u/IqSYhIwmLUo4aZ0Ot01NTX46rYhn7WJP8Yz2nz5bdXU+R3LOj0kmxi8bcS6dO74570EboF9zY2AVcz+8H5LxEbkcPC29I5WFxDYoSZrIRwUqMtv1mBTI2m0URTM8FWkY/+27sx7Z/v3bjiH1gH/4XKnldxvM5EXqWVMKOOvtGVoeNQHPXw2RWo0+aj9L26oepxNE5TDK5aE38b04E9eZXgtNIpPZpWxlJ/weShtkRcL53G+2fL7Vsgrrdxr4prRVFk5X5irdRFWvWcmPFBsqT0Jn8rjifos/9NEqi74R15CCe2lRT8o6tkpT5nuoiarg2T1TkPi/Vp5+cyxqwBMOhWmbx6XOZ/7HE5IhhmjJfVotsp1gtfy5YvxXulqnh4gtdqYzDLhL72Ndg1x1vJvMelEhw873aZPMNiJYbm1CYZQygR0+Yc+fyZDe4SnRE6j5d9h6LHJbDob95/p3SS8a99Xc5RdLqc41ACDaD7VFkB+wbPG8OkDUtT7PhUCkh+/5QIu3ajpbXItk9kfz88L9cjNgu6XAyfXe79rMsh1rSCHRKrtuQhaaETmwkDbhHX3JYPRcDYqkS4DLhZRNPJDRLjZooUsbbuDWkKPeg2CYx3OeS8J7SRMhedL5RsOWi4XmUi1n3J2wJzrpFyCblD5Z4FySK88E2Yd4t/VfiIRClTkNgW1r8pVs/kDtD3OqgtE/G/7jW5thl9ZTwXviHFMBtjcRQpntpyoPTe82CtkuMMLLapKHD+SyjHfyT2mzuJ9Yjo2JY8MvYDJn+9m6QosYAUVFqprndwexcbqV9N9E7q6T3kHgykYIecw91fhbzUatYA9DtmNzZHthz7AVd6L86d9AmDLs1Fb69GMRiIsuibFny1JSjVBVgdXtEwMCsM0/YZmFc90rhNX52PvkVPzGWHUXZ82rhdt31WgxVnPiFJ6SQFZQPQbf1Y6njVFIrbfO+3RG97m38N+yfu75/FtGYtRCYzcsCN1Kb0JvnIXMJWPy7XLiKJqj630qbLBB76bj/ztufjcKlEmPQsvzoX/cwLg126c2+RpJiVT4rVUVVFcI95zhvU74s5Cl2HsSLkrZWgM6KEx0N4PLFIocv2adGhx/wnwe5041bV37yS+0+hiaZmgKqquA98R9j+4IeIZcs76DtfSOqp7XQ6vhBbWBKmHlNC7KUBRz26U5sk+6fBzWECuo18hsq0wZx1+C0sNSeo7nQpxj0/Nr2fPfPggteCSwx0niCCLTCLaci9ElTrcUVEJouQ8sS9gIiHqDQwHYAvr/dury2R+KKRT8gE77FQnVgrWWizporr5PyXRHQU7pBJKy5XVtYpnWTfHcbK+wp3inXi8EqxNKV1k1pMq72FBP3I3yYPweMNomngLWJV8+2h5rJLTIzTBtPmikWhplBE0dB7ZeKfd5tY0AwWeVDHZMCIJyQLraYQUEUE9r1eAsgDrQQtB8ukfPEMqZ+EKr3X9i+U41/zb3GZRiTJw1xR5EFuDG/omVYq12fmRf77NZhDx/h4cNRJfz1Ptt3BpeJ+GPWUCLTsIVJBvN0YKWwZiqLdkq14aqNYQGsK5b2D75L/91Qzry0R96G1SgLl325wRepNUn8rf7sUAu0yURrl1pZKUHbpfsmg83DFN7D2FXFl9rxczoVOL/v/8WUpd5Dc0Sua+lwFq1/wF0wgxTF3zhErp4dja+Rv9L/AnSbXyrN98wcweZYIRGuliPiYTLlGyx8Vq5QnSWDnHIn38mQfemg1DEoOoPj+NgAqjpE4ZzzvT1xISY2NSMWGU28hPdpK9MdXNSyCosTqaK2AmCz5/95XQUYv+d2oblmwxGYFi3idQfoMBhRK1Sfkostbh2XxA42fUad+5o0Z7DJBzqW1UhYuJQdwVeZxRe+BxBtjOFHl5tzW4UR9/IRUzu82VSxFFcchewjKyif9j+PUJhh6n7hqQ5RjoN8NoetsORvO9fbZYoU+++8QlYJhwV2yeOo6HhSFpP2LSCxagxKZ7D0HtcVEr/g7tsqjJJunNPbaTIwyo5zaEPr3UX5E7uHpixrOrQvMMUFWMD8UpcGCmd70e/5DPNXGC6uksniLuHASm6gs/t9GSY2N/QXVfPjjUawONxf1yqBPdvxv1iLsp9BEUzPAWlFI2IYQ8RcGM1zwGsZPp5DUYJIOA0jvLD/KUAX4FEVid2r9TcrGZf8k8YLXYMu7AESBFPH7KXQ+NUEMZnnQ6XT+wdEgk3jumSIO9n4LUSkyeXedLJPKri+9MRHmSP8JypcVT0p9nL7Xifup9LAIopgMWTn7rp4VRURd/g6pdaTTi+CozhOXwvG13vdufEcCwIfcI/EZgUQk+T+8M/vB9/8KfYzbZ0lhxT3zRAAdXiljmvatCBlbtTxcjWEyKRz7ERb5NDi2xIrlaNo3Eke252sRWb2nS7B2+RE5Z3vmipWi1zSxgnzsE7xbWywB9n2uEUFoDBd3QFJ7KboZ6P47tUncCdtnhx5Tq2HBrVF6XSHVvPcv8G5L7ugf5xZIwQ5xp/kGw655Qc5LYPbiutckzim9p5ynkn0SgzShl2Rq2qtkosoaKFXWA3HaxeUanijWJk+geWyWiO/j68XimNFHzmP7sbKQCKTLBKkgH4oVj0uFdl/qy2V7encR0Xqj1x1mipA6XB7RVLQHdfQzkLcFZf9C7z66ToJvQxeOpK6MFjXbyVj+BJQd8pbtGPOcuHgVRay8sVmyaJjwntzTqxruV3MUDLgZ9ZLPUde8jM5aIftw2lF6Xi7n3fdej0yBDuejeDL+GlDqS8Xi2Ocq6dH4w4vyO+lzNcRmozdFMXX1Q+iLduOOaYmafQdMmS3378Z3RKx0GAeGgPvKw9KHJQRg6cNel3dkivw2DiwOnUnZZiSsedFbrb31cPm9d71YBHXVKRHfHcehdJkgv4s1//YLEzBvfY9Jk6/k9YbHQ3y4CWPVcfkNdp3k7X9ZXy4Lo/xtsmCKzw09jgDKam3kV1hZf7SMKIuBPtnxJEeZm27yW1sswfVVp2T8UWny/AzA7VbZnV/F9Pc3UFQttcMUBS7ulcld57YjKeo0MgL/IEpqbDwybzdfb/POVyv2F9MuNZL3r+j7uxfr1ERTM0BRXaHTmDteIK6TwIlqw9tigZl7S/Bnul8qwYzjXhHrh60KtsxsCOb1mUwNYdBxnNRjCkWH86Xq8YR3G4pSRojVad+3YnFo3I9ZHn5zb/Y/zjUvyUow9yx5GDlt8kCrLQndcgXkYe60SzC6tUIm8/Jj0sR107vy3W6XuE+G3AtbP4G2I+CLhkl10kdiqfAVTB42vguTPxYXjcEsIuvYDzKZtznHG8Qck9EQC6aIC6HHpSKArNWw+X2ZIKMzxApRVybFEytPyvFuel/EjMvutdR8fYN//Iu1QoLJL58n4qr9efJQ//FlEZrmKHFbeYK+N7wtGWFdL/YXPXvmiUXq08u9mXgteoulI5DqArn06T2C24MYw2Ui/NTH5ZbeU0SYr2ACsSIaw0JXJwc5F/u+DfiMS2JCLLH+jXUd9RLL1PZcmSA7TxBrn9sJs6eKGDFYIHeYuIQDqTwBqd3EletLxXFxz02bJ9afrP5i1QpYRMjYw0SINFUuoa5MjjGQIyvFKrbhbX+B6rF6TvIWOFSWPYpzyH0oQ/6Ccng5mCNxJ3XG4HsuAlBKDsq+AFwOlK0z5fizh8ISH9ftJZ9L/zvfGlO2arFsRaZQ2+MaIja/ic5ehbPzRIwKwf30elwqRV8DOfgd9LteeiF6XOzV+bBgO3Sbgj66hSSFALrqfJh1sQjzwt3isgZx3ce1DN43iFD65m64dI4IZtUtbszYllLHKpDYLIkRW/G4d1t6T0kk+PZuKXbb6ix5huydLxbFUU+Ka7dot/czqpuw+vzG6uXHy+qwpvWFiz+SArOfT5f7ITZLWgZVnsCmmDkdSVJcbeWvX+5gyW7vvabXKfxrQldGdEoNLlxZeQI+vcK/eXliW3Etx/sHgedV1jPlzbV+9ZtUFWZtPEHLxHCuPSP3N+9T+p9ysKjGTzB52FdQw5zNJ7nhzNbof8fYLk00NQP0YTE4s4Zg2PWp/wttR0pxvUDyt0pdn0u/kBVm/nZJUx94qzwkDn0nq8+6UnGT9b1OXFe+3eKT2smqptOF/llZ0NAa4xL48HyZ9BTFK3Q8Ky6dQR503S4Ri0koC8SyR+CSz+Dji+W9iuJNvW+K+jL/gO2O4+QYI5JlVQ0yMS57RMTKGXdInAOKCB7flhqB7P5aYljytsgDOjJZJtfqIhFmpnCZzN1OmPB+Q7+5W+RBHJ4gwc5tzoH3RvmL3Es+g69v9D8H0S1EkIYKGPbE73S7WLLWfM/X5V9L6nKXyYBT6lR9/4xMkGldRbBZKyWe6our/UsXmGO8FbMDWfgXOfd5m8USYKuRsfSe3tCLzicduvN4EZmB7JknQfahYl30Jgmy/u5hGbu1whvLozdKFmMgdSU+TaDfh+t/EBfauFdk4rXXymTY+UI4+r1/YoGjXkRsKBx1YgHMPUu+Q28UN5Yx3N8N44lV+0lUyTy0xMjku2WG/K5iMuG85+U4otPFDRiRItfA48JUVYhugUFvQlWdKOndQKdHMRjEAtZEIVrSunpbAHk4sEQsix6XbHiCWCkCi3I2oKx8kqjz/g2pncBgxnjgW9Ti/SgXviGlLVoOlDeGx4cWTUkd5LcUmFkKEus2ZVZwzOLKp0UwesqMFO2WjNKYTIlBjG7RUOrBAnvmi1i2Vvr/BqJbyPU/sETiCVHEVZs7VDJsfbHEyLNr6myxCJ3cINawi94RF7PHdeormgDVFIHTLda20lo7lsR28NWlXrEH8oyZfzvqxA+o0icQ6JBzOZ04K/NQGizIangiu4tNfoIJwOVWufOzbSzJiKV1cqT3hfoK+PpWf8EEIgJnTYXLv6LOlIDN4SbCbGD7yYqQBS8B3vj+MBf0aBEyE++Pxul2M3NdiPIqDXyy/oR0p/Cpiv5bo4mmZoDbGAZn3Ilh31x/15dOH/qhBWJ1SGgNo56RCU/RSfzLmhf9J7WaIpmMB9wkAYrjXpUJxNBgMeh8kQiT9W/JKrXdKGkcO2d6aItC7llS/Tq1q0xG0S3gtQFND+7UZtmf0yoTmKrK94eyrMVkBGeO7f5agruzBnhrAUUkysMxrRsYI72Zaei8sTOh0BlF5LU/D1Dk+De+J0KgeI8IkeoCSO8NB5f4x2PVlYq4qC8XEecRFbEtJfsvUDTGZEiTYZBrk9hW/luyX0RZyV7JBpv0kUyEJ9bLZFRfLte14rhMaH2ukhioop3S8qRkv5jxB94qgdaL7hc3SmyWTNp1ZaH7AOqNUhspLgfOe1FKLdSWiJAITOYzR4fuQL9nnkyKRXuk3pIHg0Vcao56uOgtOReRySK0f3hRrJSBwdAtevq7YHpOk+Df7IHwwVivOxdkfJd+KT34Go8xIjg+yZe8zVC821sBveM4OZdrfKo2u+xyTczRoe+bhFZyrda8JPdlZl+Jqzu2RoTH0n/K+xRF4oo6nCf79FifdAaYPBMOL0f5/l+NwlSJzZL9LHtE+iX6Ep0uLsfweCno6UttUYP1uFqut6dcQyDmKLGEFmyXZ4GjDtqMQBnxiCwEzFFSrgJEYESlBd+/iW1+umlx3ha5p32zSV32hjiohmeL6obj62DCO/DVjd62LDqDxKGd84jESPlSdUoWimNfgvhsOZcJrcUCZK2A7MESkO+0ihA/4055v28h2q0fi9XLXhtcxy0yheP2aFzuCgBiw42E1Z6Uezq1izwPzVFQsBN2fIay9J9EXtqXnafCcbhUEiPMxJkcmI59j/nbW72u2Ygk+p33Cvefk80TS476faWqwtytp7hzRDvvxtoSqfYfiqLd2CoLuXP5CU6U1dM9I5aLemUwuHUiqw+WBL29os4hVdz/C1FVsDqaPja70/2TycS/BZpo+h/F5nRRVmsHVUr2v7/JwR1XLMaw5K/ojq0GnR63w4qS2R/lRAh3E4iQ2DlHJui6UkktD1XTSNHJyrFgp0wANQVikRj6F3lt6T9lYjKGyQPDWS8Pv5gMcZEpOlnpVxdAz8vkIWyJ9lqbmhJ2ICvLs/8uFiSdXoJXx78Nn1wc0HfOIg/6ZY9I/aWwOMlsU11SiXn5o17XQkSSHHuLXvL9lmg5xuo8cefsmC3uoPTu8v0nN4hQ6nKRWITyt8pE3v0SsVJZKyVeqmC7WHJyhoZuPgwSfHzFt17RlNJJinWCCJL2Y2QlrbdIH7y0HtBupNSmUt3iIjuyUtyOB5Z4J97csyS4ec3L8gBP7iCT5JYZYg368RWZJI3hsn3xAxIwe+33YC2XgOmoFOkNl9ZVLBlbZ0gRvoze4mo4vkYsFDtmy4SSM1SsBSMek5gpj7Wp7LC0NQlytTkliPiyr+Shn79F9heXLav+b++Wc+shuoWIho3v++8nJkMmzPAkuH613D+mSECBV6bKe8zR0ly5rlSOI3swXLkYDiyQJ3FqV7F6eoo6BuK5LsYwEW+7v5Z4o2H/kIVFfbm8Vn4ULnwdPr3M3ypoipD7ce4t3qr4R773WniX+7iJVFVcdckdxLV8ccNv0BwldcS++4f/sVUch0+mwDXL5Ptd9obYqAbBfHK9/FYCscR6LWW1xXIeQzHySYnJ870W2z6RmkOXzhHLi0eU/vCCFKJd8nf/fRjMXqtWKPSm0NZDvdH/PKZ2lpgx38WQ2ym/n9iW4s4LpP0Yybj0ZNd1mSiW75yh4no/vFyuT68rYdljoSv3L39cGm87rWJRrS6AsiOUnv8hhTVxgASId20Rg/HUChH99eXyu68rkWfrpA9g9QsYHdV8sr4Uq8PFkDZJjEwsxjTnMv9zU1uM5bMpXH7V97yyyhBU0f1kuf8CVLXX8FMOqbKSQpbvdWJzutmVV8Wnm07wwsU9yK+0cqjYv0hmarQFs+GXZ6PV213odQomw8+79fIr6rG73Oh1Chk+RTZPltXhUqXYamoIS5dRr2NirwyW7A5d7X9M19TfvTWMJpr+i3G73Zwor6es1o7V4SI52kJipImqeievrTjIl1vycLlVRnZO5cYzW7HH4SRy2BukW+yoKBgtYRgjk6Qytdsppn+nVf6G3CNCI3+ruCmiM2QSGfYPaWDbbYq3RYnLJvEHvm64o6vh2FhJn9abvYUA+98g4mraPKlLs+1jaWcy+G6ZfIv3SFq5wSwPzX43/LSrIecMmHuTBAdHJstkGd9agoB3fSn7S+3a0LhWFbcYyENdZwAUeH+kf62U2mL49i64ZI6syLtNBdxgiReLWmafhkDyo/LgH3qPxErNGO990NlrJcX81CaJ6/DEUdhqJHajqcnCaZNJ4ZoVcl5NEVB6RKwQKLJyttWIO2HI3TJZz5zov4+e00RIbv5IJm63Syab42slRmvNvyWwNSxOUv4dtRIIXFskVjVzlJyPjD7iCvJd7WcNhNFPw5IHYchfpJZM2TERRFV5coydLpRrChK70n2quESWPSzjzuwrrV4OLgmutWWJkXO69lX5b22xCOvivSIA+l4r8Rg1xWIN/fRysRK1HCCWhuSOUm5CMci5ismUc1h5QkTUqGekdpetWqwVsZkiLte+Ju6wIytlDAltJObmqxuCr5HeJBNvzlBxX5qj5BwntRfhOeY5ryixVkFSRznGfQskkDmls8THGMPl9xCT4S1/sHe+NKg+425wXy3vObpaRMm6N8Rq9+1dsu+oNDj3MbHeerL4PJzzsARYb3jLe46TO0h8YNYAr5j2EJcj8VWD7pDfUflROXeBVrKYDPnt+AomD7Yq+b6eV3i35W+DntNQu01F2faxzzk0Q86ZIlACURQR5ssf9d9ujpLfrGcRFZUm92ug9djDmhdFsO/7xv/424+RZ56HfQukdc9HF/hXka8phCMr5P8tsXLfWStF9Ce2QUWVfZ3cCLnDcLc6m7fWWRnbM4oPp/chv9JKr6w4lHqnxBT6LhL2zBPX+KQPQW9k5jqxkkXqHIze/3Lo54PbhX7969w97BYe/OZg42aLUcflA1qyK6+SfQXVJEWZ6Rcbheknqug7LEnYnKe8/3apPL1oL9eekcuS3YVEWvTsya/mUHEtL17UlhRXPmzfKKI6s58soMLiRFB6Av/D4iA8nryKelYdKGbetnyiwgxcMSCbNsmRxEcGR24VVtaz5UQFLy49wMGiGrLiI7jxrFb0zY5n5f5i3vz+MPmV9XRIi+auc9rSKT2axCh/V1u3zFi6tohm+yl/a258hInpg3NPS7T9mmii6b8Uh9PNvoIqDpfUYjHpcTjd1DtcuNxupr27wa+z+dxteRwtreG1KV1Rq2qxVlagKjosujjYPU8maJ1eJmO9SR7UZYfg3XO8P966MolxueQLQJV4hNoSWe1PeE+CLgNRVclYO+tvkgGmN8vE1rGzTAyHfMzHh5ZKwb/znpcA58KdDTV0Wklxyw/GBAfUZg+WydSTTeXpQWavlpTixLZeC9i+heKqqjopAbw1heLCSe8hK/41L4lJ3xghD8rtn8rE0uNSb3pyVKqUPYhtKaKpKl9ESUQKHF4V+kF3apMElUeliRC0Vcsk/lMYzCJsLLFyvgbeDLtW+Wf3FWyXIPPxb4q49XW7bv5AVr/9roPcM+Q8RqWJe2r2JV4rQH25WDDajBALnG8JhKR2Us090I12fA1U5sGIR8V9VX5EJriwOIk5WnCvt6K5KUIm/8pTYpUb+6JsN0fLfTHxfam9dWqT3H9tR4nVa/vnMOZZeRgrCljiRAD0uUosCHvny4R+9t9kAqsvhe+fhshUGfuUT6G2UB7wix+Qcab3lLiXiFaw+tnGOkIoCnS/TGo3xefA0Ptlm2IA3NJGZcXj3pi7iERxOR1YIpO655p3v0R+M0t96ih5OPN+iflL7SKBy+GJDfEyX0mc2ycNJT48bqU+V8l9X7xPzn+HseLi/e5BcRt6qM4Xy9yUWSLKPe7u3DNFAAfGEhXtgZkTpM6ar1iNyUCd+hlKyT4RjRXHxZLW4Xyx+n1ysVeYZPSRxsdNsW+hLHR8+fYulPFvow6+A2pLUPUmlPA4lMG3y30ceI8N+4c8l3x/T4oO99iX0K1/27stOi109XwPdWW4kzqgyx4MjjoqW42jKmcUyZRhjsn0xmv1uBR13Rsogb0v6yu8ddxMkZK9GZkkohcF5cML/MIA9MYw7p46h10uF/d8th2jQccZbRJ4vK852KoKIv5WPYv7Qm98X5s4BePehtinFr3EQowqv838bZiKd9GmrdfqY9QrvHpJT55auJcfD5c1br9pUCp3drsE/RZv0oAHR84wvjvhpkuLGBIjzRwrreVwSS0jWoUzPqOK8cVzMNflUzbwXEw5A4nKW44y+3Z/AdbjUonB/OIar5s1oy/OMc/z4MIavtvnvaYLdhRwaf+W3HlOG+IjvMKp3uHk250F/HOe1xp4qLgGvaLw6opDfLLeW9Ji+8lKpr23gecndWNs13QMPkIoJdrCm5f35psdBcxYewybw8WoLmlMG5hNVnyA+/R3QFHV39sj2HypqqoiJiaGyspKoqOj/1/7Ol5ay5GSWvJLKzknW48elSKbgS0lCgt3FvDksBjiqEHBTY0uGocplvgTizEsvt+bPh2ZIitPSzzs+lwmEk9dlvKjIi7OuFO2OW0ySeZv9c+Ii8+V1f/CvwQfpKKDy74UK8z6N2Ul2nWKCI6AFORGznlELESe4pqKAiMel4fH4gekQGV4vHxnUnuJRfAIBkWB61ZLI+Ah94gVxGH1ulBKD4plq8sEKdBYdlgeRhe8IlYoTxyDIUwm8a9vgWF/l8lJUaDsqATPr3tdJr/MviJAds+VGI3YbMAt32Uwyap1w7tiDXDWQ/F+WaGl94Rv7vJWpfbl7AfFXbjhbfneVmdL1toHY4LfC5I5qOiCU+5bDpJz+fbZ8u8Rj8P2T2SC6jlNzp2jDmy1cPwHmRx6Xi4CS3VLWvbOz2HUc2AOlwndaYP6SjCapXeg5z4CEZOzpvoHfHu46B0RxJvek/OY0kUKWlorpfRAUgdAlYm/pkjiqb5/Wu7H8AQRf63PgTeGBLtqz/67rPZtNSIUotJFoH97d+gsqYveltgz33pQkSniuiw5IG4bEBGU2k1cKdZKmRz1poY+e2mw6K/yvZHJMrlHpsg5CYyly+gjmXBf3egv+s1R8ttw2IKv7bAHodMEUB2AToT+kR+g0ziZfJc94v/+LhPkHtjekOhxwWvyW2mqcveUWbI4KNwuYwlPglMbggWfokg7msS2kjFbXyZWqh9ebCiNcb5YuXQGcW1vnSHndvLH3vpYAP2uQ41vhbL8cW+GY2IbGP084JbrfHKDCNIO50P5MdzZg3HvnochbxPOhLaUtp/KgfpousfZCd83B13xXlyth6OLSEBpqqRDeALu6YtxndqCTnVSYcnCHpnOlnILproiciPlXoqOiiZx5vDguLNzHpUFweIHRDB5GPmkBNIHdhEAiEjEeeUSvi+JwOpwk50QTvtD76ALtOz5UH/9RnYUWjHooMZlZMDxNzC2OVvcnHu/lWvb4TyIz8Vx6HsWZ9zGTZ/uQqfAfSPbcbysnpnrjvvtU69TmDk5m97H3saw9UOxNio6XB0v5Fivv4LTStyp5YRXHqQqpS/21F5EFW8i+tsbvTsxmOHimZLhGIpzn5DyK76B8KZIjkxcxLB3jxHQrYe5Nw+ia0Zs47+PlNQw9qUfqPEJQNfpYN7NgznvpdUh16BJkWY+v2EALROCF56qqlJSY8etqsSFGzH9By7Fn+J052/N0hTAq6++yjPPPEN+fj6dOnXihRde4Iwzzvjdj8PmdNErrh7T/jcwLZ8BjnpiMvrSYvRLTEywovPJfIpL7oh67uMo827230lNofwgLvlMLBseC8SBxQ0WlUwpMlm0WyaHy78WV5gvbqdMbKG47Et5kPvG76R2Cd313cP2WRJU6xFNqirByFcvE9dSt4tFCHkmT53vLarIGEY/LbE6G9+VB2FYnIisrpMb3H8PyoSS2lWsZE67CK2Di+X74nOlFs+4lyT7avdXsspqN1rih3pdIVaGZQ+L9azLRFnd7/pKJnvP5BiXLYJh9Yuwd673MCOTZeL69l7/7Ja+14ogeWe4d5vB7A1wDcWeeWLBCRRNtcUy6XmIzRAXzJB7xW2x4gmxdvSeLtaVwh3iivIEgve7Xh6YR36AVU9LAG18rvTvyz/sL5jSukuh0FCCCaRu1phnZayqW/Y/7lX46EL/2J2YDKmI/v4o76q28qRcm8OrxLU2/zb/85jeQwSQp9muMQwmzQgtmEAsXOPf9t82bR58e4+/q+jYGnGdzJzQOOE0/j5iMuTcfHZFQ59Bk4jXC9+AOVf7T74DbpKipIFWUlu1FGCdEnDdEtpI/aGaAok9steIO6TzhQ2CNcTKuWiviBcPxvCfbnVSsEPcxzs/F2vKtHnBBTJBfgvf3i1Cc85VYhXLGij3acdxsriZe4ssNrKHyLUrO+Jf9DI2C9J7ogRm6ZYcgNlT5PdxeDkkdxJx+uV14LShXLMcQ/UpiMvGYK8itmgj/dsNR7d3KbqS/aA3oj+0DPWMuyQGMZSLru+16Oy16L68Wk4toKb3Im3Qy1z45cnGt82YZGJwqPNUUyhuUV/BBPJcDCWYAGpL0NUWce1HZThdKh3To5nbJ4wmHUSKDrO1iL7zxoPLhjPnbPSjnxKLsG8D6bzNkNoF/YT3KD0Ar17SU0rmhZt4bklwwoLLrXLJrKPMuuJOYttcicFZi10fRlhsGtEl20n8akrjMzSJ90WwXviGf1uX3LO8fTpDsfkDKUHiG1NnryHpwGec2WYky/b734NfbTnlJ5rKax1+ggkgNyGCg0U1TUYvFNfYqKwPEesGKIryX1FPShNNPsyePZvbb7+dV199lUGDBvHGG28watQodu/eTVZW1u96LClKBeGfT0bno/KV/K1E6BxSsdl3Rd52JEqownsg7zu0TH4gDXVR6HmFxID4puDWFksgaaCPXGeQlains7uHsHiZwAIDnhVd6ADPxuNxSM+vQDZ/INaEXXO829J7Sl+3+XfIv/UmWcGvfEoedh7qy2WbrVosB54JxZM9NGO8TM4eyg5LWYJJH8kq2DOuQ0ul5tCsKV5XiNMqq60j38PE9/zrvJQflUn3/Jf8RVNNkQSvTpkllruK4yJIUrvAm2c2fW5C0kS4Z/ZgKPERWwaLpPp/drnX7WGtFDfU0VXeOBmQyWLpP0VAdTjf25Ou7LBMnmfeL/WP9i+S7bGZTWdagbxWXeitb3Vivbg4pnwC74/2Hs85j4jbNlQcxsHFEjt23gsigu01cq0XP+A/ucTnQtGupo+l9KB/UdX258ukGBhb022yxOF5rr1vtl3lSclcNDQ8oF12Een528WVM/922e4pNxCqKrXnWAKz/i6eIdbPxX/1Pw/tRonlIytEJmlcjgTqe1BdTWeQgrj+Di2XcZiixEIXqo8jyL2qN3tjHfcvkDT/Tyb7L36OrIQTa1Gv+g5l/p3e7d0vkVisUNiq5Zy5nN6eeooOxr2GsvxR7/0FmM1RqKu3owSUo1AKtst99NUN3mxHvVGsqRGJgH9mlZK3idy8eQzMPYM1h+X87K0wMKjj+cEV1DP7yP0eSFMthjzf4awjwqSnst7JvoIq1OwhTb+53Sh0FccbreUGe6W0CfK9pz0U7MB1bD178rvwyXpJHnhnWm9sTWS2udwqPxyvY8GOcvYVyiJn2dVmEudfGWyxrS2B5Y9J2Ynlj8m2sNjQRUA91BRKCEEAkXmr6ZE2lmUBj4Q6u//v2qgPfnbV2lzB9aYCMP6X1ovy8N99dL8zzz33HFdddRVXX301HTp04IUXXiAzM5PXXnvtdz8WY+leP8EEiCtt80fBP4iMPlLgrSkKdsp7PPSaFhwoiuKdJPxQRVCd+4S/ZePsv0vmXSDHfoA25zZ9LG1HSuBrINV50LK//7a8zeJWCouTfzutImaaqky94W0p6OlhyH0ST+MrmBqHpUrmj2/vuc4TGlKsQ5RKqDgmk6+nPo2H+nJxs8W2DBhPvlgpVj8vk+TG96T2jxrwADyxToKmm6LDeVIzxhdjuBSrdPocp9MmGU+BS7iOY4MDbj1s+0SsKrqAh9ial2Qy9FCVJxN3U8TlNGSv+VC4U1w616+ROkXnvyxxNKECjD0cXin3xmfTxFpYclD6zPnWB9MZvfdDKHR6//ef9RepKxVISpeGgq1NcHytHK8vBdtFyHkmEp0+uLp9IL5iJedMCcpfeF+wcNy3QGK5IlOD99HnKnHRXTJHLMQteolLMBQRieKa9VT7N4Y1xG/9BL7nK7alVKEPZS122mDl09jPf4Xq/ndR1f9uXB0vFOtuUxTtkuKqPadJ/Nu1KyRBwEcwAdDmHJQQ8TmUHYa5t8IFb4hbcOIHsthxWiW+TgmewmJ2fMBlXbwWu9woF0rniWJlCaQpMekpDhqITg9R6Y1p8G1TorCVHpekjUAiUyQUwuLj6sk9q+meeYBx24fcPNB7nLHhxp+0rvTPSaBbZgwd0qI4v1saGRQ1XTbl1Gb/e7p4nyTnNEWLXiEtbs7wVIrrg01Fo7qk+f07NtxIdoK/5TS/ykpajIUIU2jXWpcWMcSGGZs+pv8CNNHUgN1uZ9OmTYwYMcJv+4gRI1izZk3Iz9hsNqqqqvz+fi0MBxcFb0xoIw/uQExhkmrcFIlt/H+4bmdw521UecAGTv4o4p47/qO0Oeh3vWRPtRkeekVWuEssE0ntg1+LyRALiSdjxZesAaFX7Kc2evdlCJNVc6Dw8OCy+7tJkto17cYBEVS+x5neXawyTbHvW6kxFUjZYQlaDaSuVCwN+dukF1yo0grWSrFEtRsd/FpksqT6+7omMvuKxWvd68H9qXyz4Dw4rE1bJECsRHHZ/tsC3XCnNsn3GpooINfvOlBD3As7PpUH86pnvSnpgQLNF4PJa1irKxX36KFl0N8nDsPtlAy6pvbTdhTYfQpQ6k2hK8jbqpp2O0NDo9wQ7sjCXd4CrY56Cfpu6ljC4sRF6qH3ld66T6HY9B44fSxTxjDpgWiJlkKxH0+Q/xbtkXH2uMy/tEB8LlzkCTr2JHiUSIJDoKj1/Yxvgc607v4JHAEoh5fj0oeR3/0OCnrcTp0uKsQzw4fYliJaK0+Kta/sSHBtJXN0Q9X5Jnw2RbvFMj5rqgjqTyaL5a/dGP+WOx7sNYQZvQu8MAOw8F7J8Bx4q1h8s/qLqIkN4UHY8pEkDoSi5zSw10pDYsQq4szbIVa1i2dI1nGbERK3dt7zYiUPdN0qwRYYnxcx+wRBWx1urh4cesHSLiUKRScuvOEdUmgRG9a0C92D77HkbRGLcERi8Pt0ehHmgWEBQEnXa/l6l/8zpV9OPE6X/3M5Mz6CFy7uTmSAZemzDcd5YXJ3DAFVvOPCjTx1UdffvS3KL0UTTQ2UlJTgcrlISUnx256SkkJBQUHIzzzxxBPExMQ0/mVmZv5qx+MKD6whi7hAQvUwKjnQUNU6BHpjQwzRFp9tIepaqKqs4kc/47+Sd1rle1O7itvKs/LYs0ACZUMx/w6JcRn2kIi5uBzof5NUpf7m7uCHY3hCg389xAosJkuEWs9pcMZd4m74KfQ+q5QT66XSeVNEJPsLNUe9PMCbwhIXWhwktA7dxy8m0ytYyg5LfE4olj4sQvSid0RUpnWXh/a4V2SSyR0qq+tJH8l5mnebpLr7ttJQXaGvq/5nVm1hsaHjYwKFwKpnxd3ou1rXm+Q4nXbJoAtEVcVFVVMo50HRhRaHIBNJy0FQEOB62zlHxu+ZaOpKRfCP/lewlSEuR6wyvitttzv0d+76wt+aFkjbc0ML7vhc6aF4xl3iSozPlQbNoRj2kMQUNaKKBbIpaopE60x4V+6F8W+J0Nj0vozB87vZ8JbcT10mSrzixA/k2ox8AhQdqsvhXRQpCq7aUtTRTwefL2OY/N59M78cdf5CLxBLDAeK6xjxwveMeP57dlWaqBlwT+j36k2QPQj2zhPX96lNoe9Hl61pUde4r4D7MSYDzvyLVJAPwJZzDiuOeRco3x+344htLaU7CraLtTujj1iaB98Z9HkOfifPgfFvyYIT5Lc8/J+Q1A5VVSmrEwvVgcIaajPOEBflF9fKIikiSUqFzJoqSTG+ru1DSxuK44bG2m0a+fXe33G11Um9w8V9I9sRFy7nTqfAOR1TuH90ew4U1jB/Rz4vLTvIBz8ewxEbYn7wEB4fvIBY8hDqpV9KcoqHpPayQK4u8H/GKAq1Z/wNe1wbRnRKIT3GQpvkSO45tx2T+2ax7UQFgXROj+HrmwfxtzEdGNctnbtHtOXSAdn0aRnPgtvO4LZhbRjXLZ1HxnXiixsH0TH9/5dA9XugxTQFoASsAlRVDdrm4f777+fOO70/uqqqql9NOOk6XQCrAoI3Vz0jwmPbx/7CY8tMKdY4/B/SisBTwC4iUdxqLqcEf3uozkfNGoAS6J449gMU7pUYneqCxnolxGbI5y+eIYGD1QVgKxMLRZsR/vv2YAyXlP0el0oM0+EVsPxJuOBVycQr3CkTYc5QSW21VgW7bhRFfsyzL5PqvnlbRFzEZYdu/5Da1b/K8/dPwTXLZcIPZZ3qeblYQzzs+lJiXXyDl33pfomsWH0JjxfxFdgRPvsMifs6/yVZZcdli/uz57TgNiKezuab3pMxGMPEohGVLpPsDy8EH8uFb8Ayn+M8uUkm0cAYM0/phcCeYSCTY3iiZE35ktReVqSNFdST5EHvqJfvddSJW8NgkRY4elNol2vni2TC9LBvgQiMvC3ego8ezn5QrlEoV09VnrjEPK5Qt1MCuad+KhbQ2mKJfzNHwZHVUhBx8kw59up8iRfaMsM/ff3kRrE6tDlX6pL5MvwfcOC74AlGbxLhO+NCyUqrLRZ3+fg3Je7vx5el6GhyBxFVie3EzTflExGW8bnS0DWw7ZCHjD4ibL683r/1UEpnGYPn1BxcKm7o4z/KAicyRa7N8bXQZgTWyEwcF3yAaqtBMYVz1BpJi+QM4i/7CmXXl+JqTu6I2mEsLmMkBt8G2EdWimgL1eoGcPW5jiJ3DL1bxkmZLbsLW8ZAlAF3E7HuBa8lIywO+4Xvone70Ecky32o0+Ny2NGndvEPvnbaALXpOK207qiJbaHfjVCTj9p6BEpWf1h4P4o14P2mCNxD7mHlTK84XXq4ltsufgDjkaXyHPIRw2paT5j4Psp3/5T7w2CB7lNRkzugLPm7JIXEZMq13jITrBVUjB+G2y2xQPUOF6uLLYxvMwrjgQXSKNuDziD3wYL7vNtObYZBd6Amd0IJiM1zp/fE1XIQX/x4EqNeIdxkwO50sbegiso6J38/ryMWox69TmH1gRJum7WF5yZ1byx8WWd3cdIRSVa3aYRtC75+ruGPUOfUERWbBXVlODIHUtH/PmLCEjH1u0FqirldgE7u54w+spA5/iMoetTMfmzI03HNq1s5t1MqV52Rg9XhZsHOfHaeqmLR7cGxXQaDjlZJkbRKChbFsREm7jjnZxbB/4VooqmBxMRE9Hp9kFWpqKgoyPrkwWw2Yzb/NtH87shUbKOex7zgDu/G+nLcpYdRLnwDZf4d3iDTot2okYlwqgZl/JsNfdp04KhDrS2BFr1Qul8iK6iwOFSdUUzHgcXeFj8A130v/buOr5EJdd+ChnpAQ+DzqyX+JjZLHjwdxknNldbDJc7IVi0ip+tkmWgdddL3zJOZNvRemTz63ygTnKKIlSMmw//BAvLa+a+I9an8sPwBlBySrKcZ4/0r+Uani2Xmk4u925w2KNwjKdpzb/aPX2g3WgTb9894t53a1NAk+MxgK0P/GyE8zj/WI6G11CI6utpbJFBvhE7jZRW89GEJIA9PEAtJmxES15HWTSbx2iKJGxhwE6x/W9o7xLQQkZvWTdqrRKX512nSG8X0H5stffNcdhGlih6S20lqt+/KdtED0kz0owv8XbIGs1gofNuCgExcF74Oe76R1z2NUI+ukfPy9jkSS+XJNmszUsbgex5BHrrtRnn7gxnD5TORyWIlKj8ilsDweLl/dAaJ/wpFRJLcN57xF+yUe/Lji0VMW2Ik8DmlkwRqlx0WC429Vix3cTkNY5orol9VG8oJpEp6ec/L5FiMYTJRhCfCNwEWCL1JrD8n1sl59JzLujJxgbU8QwSV2yVjMUWKBSWprbhnawplW2Y/uaaBFidFJ+UvVr8Q5MZVI1NQAsSEWlOEktxJXJqOOqkin9QWl9vN06tKmbmpiJhwI9VWB1FmIx9d1Qd3RBhR/W8HpxXVGEaNGoaqulDHf0HC9rcw1BdRlXEWuph2hJ/1d3TL/UsfqC0Hkpc1lu82FnHTWa0AWLCzgDZnt6aw/dW06DIZd8UJFKMFNTKNQ/URHC6pJ/Wsz0gxO3EqRpYed3PN2DcJ+3is/+937RuoEz9E+Xiif5xYRCLquFcoMmWiG/wQCipOt4rLpWI+80nMcW2J2jkD7HXYW51LZf970Edm8fblGZTU2tChEB9hwh6hw3LtCvlNHlwi16LXldi7TmFvmYuOk2eht9egGixUEE2c0Y7iqJffj4foFqiXfMaT33vdv4oC6woVzh/1HM7sswjb+CrUleHMHABnP4CuYCe6gJhKdevHOCfNxH34e8zbPwJFh7X7NOpaDKbMFUvPLD0TembicLlZe7iEO4a35eoPN3Lnp9u8t6NO4ZWpPThWWotRr+BwqegU+GpPNVcPvBdbQgdiN/5bFhzJHSkd8AC6lv0osZs5NKIzesWNyxhJcmISptgwiEoWYaiqEB7rzeIMi220tilAB6OVzi3ymb9d/jz8/bwOpMU24b5vZmh1mnzo168fvXr14tVXvUXjOnbsyLhx43jiiRApuwH8mnWaAKy1Vehq8nHvXSCNHducg5rQGktENO6qApTKE+B2oca2RBeZjMtpRVd2SISG0QIdL8Aans7cfXV0ineRHenGgZ55B2wM65hBOgUyyR5fK4X/2o5EObVFgp2d9TLpmCJlX6oiRSV3zoHK4yKOWg5GtVWhmKIaYnYcIhCslVIfyFkv8U0oMslsfh+GPywmX2ulWDLC48AQIT/Y2iKZ/CKTpX6RKUImodKD0kYkviW0aKidpNPJ9pL9EuOS0FoewpYYsRxUnJBq4imdxfoU11LGaq2UeAYQS1B9hUymbqdYU6LSZP+lByRY1WiRDLPIZNCZwV4lE6XR0mD9qIbIhAbrW5Ucc2QyGKMkPqW+QsYbFt/g9lDkvfWl8j1R6VI6IVQQLIgrYcSjItZUt4zT0ODzt1f7nMcEyYJy1Mo5ObRcLBAdzpNAYJ1e4sNOrpfYuFZnixipK5EA7OI9Yq3J7C/9+GxlYr2qzhNRFJcrblpLlFgkrVWQM1gm+/BE+b6tMxqaME8WIVRb6q3WrejF0rJtlrjb0Ml9ZIoQgbXt42ABByKqrlrS4I6sFFEXFgdVhRAZD+XH5d5JaifXw14nT/eyIyJa4nJEDIEcm8fC5WkhojdKra3ywzKWloPleqtOEV8n1oklseUgic/SG+X7CrbLvpM7iCVz9L/kunpiiGIyQB8GblvDwkQnLqbIFLlPlv5TLLSqKpa9kU/KWN86OygGRr38a1SdCWXP1+Ke7jgOqyURnepCV7wb3eEVqFFpqG1HYrUkUe4wcqqingNFNWTGhZOdGE5mXDj5lVYq6+3U291EmPXERZgIN+qosjpx2qw4HTYs4VHo9XrSLXaozsO962uwVqF0OA8lsTVlSiwny+v5Zns+OkVhdJdUWsSFU2N1kF9p5VhJLXq9jg5pUcRHmLA63ewrqGb1gWLSYsI4p2MKqdFmzHX5qCc2oMvbiDuxA0ruENTwRIy1+SgHv5PfbEYf1Mx+6BJbhfxpVNU7qK6rx15VhKK60YXFEh4ZXFHaD1u13EuKIve/3sjRklr2FFSy61QVseFGhrRNJjnSRKyjELXypPyeYluixGVDfA5ltXZKa23U2pzEhplIiDShUxSsNgdmeymK6sauD8dtjCTBYIXqPNRdX4GtBqXDWEhoRb4zkmqrE1ddBYoCurBYosOMhJsMlNTY2FdQTYTZQG5SBMnRZkpr7OzKq2L1gRIy4sIY1iGFtBgLblWlpNpOtc1BhMlAYpQJs15PSY0VZ2UBiupE1ZsxxiSTGi2/A6fLjcPtJsz4n9lMiqqtHC6qZcmeQmLCjIzqnEpqjIUoy393APfPcbrztyaafJg9ezaXXXYZr7/+OgMGDODNN9/krbfeYteuXbRs+RPBjg382qLp18LudFFUbaO8zoHZoCMhwkRCQ8l7u9PV4OlTMdcXSw2hypMyGUSlyyRTsh+S2uMe+zK66FRZhVQcx1GZhz6tK4q9GqW6CFQHakSSiAPVjVKws6GNihO6TELN6I0yc5IImQE3gN0KS/8hAmT8W2JNcFllgtWbJO5k2SMyaWf0hYqjYvnKGSLxBfUNJRJ0OkAvK6S930hBwbBYeX/r4SKqTm6SeCy9Xiw3rYfD989KUUiTWWJJ7HWAKkU/I1NFPKpucSUcWS6tODL6iLWtrqyhk7wK0xeLlapFbxECR1dLxe7oDIhIk+KR1ioRloeWyvnIGSqfNUXBnq8lWywUiW0kvsAc22CZK5bj63iBFOyMTJb9VOXJxG+JlQk9PF6CwC0xMtErOohqISK2vlwa/u78Qtp91Fc19LRbIedbUaRSdGSqWASPrZH9Zw+SQPm6UhFibkdDrI0bVv1L0pkVRTIFC7bDtPkNDW0jRRw46iSQt2CHHHdyJxGUlSfF7ff1Tf4WGGO4xOos/ptUoU7tIj0Mlz0ibuoVT8rxxbYUl9XopyWDsP3ohixHRfZdfkzqY/3wb7EYKjoJpO9xiRz/4e/Fkup2w5qXcXS+iNr4LsSYdQ2B6TrcqgPdhrdRvn9GUvpjs0XkF+yA9mNQe09Hydva0Kh2K2p8Lkr2IFjxHIx4SIRuwU4pMtnhPPmsopdzp7pRI1NQ9y1At/ivXte7okiNrb7XyuIiBG63itPtRofiV0VZQ0Pjl6GJpv+QV199laeffpr8/Hw6d+7M888/z5AhP1GHw4f/VtH0i8jbKl3ifYNpw+Lgim9wxOagVheBy4ZqjMAc18Ibe+G0IzVTdByt0bFgyyGu7J2ASbWBquLQWVi4v4aRrcwYds5Gv2sOGCy4el8NSR3QH17mDZBUAIdd0hSWPyYtLTwktUO94DUUc4yIjcpTYkVqNxoVHUp9mYgTpSEeJyZTiuwpOmmnYY4Qy8re+ahtz4VRT4t1TXWhpnQCgwW1YAe6+NyGKtFGER5ulxQC9LTnAHFxXfiGpGindREXndspMSslB0SQ4RZrirUSivZBqzOlLtKur2Ti7DZF4p0+utAbi+bLmGeh9QgpwKm6oNUwnKYoXMZITG4rir0WVacDcxyKrUI6tJcdEVedrUYsVR80nNfMfjLh1xSJ+9HtlPiVr2/yL7PQ4XzUcx6G/G0o1QWo6d0hMhW7W8HkqESpygNU1IhklOp8KYq4e65Y7FQ3tB8L/a+XQNioNBG5TqsIhxY9JfspMMas04XSNqWgoVRBYms53i0z5PP7F4pLL6mDCL2dX0gsnNsp+zZHQm0ZamQy/Piy9EFz1KPmDEE5424RkdZK2YeKWFb1FsjbjCv3LNwlB1CM4bhis7l7QRHzdhXTMiGc9qnRVFsd7M2vYtV1rYjY86kE/Xoa9na9GAbeiis2G6XiKIrLgWowowuLRZ1zDUrPy8Sd7bSJWN/1Fa7cM6mJak2EzobidmLThVGhxJIcpmKoL2now6iKQI9IEeuehobGb4ommv4AmoVocje4MI7/KBNYeneZvGIyfiZV1ovLrbI7v5IHvtxJfkU9OkUhJtzIoxd0oXNaJLV2h8RoKHrUsDiinOUoS/+Beddsb1NhUzjOy75F76hEcdTJRG+JFatUcidw23Hb6sBRi2qMQG+OlOw/a4UIHKdVxIpOj/rFtSgn1/sfpDEMrv4Oe2xr3HUVgIpiicEcFo6rrgK1tlgK8xkt6JLaoZt5kcTLRCRJ/aiIJHFzOepQ176O4hvwnNIZdcJ7KPnbJOakptAbOJw9VKxo1XkiTD1B7S6bVK32ZOEZzND/RtTYbCozzsLukp9plV0lIj6VtJgwSmpsuO31KDodkXoHllPrUCKTZf91ZTJGvUFcPk1x/svSZ9DTH6zNOQ3Bry0hvpW4qYxhoDdSWmOjvLKaKF0dekXBrRhJWnEPyr5vJX6p9XBAEUGmM0iF9+/+4a2TNWW2uOe6ToA1L4vrKzxeAuzTe4o17sAScNvFYnfmX8VC+ONLIpQiUyWI+egq1CmzJY5ty0yU6nzU3LNwZw+mNjwTXA7CbFKawmWKxrT7c3TLH5Oq3q0aamIdXi5xeP1ulFi7huwsVVWZs/kUd3+2jUC6tIhmzuXtMNlKvT0Gw+JDl5sAEfQrnpQq+C67iKcRj0LumVj1kZTW2lFVlUizxQ8AigAAFBRJREFUgdjwEJmPGhoavxuaaPoDaBai6VekuNpKRZ0Dt6oSG24kJbrp+hs1lSXo60pxFh9ECYtBH5cF0WmEmYyShee0itUnMuXn0+jrq8TyZYmQCb/ipPRl2/iuTHathkmgdkKb4FTmUKiquOLm3y4uo7iWYsWpbAjUnr4EnPUSdG+JQolMbYi7OSWB4GWHxELScoBYvgLFZ+VJSVfuNkXcMG6XjHH7p1JuIas/lfUOVFX9yclVzd+Osv4tyVrTG5CCpRb4aJx/WxRfrlosVhOnTYTa8bWSETd9cZPlGmwOF063KpV9T26At4eH3ve1KyS+p75CRJTqglf6iWuux+USuG2vkdpFR1ZKP8GqPMkAVJSGiurbRaBufEfEZVJ7sUiVHBDLny9RaXDlt8FlOSqOi3AMbMURHi/ZlQF1qspr7czdlsezS/ZRVS/xRQNbJfDkRV1/eYNQe724VF12EVlRTQgsDQ2NPxRNNP0BaKLpvxi3q6EwpipuNPPP1IUJpLZEKo6vfs6bCp7UDiZ+CMkhCnn+ApwuN86SQxgX/wX94aVyjHE51A5/ErL6ExEVe5o7ckD5EdRVz6Ic/V6sIOc+JhbDxQ8Ev7/DWBnD9//yblMUmPyJpOLrTiNGxloplcUX+bQFUXRSQqD3Ff41v8qOSAugpnoTXvK5FBbc/TXEt0Yd8TDKrKniUuw2VZohlx+TeKUZF4UuI9FutJQACKzoXHZEWu3snAOo0PHCBuEcOsjY6XJTWG2jqt6BxaAjPtJETJhmDdLQaK5ooukPQBNNzRyHVfrZebLnwhNDt2b4hRwvreWi19ZwSY94zs0xoHM7OVKj5/kfq7jprNac3z3953fii71OxIzeKLW66sqkdcPyxySTyxwFfa6VQpBVeVIuoOwgpHSFIXeKW84U8Qu+r1bcp4U7xb2b1kUyzgKFqcshcU+fTw/eR0onaZLrSdXXGXDXlqF7fYD/+xJaSe9ET4XxQBQd3LoluMo5SMxYXUPqfljsLxujhoZGs+Z052+tTpOGxulitMhkHGpC/n+wdG8RxTV2XlhVwAsBXVxe+G4/A1rFk/RTadSBmMLlz0N4vLTcaD1cAr4NJokP0hvFBTfhHdluivjPhIQpQgKr40O3e2hEb5SSBhe+IbE+5Uck/qrjOKmyHZPp5y7V6fSoLXqh+LbK8JRVaArVHbohMIhrNeYXutc0NDQ0fNBEk4bGH8yuU1VNvna0tBan61cwBnuqjofCHNV0g9Jfm5gW0jetRW8JhDeYvVWtffuoAUQkoUz8APWLa7zV68sOifBqiuSOP90KR0NDQ+P/gSaaNDT+YHq2jOXzzSdDvtYmOQpjc6u/ExYjf6dDbCbK5JkSU2arlvIPpkipur7rC//3Kjop0RAZom+jhoaGxq+AJpo0NP5gzmiTRJTZQLUtuBnwvSPbkRj527Tq+Z8hPEH+fBn5pLRIWfNvyYrL6AfDH5LAdg0NDY3fCC0Q/FdECwTX+E9wu1X2FFRx08zNHC2VApcRJj33jWrP+d3StRo+P0V1oZQyMEacvvVKQ0NDIwAtEFxD438EnU6hU3oMn14/gLJaOw6nm/gIE8nRZox6/c/v4M9MVOhm2hoaGhq/BZpo0tD4LyE5ykLyL8mS09DQ0ND4XWlmEaYaGhoaGhoaGr8NmmjS0NDQ0NDQ0DgNNNGkoaGhoaGhoXEaaKJJQ0NDQ0NDQ+M00ALBNTQ0fn8cdVDv0x9PQ0ND438ATTRpaGj8fjgd0nNu9XNwZKUUrRx0G+QM/VWaH2toaGj8lmiiSUND4/ejeA+8MxycNvl3VR7MuRq6XAyjnpTmwhoaGhr/pWgxTRoaGr8PdWXw7d1eweTLjtlQder3PyYNDQ2NX4AmmjQ0NH4fbFVwYl3Trx9c+vsdi4aGhsZ/gCaaNDQ0ficUUH7ikWPUqqFraGj8d6OJJg0Njd+H8HhoO7Lp11ud/fsdi4aGhsZ/gCaaNDQ0fh/MUTDi0dAlBs7+O0Sm/v7HpKGhofEL0LLnNDQ0fj8SWsE1y2Hft7BvIUSmQN9rZbsl+o8+Og0NDY2fRBNNGhoavy+xWdD3OuhxGehNUuBSQ0ND438ATTRpaGj8/igKmCL+6KPQ0NDQ+EVoMU0aGhoaGhoaGqeBJpo0NDQ0NDQ0NE4DTTRpaGhoaGhoaJwGmmjS0NDQ0NDQ0DgNNNGkoaGhoaGhoXEaaKJJQ0NDQ0NDQ+M00ESThoaGhoaGhsZpoIkmDQ0NDQ0NDY3TQBNNGhoaGhoaGhqngSaaNDQ0NDQ0NDROA62Nyq+IqqoAVFVV/cFHoqGhoaGhoXG6eOZtzzzeFJpo+hWprq4GIDMz8w8+Eg0NDQ0NDY1fSnV1NTExMU2+rqg/J6s0Thu3201eXh5RUVEoivKHHUdVVRWZmZmcOHGC6OjoP+w4fmv+LOMEbazNkT/LOOHPM9Y/yzih+Y1VVVWqq6tJT09Hp2s6ckmzNP2K6HQ6MjIy/ujDaCQ6OrpZ3Mw/x59lnKCNtTnyZxkn/HnG+mcZJzSvsf6UhcmDFgiuoaGhoaGhoXEaaKJJQ0NDQ0NDQ+M00ERTM8RsNvPQQw9hNpv/6EP5TfmzjBO0sTZH/izjhD/PWP8s44Q/11h90QLBNTQ0NDQ0NDROA83SpKGhoaGhoaFxGmiiSUNDQ0NDQ0PjNNBEk4aGhoaGhobGaaCJJg0NDQ0NDQ2N00ATTf+jPPHEE/Tp04eoqCiSk5O54IIL2Ldvn997VFXlH//4B+np6YSFhXHmmWeya9euP+iI/3Nee+01unbt2lhEbcCAASxYsKDx9eYyzkCeeOIJFEXh9ttvb9zWXMb6j3/8A0VR/P5SU1MbX28u4wQ4deoUl156KQkJCYSHh9O9e3c2bdrU+HpzGWt2dnbQNVUUhZtuugloPuN0Op387W9/Iycnh7CwMHJzc3n44Ydxu92N72kuYwVpK3L77bfTsmVLwsLCGDhwIBs2bGh8vTmN9bRQNf4nOffcc9X33ntP3blzp7p161Z1zJgxalZWllpTU9P4nieffFKNiopS58yZo+7YsUO9+OKL1bS0NLWqquoPPPJfzty5c9VvvvlG3bdvn7pv3z71r3/9q2o0GtWdO3eqqtp8xunL+vXr1ezsbLVr167qbbfd1ri9uYz1oYceUjt16qTm5+c3/hUVFTW+3lzGWVZWprZs2VK94oor1HXr1qlHjhxRv/vuO/XgwYON72kuYy0qKvK7nkuWLFEBdfny5aqqNp9xPvroo2pCQoI6f/589ciRI+pnn32mRkZGqi+88ELje5rLWFVVVSdNmqR27NhRXblypXrgwAH1oYceUqOjo9WTJ0+qqtq8xno6aKKpmVBUVKQC6sqVK1VVVVW3262mpqaqTz75ZON7rFarGhMTo77++ut/1GH+asTFxalvv/12sxxndXW12qZNG3XJkiXq0KFDG0VTcxrrQw89pHbr1i3ka81pnPfdd586ePDgJl9vTmMN5LbbblNbtWqlut3uZjXOMWPGqNOnT/fbNn78ePXSSy9VVbV5XdO6ujpVr9er8+fP99verVs39YEHHmhWYz1dNPdcM6GyshKA+Ph4AI4cOUJBQQEjRoxofI/ZbGbo0KGsWbPmDznGXwOXy8WsWbOora1lwIABzXKcN910E2PGjGH48OF+25vbWA8cOEB6ejo5OTlMnjyZw4cPA81rnHPnzqV3795MnDiR5ORkevTowVtvvdX4enMaqy92u50ZM2Ywffp0FEVpVuMcPHgwS5cuZf/+/QBs27aN1atXM3r0aKB5XVOn04nL5cJisfhtDwsLY/Xq1c1qrKeLJpqaAaqqcueddzJ48GA6d+4MQEFBAQApKSl+701JSWl87X+JHTt2EBkZidls5vrrr+fLL7+kY8eOzW6cs2bNYvPmzTzxxBNBrzWnsfbr148PP/yQRYsW8dZbb1FQUMDAgQMpLS1tVuM8fPgwr732Gm3atGHRokVcf/313HrrrXz44YdA87qmvnz11VdUVFRwxRVXAM1rnPfddx9Tpkyhffv2GI1GevTowe23386UKVOA5jXWqKgoBgwYwCOPPEJeXh4ul4sZM2awbt068vPzm9VYTxfDH30AGv9/br75ZrZv387q1auDXlMUxe/fqqoGbftfoF27dmzdupWKigrmzJnDtGnTWLlyZePrzWGcJ06c4LbbbmPx4sVBKztfmsNYR40a1fj/Xbp0YcCAAbRq1YoPPviA/v37A81jnG63m969e/P4448D0KNHD3bt2sVrr73G5Zdf3vi+5jBWX9555x1GjRpFenq63/bmMM7Zs2czY8YMPv74Yzp16sTWrVu5/fbbSU9PZ9q0aY3vaw5jBfjoo4+YPn06LVq0QK/X07NnT6ZOncrmzZsb39Ncxno6aJam/3FuueUW5s6dy/Lly8nIyGjc7slEClT7RUVFQauC/wVMJhOtW7emd+/ePPHEE3Tr1o0XX3yxWY1z06ZNFBUV0atXLwwGAwaDgZUrV/Lvf/8bg8HQOJ7mMNZAIiIi6NKlCwcOHGhW1zQtLY2OHTv6bevQoQPHjx8Hmt/vFODYsWN89913XH311Y3bmtM477nnHv7yl78wefJkunTpwmWXXcYdd9zRaB1uTmMFaNWqFStXrqSmpoYTJ06wfv16HA4HOTk5zW6sp4Mmmv5HUVWVm2++mS+++IJly5aRk5Pj97rnhl6yZEnjNrvdzsqVKxk4cODvfbi/OqqqYrPZmtU4hw0bxo4dO9i6dWvjX+/evbnkkkvYunUrubm5zWasgdhsNvbs2UNaWlqzuqaDBg0KKgWyf/9+WrZsCTTP3+l7771HcnIyY8aMadzWnMZZV1eHTuc/der1+saSA81prL5ERESQlpZGeXk5ixYtYty4cc12rD/JHxSArvH/5IYbblBjYmLUFStW+KX51tXVNb7nySefVGNiYtQvvvhC3bFjhzplypT/yVTQ+++/X/3+++/VI0eOqNu3b1f/+te/qjqdTl28eLGqqs1nnKHwzZ5T1eYz1rvuuktdsWKFevjwYXXt2rXqeeedp0ZFRalHjx5VVbX5jHP9+vWqwWBQH3vsMfXAgQPqzJkz1fDwcHXGjBmN72kuY1VVVXW5XGpWVpZ63333Bb3WXMY5bdo0tUWLFo0lB7744gs1MTFRvffeexvf01zGqqqqunDhQnXBggXq4cOH1cWLF6vdunVT+/btq9rtdlVVm9dYTwdNNP2PAoT8e++99xrf43a71YceekhNTU1VzWazOmTIEHXHjh1/3EH/h0yfPl1t2bKlajKZ1KSkJHXYsGGNgklVm884QxEomprLWD21XIxGo5qenq6OHz9e3bVrV+PrzWWcqqqq8+bNUzt37qyazWa1ffv26ptvvun3enMa66JFi1RA3bdvX9BrzWWcVVVV6m233aZmZWWpFotFzc3NVR944AHVZrM1vqe5jFVVVXX27Nlqbm6uajKZ1NTUVPWmm25SKyoqGl9vTmM9HRRVVdU/0NCloaGhoaGhofE/gRbTpKGhoaGhoaFxGmiiSUNDQ0NDQ0PjNNBEk4aGhoaGhobGaaCJJg0NDQ0NDQ2N00ATTRoaGhoaGhoap4EmmjQ0NDQ0NDQ0TgNNNGloaGhoaGhonAaaaNLQ0NDQ0NDQOA000aShoaGhoaGhcRpooklDQ0NDQ0ND4zTQRJOGhoaGhoaGxmmgiSYNDY0/NQsXLmTw4MHExsaSkJDAeeedx6FDhxpfX7NmDd27d8disdC7d2+++uorFEVh69atje/ZvXs3o0ePJjIykpSUFC677DJKSkr+gNFoaGj8lmiiSUND409NbW0td955Jxs2bGDp0qXodDouvPBC3G431dXVjB07li5durB582YeeeQR7rvvPr/P5+fnM3ToULp3787GjRtZuHAhhYWFTJo06Q8akYaGxm+Foqqq+kcfhIaGhsZ/C8XFxSQnJ7Njxw5Wr17N3/72N06ePInFYgHg7bff5pprrmHLli10796dBx98kHXr1rFo0aLGfZw8eZLMzEz27dtH27Zt/6ihaGho/MpoliYNDY0/NYcOHWLq1Knk5uYSHR1NTk4OAMePH2ffvn107dq1UTAB9O3b1+/zmzZtYvny5URGRjb+tW/fvnHfGhoazQfDH30AGhoaGn8kY8eOJTMzk7feeov09HTcbjedO3fGbrejqiqKovi9P9A473a7GTt2LE899VTQvtPS0n7TY9fQ0Ph90USThobGn5bS0lL27NnDG2+8wRlnnAHA6tWrG19v3749M2fOxGazYTabAdi4caPfPnr27MmcOXPIzs7GYNAeqRoazRnNPaehofGnJS4ujoSEBN58800OHjzIsmXLuPPOOxtfnzp1Km63m2uvvZY9e/awaNEi/vWvfwE0WqBuuukmysrKmDJlCuvXr+fw4cMsXryY6dOn43K5/pBxaWho/DZooklDQ+NPi06nY9asWWzatInOnTtzxx138MwzzzS+Hh0dzbx589i6dSvdu3fngQce4MEHHwRojHNKT0/nhx9+wOVyce6559K5c2duu+02YmJi0Om0R6yGRnNCy57T0NDQ+AXMnDmTK6+8ksrKSsLCwv7ow9HQ0Pgd0RzwGhoaGj/Bhx9+SG5uLi1atGDbtm3cd999TJo0SRNMGhp/QjTRpKGhofETFBQU8OCDD1JQUEBaWhoTJ07kscce+6MPS0ND4w9Ac89paGhoaGhoaJwGWpSihoaGhoaGhsZpoIkmDQ0NDQ0NDY3TQBNNGhoaGhoaGhqngSaaNDQ0NDQ0NDROA000aWhoaGhoaGicBppo0tDQ0NDQ0NA4DTTRpKGhoaGhoaFxGmiiSUNDQ0NDQ0PjNPg/iX6VY4r1Qw8AAAAASUVORK5CYII=",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#find the inter-relationship age vs deposit with respect to balance\n",
    "sns.scatterplot(x='age',y='balance',data=bank,hue='deposit')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f094eaf9",
   "metadata": {},
   "source": [
    "for balance less than 30,000 all ages can be our target customers for deposit as the target variable & have positive correlation "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "id": "851b523b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 2000x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#find the inter-relationship job vs deposit \n",
    "pd.crosstab(bank.job,bank.deposit).plot(kind=\"bar\",figsize=(20,6))\n",
    "plt.title('Deposit frequency for Job')\n",
    "plt.xlabel('Job')\n",
    "plt.ylabel('Deposit')\n",
    "plt.savefig('Deposit according to Job.png')#how to save an image in python\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ce2160b2",
   "metadata": {},
   "source": [
    "due to percentage we can concur that management jobs are more than the white-collar jobs therefore the above crosstab is not valid.Thus we need to target the people having mangement jobs"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "id": "80d43bed",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "job\n",
       "white-collar     3900\n",
       "blue-collar      1944\n",
       "technician       1823\n",
       "others           1565\n",
       "pink-collar       923\n",
       "self-employed     405\n",
       "entrepreneur      328\n",
       "housemaid         274\n",
       "Name: count, dtype: int64"
      ]
     },
     "execution_count": 48,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "bank.job.value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "id": "09a081d4",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "white-collar         :       1932 : 17.31%\n",
      "blue-collar          :        708 :  6.34%\n",
      "technician           :        840 :  7.53%\n",
      "pink-collar          :        369 :  3.31%\n",
      "others               :       1021 :  9.15%\n",
      "self-employed        :        187 :  1.68%\n",
      "entrepreneur         :        123 :  1.10%\n",
      "housemaid            :        109 :  0.98%\n"
     ]
    }
   ],
   "source": [
    "#calculate the percentage of deposit=yes count for each of the job categories\n",
    "jobs=['white-collar','blue-collar','technician','pink-collar','others','self-employed','entrepreneur','housemaid']\n",
    "for j in jobs:\n",
    "    # Count the number of 'deposit=yes' for the current job category\n",
    "    deposit_yes_count = len(bank[(bank.deposit == \"yes\") & (bank.job == j)])\n",
    "    \n",
    "    # Calculate the percentage\n",
    "    percentage = (deposit_yes_count / total_entries) * 100\n",
    "    \n",
    "    # Print the result\n",
    "    print(\"{:20} : {:10} : {:5.2f}%\".format(j, deposit_yes_count, percentage))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e898e8cc",
   "metadata": {},
   "source": [
    "white-collar consists of 'management' & 'admin' which is again the most high percentage of deposit when compared to other categories.So we would be targetting more of white-collar category for target=deposit"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "id": "e590b31c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "pdays\n",
       "-1      8324\n",
       " 92      106\n",
       " 182      89\n",
       " 91       84\n",
       " 181      81\n",
       "        ... \n",
       " 437       1\n",
       " 728       1\n",
       " 518       1\n",
       " 828       1\n",
       " 118       1\n",
       "Name: count, Length: 472, dtype: int64"
      ]
     },
     "execution_count": 50,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "bank.pdays.value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "id": "5f261faa",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(11162, 14)"
      ]
     },
     "execution_count": 51,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "bank.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "id": "01e177c1",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "854"
      ]
     },
     "execution_count": 52,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Find maximum number in 'pdays' column\n",
    "bank['pdays'].max()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "id": "75a0f542",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "8324"
      ]
     },
     "execution_count": 53,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Find the number of people having 'pdays=-1'\n",
    "len(bank[bank.pdays == -1])"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f7190ea8",
   "metadata": {},
   "source": [
    "number of days that passed by after the client was last contacted from a previous campaign=pdays.\n",
    "\n",
    "-1 means client was not previously contacted\n",
    "\n",
    "number of customers that have not been contacted before=8324\n",
    "\n",
    "maximum value of pdays=854\n",
    "\n",
    "we will map pdays=-1 into a large value compared to 854 to indicate that it is so far in the past that it has no effect(10,000 can be used)\n",
    "\n",
    "we can change pdays=-1 to 10,000  by using the loc function such that we can select specific rows and columns in the data frame as per the condition bank.pdays == -1 is true"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "id": "dbb32045",
   "metadata": {},
   "outputs": [],
   "source": [
    "bank.loc[bank['pdays'] == -1, 'pdays'] = 10000"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "id": "d59a325b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "pdays\n",
       "10000    8324\n",
       "92        106\n",
       "182        89\n",
       "91         84\n",
       "181        81\n",
       "         ... \n",
       "437         1\n",
       "728         1\n",
       "518         1\n",
       "828         1\n",
       "118         1\n",
       "Name: count, Length: 472, dtype: int64"
      ]
     },
     "execution_count": 55,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "bank.pdays.value_counts()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0e562635",
   "metadata": {},
   "source": [
    "we are creating a new column recent_pdays with the  reciprocal of pdays because we want to decrease the weightage in the pdays column for the clients who have never been contacted significantly"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "id": "cd4695d9",
   "metadata": {},
   "outputs": [],
   "source": [
    "# The condition bank['pdays'] checks if the pdays column has non zero values. In python, non zero values are considered True\n",
    "# and zero values are considered as False. Therefore this condition will be true when pdays is non zero and False, when pdays \n",
    "# is zero.\n",
    "# 1/bank.pdays (1st one) is the value to assign where the condition is True. It calculates reciprocal of the pdays column for\n",
    "# rows where pdays is non zero.\n",
    "# 1/bank.pdays (2nd one) is also the value to assign where the condition is False (else condition). It may seem redundant\n",
    "# as it assigns the same value as for 'True', effectively applying the reciprocal operation to all rows, including\n",
    "# those where pdays is zero. This may be unintensional and could be simplified to any other desired value.\n",
    "bank['recent_pdays']= np.where(bank['pdays'], 1/bank.pdays, 1/bank.pdays)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "id": "fe878d29",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>age</th>\n",
       "      <th>job</th>\n",
       "      <th>marital</th>\n",
       "      <th>education</th>\n",
       "      <th>default</th>\n",
       "      <th>balance</th>\n",
       "      <th>housing</th>\n",
       "      <th>loan</th>\n",
       "      <th>duration</th>\n",
       "      <th>campaign</th>\n",
       "      <th>pdays</th>\n",
       "      <th>previous</th>\n",
       "      <th>poutcome</th>\n",
       "      <th>deposit</th>\n",
       "      <th>recent_pdays</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>59</td>\n",
       "      <td>white-collar</td>\n",
       "      <td>married</td>\n",
       "      <td>secondary</td>\n",
       "      <td>no</td>\n",
       "      <td>2343</td>\n",
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       "      <td>white-collar</td>\n",
       "      <td>married</td>\n",
       "      <td>secondary</td>\n",
       "      <td>no</td>\n",
       "      <td>45</td>\n",
       "      <td>no</td>\n",
       "      <td>no</td>\n",
       "      <td>1467</td>\n",
       "      <td>1</td>\n",
       "      <td>10000</td>\n",
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       "      <td>0.000100</td>\n",
       "    </tr>\n",
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       "      <th>2</th>\n",
       "      <td>41</td>\n",
       "      <td>technician</td>\n",
       "      <td>married</td>\n",
       "      <td>secondary</td>\n",
       "      <td>no</td>\n",
       "      <td>1270</td>\n",
       "      <td>yes</td>\n",
       "      <td>no</td>\n",
       "      <td>1389</td>\n",
       "      <td>1</td>\n",
       "      <td>10000</td>\n",
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       "    </tr>\n",
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       "      <th>3</th>\n",
       "      <td>55</td>\n",
       "      <td>pink-collar</td>\n",
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       "      <td>secondary</td>\n",
       "      <td>no</td>\n",
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       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>54</td>\n",
       "      <td>white-collar</td>\n",
       "      <td>married</td>\n",
       "      <td>tertiary</td>\n",
       "      <td>no</td>\n",
       "      <td>184</td>\n",
       "      <td>no</td>\n",
       "      <td>no</td>\n",
       "      <td>673</td>\n",
       "      <td>2</td>\n",
       "      <td>10000</td>\n",
       "      <td>0</td>\n",
       "      <td>other</td>\n",
       "      <td>yes</td>\n",
       "      <td>0.000100</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
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       "      <td>...</td>\n",
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       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11157</th>\n",
       "      <td>33</td>\n",
       "      <td>blue-collar</td>\n",
       "      <td>single</td>\n",
       "      <td>primary</td>\n",
       "      <td>no</td>\n",
       "      <td>1</td>\n",
       "      <td>yes</td>\n",
       "      <td>no</td>\n",
       "      <td>257</td>\n",
       "      <td>1</td>\n",
       "      <td>10000</td>\n",
       "      <td>0</td>\n",
       "      <td>other</td>\n",
       "      <td>no</td>\n",
       "      <td>0.000100</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11158</th>\n",
       "      <td>39</td>\n",
       "      <td>pink-collar</td>\n",
       "      <td>married</td>\n",
       "      <td>secondary</td>\n",
       "      <td>no</td>\n",
       "      <td>733</td>\n",
       "      <td>no</td>\n",
       "      <td>no</td>\n",
       "      <td>83</td>\n",
       "      <td>4</td>\n",
       "      <td>10000</td>\n",
       "      <td>0</td>\n",
       "      <td>other</td>\n",
       "      <td>no</td>\n",
       "      <td>0.000100</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11159</th>\n",
       "      <td>32</td>\n",
       "      <td>technician</td>\n",
       "      <td>single</td>\n",
       "      <td>secondary</td>\n",
       "      <td>no</td>\n",
       "      <td>29</td>\n",
       "      <td>no</td>\n",
       "      <td>no</td>\n",
       "      <td>156</td>\n",
       "      <td>2</td>\n",
       "      <td>10000</td>\n",
       "      <td>0</td>\n",
       "      <td>other</td>\n",
       "      <td>no</td>\n",
       "      <td>0.000100</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11160</th>\n",
       "      <td>43</td>\n",
       "      <td>technician</td>\n",
       "      <td>married</td>\n",
       "      <td>secondary</td>\n",
       "      <td>no</td>\n",
       "      <td>0</td>\n",
       "      <td>no</td>\n",
       "      <td>yes</td>\n",
       "      <td>9</td>\n",
       "      <td>2</td>\n",
       "      <td>172</td>\n",
       "      <td>5</td>\n",
       "      <td>failure</td>\n",
       "      <td>no</td>\n",
       "      <td>0.005814</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11161</th>\n",
       "      <td>34</td>\n",
       "      <td>technician</td>\n",
       "      <td>married</td>\n",
       "      <td>secondary</td>\n",
       "      <td>no</td>\n",
       "      <td>0</td>\n",
       "      <td>no</td>\n",
       "      <td>no</td>\n",
       "      <td>628</td>\n",
       "      <td>1</td>\n",
       "      <td>10000</td>\n",
       "      <td>0</td>\n",
       "      <td>other</td>\n",
       "      <td>no</td>\n",
       "      <td>0.000100</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>11162 rows × 15 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       age           job  marital  education default  balance housing loan  \\\n",
       "0       59  white-collar  married  secondary      no     2343     yes   no   \n",
       "1       56  white-collar  married  secondary      no       45      no   no   \n",
       "2       41    technician  married  secondary      no     1270     yes   no   \n",
       "3       55   pink-collar  married  secondary      no     2476     yes   no   \n",
       "4       54  white-collar  married   tertiary      no      184      no   no   \n",
       "...    ...           ...      ...        ...     ...      ...     ...  ...   \n",
       "11157   33   blue-collar   single    primary      no        1     yes   no   \n",
       "11158   39   pink-collar  married  secondary      no      733      no   no   \n",
       "11159   32    technician   single  secondary      no       29      no   no   \n",
       "11160   43    technician  married  secondary      no        0      no  yes   \n",
       "11161   34    technician  married  secondary      no        0      no   no   \n",
       "\n",
       "       duration  campaign  pdays  previous poutcome deposit  recent_pdays  \n",
       "0          1042         1  10000         0    other     yes      0.000100  \n",
       "1          1467         1  10000         0    other     yes      0.000100  \n",
       "2          1389         1  10000         0    other     yes      0.000100  \n",
       "3           579         1  10000         0    other     yes      0.000100  \n",
       "4           673         2  10000         0    other     yes      0.000100  \n",
       "...         ...       ...    ...       ...      ...     ...           ...  \n",
       "11157       257         1  10000         0    other      no      0.000100  \n",
       "11158        83         4  10000         0    other      no      0.000100  \n",
       "11159       156         2  10000         0    other      no      0.000100  \n",
       "11160         9         2    172         5  failure      no      0.005814  \n",
       "11161       628         1  10000         0    other      no      0.000100  \n",
       "\n",
       "[11162 rows x 15 columns]"
      ]
     },
     "execution_count": 57,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "bank"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "id": "0933f373",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th>4</th>\n",
       "      <td>54</td>\n",
       "      <td>white-collar</td>\n",
       "      <td>married</td>\n",
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       "      <td>other</td>\n",
       "      <td>yes</td>\n",
       "      <td>0.0001</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   age           job  marital  education default  balance housing loan  \\\n",
       "0   59  white-collar  married  secondary      no     2343     yes   no   \n",
       "1   56  white-collar  married  secondary      no       45      no   no   \n",
       "2   41    technician  married  secondary      no     1270     yes   no   \n",
       "3   55   pink-collar  married  secondary      no     2476     yes   no   \n",
       "4   54  white-collar  married   tertiary      no      184      no   no   \n",
       "\n",
       "   duration  campaign  pdays  previous poutcome deposit  recent_pdays  \n",
       "0      1042         1  10000         0    other     yes        0.0001  \n",
       "1      1467         1  10000         0    other     yes        0.0001  \n",
       "2      1389         1  10000         0    other     yes        0.0001  \n",
       "3       579         1  10000         0    other     yes        0.0001  \n",
       "4       673         2  10000         0    other     yes        0.0001  "
      ]
     },
     "execution_count": 58,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "bank.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "id": "30a2db38",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
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       "      <th>11157</th>\n",
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       "      <td>blue-collar</td>\n",
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       "      <td>pink-collar</td>\n",
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       "      <td>32</td>\n",
       "      <td>technician</td>\n",
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       "      <td>secondary</td>\n",
       "      <td>no</td>\n",
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       "      <td>2</td>\n",
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       "      <th>11160</th>\n",
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       "      <td>9</td>\n",
       "      <td>2</td>\n",
       "      <td>172</td>\n",
       "      <td>5</td>\n",
       "      <td>failure</td>\n",
       "      <td>no</td>\n",
       "      <td>0.005814</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11161</th>\n",
       "      <td>34</td>\n",
       "      <td>technician</td>\n",
       "      <td>married</td>\n",
       "      <td>secondary</td>\n",
       "      <td>no</td>\n",
       "      <td>0</td>\n",
       "      <td>no</td>\n",
       "      <td>no</td>\n",
       "      <td>628</td>\n",
       "      <td>1</td>\n",
       "      <td>10000</td>\n",
       "      <td>0</td>\n",
       "      <td>other</td>\n",
       "      <td>no</td>\n",
       "      <td>0.000100</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       age          job  marital  education default  balance housing loan  \\\n",
       "11157   33  blue-collar   single    primary      no        1     yes   no   \n",
       "11158   39  pink-collar  married  secondary      no      733      no   no   \n",
       "11159   32   technician   single  secondary      no       29      no   no   \n",
       "11160   43   technician  married  secondary      no        0      no  yes   \n",
       "11161   34   technician  married  secondary      no        0      no   no   \n",
       "\n",
       "       duration  campaign  pdays  previous poutcome deposit  recent_pdays  \n",
       "11157       257         1  10000         0    other      no      0.000100  \n",
       "11158        83         4  10000         0    other      no      0.000100  \n",
       "11159       156         2  10000         0    other      no      0.000100  \n",
       "11160         9         2    172         5  failure      no      0.005814  \n",
       "11161       628         1  10000         0    other      no      0.000100  "
      ]
     },
     "execution_count": 59,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "bank.tail()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "id": "98cad814",
   "metadata": {},
   "outputs": [],
   "source": [
    "bank.drop(['pdays'],axis=1,inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "id": "d0b462d3",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th>11158</th>\n",
       "      <td>39</td>\n",
       "      <td>pink-collar</td>\n",
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       "      <td>secondary</td>\n",
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       "      <td>733</td>\n",
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       "      <td>no</td>\n",
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       "      <td>0.000100</td>\n",
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       "      <th>11159</th>\n",
       "      <td>32</td>\n",
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       "      <td>43</td>\n",
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       "      <td>0.005814</td>\n",
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       "      <th>11161</th>\n",
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       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>11162 rows × 14 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       age           job  marital  education default  balance housing loan  \\\n",
       "0       59  white-collar  married  secondary      no     2343     yes   no   \n",
       "1       56  white-collar  married  secondary      no       45      no   no   \n",
       "2       41    technician  married  secondary      no     1270     yes   no   \n",
       "3       55   pink-collar  married  secondary      no     2476     yes   no   \n",
       "4       54  white-collar  married   tertiary      no      184      no   no   \n",
       "...    ...           ...      ...        ...     ...      ...     ...  ...   \n",
       "11157   33   blue-collar   single    primary      no        1     yes   no   \n",
       "11158   39   pink-collar  married  secondary      no      733      no   no   \n",
       "11159   32    technician   single  secondary      no       29      no   no   \n",
       "11160   43    technician  married  secondary      no        0      no  yes   \n",
       "11161   34    technician  married  secondary      no        0      no   no   \n",
       "\n",
       "       duration  campaign  previous poutcome deposit  recent_pdays  \n",
       "0          1042         1         0    other     yes      0.000100  \n",
       "1          1467         1         0    other     yes      0.000100  \n",
       "2          1389         1         0    other     yes      0.000100  \n",
       "3           579         1         0    other     yes      0.000100  \n",
       "4           673         2         0    other     yes      0.000100  \n",
       "...         ...       ...       ...      ...     ...           ...  \n",
       "11157       257         1         0    other      no      0.000100  \n",
       "11158        83         4         0    other      no      0.000100  \n",
       "11159       156         2         0    other      no      0.000100  \n",
       "11160         9         2         5  failure      no      0.005814  \n",
       "11161       628         1         0    other      no      0.000100  \n",
       "\n",
       "[11162 rows x 14 columns]"
      ]
     },
     "execution_count": 61,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "bank"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "id": "26beaf45",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "default\n",
       "no     10994\n",
       "yes      168\n",
       "Name: count, dtype: int64"
      ]
     },
     "execution_count": 62,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "bank.default.value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "id": "cbc11b4b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "marital\n",
       "married     6351\n",
       "single      3518\n",
       "divorced    1293\n",
       "Name: count, dtype: int64"
      ]
     },
     "execution_count": 63,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "bank.marital.value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "id": "384555aa",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "job : ['white-collar' 'technician' 'pink-collar' 'others' 'blue-collar'\n",
      " 'entrepreneur' 'housemaid' 'self-employed']\n",
      "job : job\n",
      "white-collar     3900\n",
      "blue-collar      1944\n",
      "technician       1823\n",
      "others           1565\n",
      "pink-collar       923\n",
      "self-employed     405\n",
      "entrepreneur      328\n",
      "housemaid         274\n",
      "Name: count, dtype: int64\n",
      "====================================\n",
      "marital : ['married' 'single' 'divorced']\n",
      "marital : marital\n",
      "married     6351\n",
      "single      3518\n",
      "divorced    1293\n",
      "Name: count, dtype: int64\n",
      "====================================\n",
      "education : ['secondary' 'tertiary' 'primary' 'unknown']\n",
      "education : education\n",
      "secondary    5476\n",
      "tertiary     3689\n",
      "primary      1500\n",
      "unknown       497\n",
      "Name: count, dtype: int64\n",
      "====================================\n",
      "default : ['no' 'yes']\n",
      "default : default\n",
      "no     10994\n",
      "yes      168\n",
      "Name: count, dtype: int64\n",
      "====================================\n",
      "housing : ['yes' 'no']\n",
      "housing : housing\n",
      "no     5881\n",
      "yes    5281\n",
      "Name: count, dtype: int64\n",
      "====================================\n",
      "loan : ['no' 'yes']\n",
      "loan : loan\n",
      "no     9702\n",
      "yes    1460\n",
      "Name: count, dtype: int64\n",
      "====================================\n",
      "poutcome : ['other' 'failure' 'success']\n",
      "poutcome : poutcome\n",
      "other      8863\n",
      "failure    1228\n",
      "success    1071\n",
      "Name: count, dtype: int64\n",
      "====================================\n",
      "deposit : ['yes' 'no']\n",
      "deposit : deposit\n",
      "no     5873\n",
      "yes    5289\n",
      "Name: count, dtype: int64\n",
      "====================================\n"
     ]
    }
   ],
   "source": [
    "categorical_col = [] #Initializes an empty list to store the names of columns that are identified as categorical.\n",
    "for column in bank.columns: #Iterates through each column in the DataFrame.\n",
    "    if len(bank[column].unique()) <= 30:#Checks if the column's data type is 'object' (typically indicating a categorical variable) and if the number of unique values in that column is 30 or less.\n",
    "        categorical_col.append(column) # If the conditions in the if statement are met, it appends the column name to the categorical_col list.\n",
    "        print(f\"{column} : {bank[column].unique()}\") #Prints the name of the column and its unique values.\n",
    "        print(f\"{column} : {bank[column].value_counts()}\")\n",
    "        print(\"====================================\") # Prints a separator line for clarity.\n",
    "        \n",
    "#The purpose of this code is to help you identify and inspect categorical columns in your DataFrame, particularly those with a manageable number of unique categories. This information is useful for data exploration and analysis, especially when you need to decide how to handle categorical variables in machine learning or data analysis tasks."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "592c0197",
   "metadata": {},
   "source": [
    "job=one-hot\n",
    "\n",
    "education=one-hot\n",
    "\n",
    "marital=one-hot\n",
    "\n",
    "default=label\n",
    "\n",
    "housing=label\n",
    "\n",
    "loan=label\n",
    "\n",
    "poutcome=one-hot\n",
    "\n",
    "deposit=label"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "id": "5935543a",
   "metadata": {},
   "outputs": [],
   "source": [
    "# values for \"loan\" : yes/no\n",
    "bank['loan_cat'] = bank['loan'].map({'yes':1, 'no':0})\n",
    "bank.drop('loan', axis=1,inplace = True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "id": "42b27e9f",
   "metadata": {},
   "outputs": [],
   "source": [
    "# values for \"default\" : yes/no\n",
    "bank['ldefault_cat'] = bank['default'].map({'yes':1, 'no':0})\n",
    "bank.drop('default', axis=1,inplace = True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "id": "60f6afaf",
   "metadata": {},
   "outputs": [],
   "source": [
    "# values for \"housing\" : yes/no\n",
    "bank['housing_cat'] = bank['housing'].map({'yes':1, 'no':0})\n",
    "bank.drop('housing', axis=1,inplace = True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "id": "38d4e557",
   "metadata": {},
   "outputs": [],
   "source": [
    "# values for \"deposit\" : yes/no\n",
    "bank['deposit_cat'] = bank['deposit'].map({'yes':1, 'no':0})\n",
    "bank.drop('deposit', axis=1,inplace = True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "id": "4955521e",
   "metadata": {},
   "outputs": [],
   "source": [
    "le = LabelEncoder()\n",
    "binary=OneHotEncoder(categories='auto')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "id": "56e087b3",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "job\n",
       "white-collar     3900\n",
       "blue-collar      1944\n",
       "technician       1823\n",
       "others           1565\n",
       "pink-collar       923\n",
       "self-employed     405\n",
       "entrepreneur      328\n",
       "housemaid         274\n",
       "Name: count, dtype: int64"
      ]
     },
     "execution_count": 70,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "bank.job.value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "id": "6db48c63",
   "metadata": {},
   "outputs": [],
   "source": [
    "bank= pd.get_dummies(data=bank, columns=['job','marital','education','poutcome'], prefix=['job','marital','education','poutcome'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "id": "c55b4123",
   "metadata": {},
   "outputs": [
    {
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       "</div>"
      ],
      "text/plain": [
       "       age  balance  duration  campaign  previous  recent_pdays  loan_cat  \\\n",
       "0       59     2343      1042         1         0      0.000100         0   \n",
       "1       56       45      1467         1         0      0.000100         0   \n",
       "2       41     1270      1389         1         0      0.000100         0   \n",
       "3       55     2476       579         1         0      0.000100         0   \n",
       "4       54      184       673         2         0      0.000100         0   \n",
       "...    ...      ...       ...       ...       ...           ...       ...   \n",
       "11157   33        1       257         1         0      0.000100         0   \n",
       "11158   39      733        83         4         0      0.000100         0   \n",
       "11159   32       29       156         2         0      0.000100         0   \n",
       "11160   43        0         9         2         5      0.005814         1   \n",
       "11161   34        0       628         1         0      0.000100         0   \n",
       "\n",
       "       ldefault_cat  housing_cat  deposit_cat  ...  marital_divorced  \\\n",
       "0                 0            1            1  ...             False   \n",
       "1                 0            0            1  ...             False   \n",
       "2                 0            1            1  ...             False   \n",
       "3                 0            1            1  ...             False   \n",
       "4                 0            0            1  ...             False   \n",
       "...             ...          ...          ...  ...               ...   \n",
       "11157             0            1            0  ...             False   \n",
       "11158             0            0            0  ...             False   \n",
       "11159             0            0            0  ...             False   \n",
       "11160             0            0            0  ...             False   \n",
       "11161             0            0            0  ...             False   \n",
       "\n",
       "       marital_married  marital_single  education_primary  \\\n",
       "0                 True           False              False   \n",
       "1                 True           False              False   \n",
       "2                 True           False              False   \n",
       "3                 True           False              False   \n",
       "4                 True           False              False   \n",
       "...                ...             ...                ...   \n",
       "11157            False            True               True   \n",
       "11158             True           False              False   \n",
       "11159            False            True              False   \n",
       "11160             True           False              False   \n",
       "11161             True           False              False   \n",
       "\n",
       "       education_secondary  education_tertiary  education_unknown  \\\n",
       "0                     True               False              False   \n",
       "1                     True               False              False   \n",
       "2                     True               False              False   \n",
       "3                     True               False              False   \n",
       "4                    False                True              False   \n",
       "...                    ...                 ...                ...   \n",
       "11157                False               False              False   \n",
       "11158                 True               False              False   \n",
       "11159                 True               False              False   \n",
       "11160                 True               False              False   \n",
       "11161                 True               False              False   \n",
       "\n",
       "       poutcome_failure  poutcome_other  poutcome_success  \n",
       "0                 False            True             False  \n",
       "1                 False            True             False  \n",
       "2                 False            True             False  \n",
       "3                 False            True             False  \n",
       "4                 False            True             False  \n",
       "...                 ...             ...               ...  \n",
       "11157             False            True             False  \n",
       "11158             False            True             False  \n",
       "11159             False            True             False  \n",
       "11160              True           False             False  \n",
       "11161             False            True             False  \n",
       "\n",
       "[11162 rows x 28 columns]"
      ]
     },
     "execution_count": 72,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "bank"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "id": "c0d66775",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "age                      int64\n",
       "balance                  int64\n",
       "duration                 int64\n",
       "campaign                 int64\n",
       "previous                 int64\n",
       "recent_pdays           float64\n",
       "loan_cat                 int64\n",
       "ldefault_cat             int64\n",
       "housing_cat              int64\n",
       "deposit_cat              int64\n",
       "job_blue-collar           bool\n",
       "job_entrepreneur          bool\n",
       "job_housemaid             bool\n",
       "job_others                bool\n",
       "job_pink-collar           bool\n",
       "job_self-employed         bool\n",
       "job_technician            bool\n",
       "job_white-collar          bool\n",
       "marital_divorced          bool\n",
       "marital_married           bool\n",
       "marital_single            bool\n",
       "education_primary         bool\n",
       "education_secondary       bool\n",
       "education_tertiary        bool\n",
       "education_unknown         bool\n",
       "poutcome_failure          bool\n",
       "poutcome_other            bool\n",
       "poutcome_success          bool\n",
       "dtype: object"
      ]
     },
     "execution_count": 73,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "bank.dtypes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "id": "6f3dcd28",
   "metadata": {},
   "outputs": [
    {
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       "      <td>0.000100</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>56</td>\n",
       "      <td>45</td>\n",
       "      <td>1467</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0.000100</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
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       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>41</td>\n",
       "      <td>1270</td>\n",
       "      <td>1389</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0.000100</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>55</td>\n",
       "      <td>2476</td>\n",
       "      <td>579</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0.000100</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>54</td>\n",
       "      <td>184</td>\n",
       "      <td>673</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>0.000100</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11157</th>\n",
       "      <td>33</td>\n",
       "      <td>1</td>\n",
       "      <td>257</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0.000100</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11158</th>\n",
       "      <td>39</td>\n",
       "      <td>733</td>\n",
       "      <td>83</td>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "      <td>0.000100</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11159</th>\n",
       "      <td>32</td>\n",
       "      <td>29</td>\n",
       "      <td>156</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>0.000100</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11160</th>\n",
       "      <td>43</td>\n",
       "      <td>0</td>\n",
       "      <td>9</td>\n",
       "      <td>2</td>\n",
       "      <td>5</td>\n",
       "      <td>0.005814</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11161</th>\n",
       "      <td>34</td>\n",
       "      <td>0</td>\n",
       "      <td>628</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0.000100</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>11162 rows × 28 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       age  balance  duration  campaign  previous  recent_pdays  loan_cat  \\\n",
       "0       59     2343      1042         1         0      0.000100         0   \n",
       "1       56       45      1467         1         0      0.000100         0   \n",
       "2       41     1270      1389         1         0      0.000100         0   \n",
       "3       55     2476       579         1         0      0.000100         0   \n",
       "4       54      184       673         2         0      0.000100         0   \n",
       "...    ...      ...       ...       ...       ...           ...       ...   \n",
       "11157   33        1       257         1         0      0.000100         0   \n",
       "11158   39      733        83         4         0      0.000100         0   \n",
       "11159   32       29       156         2         0      0.000100         0   \n",
       "11160   43        0         9         2         5      0.005814         1   \n",
       "11161   34        0       628         1         0      0.000100         0   \n",
       "\n",
       "       ldefault_cat  housing_cat  deposit_cat  ...  marital_divorced  \\\n",
       "0                 0            1            1  ...                 0   \n",
       "1                 0            0            1  ...                 0   \n",
       "2                 0            1            1  ...                 0   \n",
       "3                 0            1            1  ...                 0   \n",
       "4                 0            0            1  ...                 0   \n",
       "...             ...          ...          ...  ...               ...   \n",
       "11157             0            1            0  ...                 0   \n",
       "11158             0            0            0  ...                 0   \n",
       "11159             0            0            0  ...                 0   \n",
       "11160             0            0            0  ...                 0   \n",
       "11161             0            0            0  ...                 0   \n",
       "\n",
       "       marital_married  marital_single  education_primary  \\\n",
       "0                    1               0                  0   \n",
       "1                    1               0                  0   \n",
       "2                    1               0                  0   \n",
       "3                    1               0                  0   \n",
       "4                    1               0                  0   \n",
       "...                ...             ...                ...   \n",
       "11157                0               1                  1   \n",
       "11158                1               0                  0   \n",
       "11159                0               1                  0   \n",
       "11160                1               0                  0   \n",
       "11161                1               0                  0   \n",
       "\n",
       "       education_secondary  education_tertiary  education_unknown  \\\n",
       "0                        1                   0                  0   \n",
       "1                        1                   0                  0   \n",
       "2                        1                   0                  0   \n",
       "3                        1                   0                  0   \n",
       "4                        0                   1                  0   \n",
       "...                    ...                 ...                ...   \n",
       "11157                    0                   0                  0   \n",
       "11158                    1                   0                  0   \n",
       "11159                    1                   0                  0   \n",
       "11160                    1                   0                  0   \n",
       "11161                    1                   0                  0   \n",
       "\n",
       "       poutcome_failure  poutcome_other  poutcome_success  \n",
       "0                     0               1                 0  \n",
       "1                     0               1                 0  \n",
       "2                     0               1                 0  \n",
       "3                     0               1                 0  \n",
       "4                     0               1                 0  \n",
       "...                 ...             ...               ...  \n",
       "11157                 0               1                 0  \n",
       "11158                 0               1                 0  \n",
       "11159                 0               1                 0  \n",
       "11160                 1               0                 0  \n",
       "11161                 0               1                 0  \n",
       "\n",
       "[11162 rows x 28 columns]"
      ]
     },
     "execution_count": 74,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "bank.replace({True:1,False:0},inplace=True)\n",
    "bank\n",
    "#if the columns with true or false values have the data type as object then code will be \n",
    "#bank.replace({\"True\":1,\"False\":0},inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "id": "f7b5b7a7",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>age</th>\n",
       "      <th>balance</th>\n",
       "      <th>duration</th>\n",
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       "      <th>...</th>\n",
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       "      <th>education_tertiary</th>\n",
       "      <th>education_unknown</th>\n",
       "      <th>poutcome_failure</th>\n",
       "      <th>poutcome_other</th>\n",
       "      <th>poutcome_success</th>\n",
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       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11159</th>\n",
       "      <td>32</td>\n",
       "      <td>29</td>\n",
       "      <td>156</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>0.000100</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11160</th>\n",
       "      <td>43</td>\n",
       "      <td>0</td>\n",
       "      <td>9</td>\n",
       "      <td>2</td>\n",
       "      <td>5</td>\n",
       "      <td>0.005814</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11161</th>\n",
       "      <td>34</td>\n",
       "      <td>0</td>\n",
       "      <td>628</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0.000100</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>11162 rows × 28 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       age  balance  duration  campaign  previous  recent_pdays  loan_cat  \\\n",
       "0       59     2343      1042         1         0      0.000100         0   \n",
       "1       56       45      1467         1         0      0.000100         0   \n",
       "2       41     1270      1389         1         0      0.000100         0   \n",
       "3       55     2476       579         1         0      0.000100         0   \n",
       "4       54      184       673         2         0      0.000100         0   \n",
       "...    ...      ...       ...       ...       ...           ...       ...   \n",
       "11157   33        1       257         1         0      0.000100         0   \n",
       "11158   39      733        83         4         0      0.000100         0   \n",
       "11159   32       29       156         2         0      0.000100         0   \n",
       "11160   43        0         9         2         5      0.005814         1   \n",
       "11161   34        0       628         1         0      0.000100         0   \n",
       "\n",
       "       ldefault_cat  housing_cat  deposit_cat  ...  marital_divorced  \\\n",
       "0                 0            1            1  ...                 0   \n",
       "1                 0            0            1  ...                 0   \n",
       "2                 0            1            1  ...                 0   \n",
       "3                 0            1            1  ...                 0   \n",
       "4                 0            0            1  ...                 0   \n",
       "...             ...          ...          ...  ...               ...   \n",
       "11157             0            1            0  ...                 0   \n",
       "11158             0            0            0  ...                 0   \n",
       "11159             0            0            0  ...                 0   \n",
       "11160             0            0            0  ...                 0   \n",
       "11161             0            0            0  ...                 0   \n",
       "\n",
       "       marital_married  marital_single  education_primary  \\\n",
       "0                    1               0                  0   \n",
       "1                    1               0                  0   \n",
       "2                    1               0                  0   \n",
       "3                    1               0                  0   \n",
       "4                    1               0                  0   \n",
       "...                ...             ...                ...   \n",
       "11157                0               1                  1   \n",
       "11158                1               0                  0   \n",
       "11159                0               1                  0   \n",
       "11160                1               0                  0   \n",
       "11161                1               0                  0   \n",
       "\n",
       "       education_secondary  education_tertiary  education_unknown  \\\n",
       "0                        1                   0                  0   \n",
       "1                        1                   0                  0   \n",
       "2                        1                   0                  0   \n",
       "3                        1                   0                  0   \n",
       "4                        0                   1                  0   \n",
       "...                    ...                 ...                ...   \n",
       "11157                    0                   0                  0   \n",
       "11158                    1                   0                  0   \n",
       "11159                    1                   0                  0   \n",
       "11160                    1                   0                  0   \n",
       "11161                    1                   0                  0   \n",
       "\n",
       "       poutcome_failure  poutcome_other  poutcome_success  \n",
       "0                     0               1                 0  \n",
       "1                     0               1                 0  \n",
       "2                     0               1                 0  \n",
       "3                     0               1                 0  \n",
       "4                     0               1                 0  \n",
       "...                 ...             ...               ...  \n",
       "11157                 0               1                 0  \n",
       "11158                 0               1                 0  \n",
       "11159                 0               1                 0  \n",
       "11160                 1               0                 0  \n",
       "11161                 0               1                 0  \n",
       "\n",
       "[11162 rows x 28 columns]"
      ]
     },
     "execution_count": 75,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#method 2\n",
    "#bank=bank.astype(int)\n",
    "bank"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "id": "3c164401",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['age', 'balance', 'duration', 'campaign', 'previous', 'recent_pdays',\n",
       "       'loan_cat', 'ldefault_cat', 'housing_cat', 'deposit_cat',\n",
       "       'job_blue-collar', 'job_entrepreneur', 'job_housemaid', 'job_others',\n",
       "       'job_pink-collar', 'job_self-employed', 'job_technician',\n",
       "       'job_white-collar', 'marital_divorced', 'marital_married',\n",
       "       'marital_single', 'education_primary', 'education_secondary',\n",
       "       'education_tertiary', 'education_unknown', 'poutcome_failure',\n",
       "       'poutcome_other', 'poutcome_success'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 76,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "bank.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "id": "84dd2a9b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>age</th>\n",
       "      <th>balance</th>\n",
       "      <th>duration</th>\n",
       "      <th>campaign</th>\n",
       "      <th>previous</th>\n",
       "      <th>recent_pdays</th>\n",
       "      <th>loan_cat</th>\n",
       "      <th>ldefault_cat</th>\n",
       "      <th>housing_cat</th>\n",
       "      <th>deposit_cat</th>\n",
       "      <th>...</th>\n",
       "      <th>job_self-employed</th>\n",
       "      <th>job_technician</th>\n",
       "      <th>job_white-collar</th>\n",
       "      <th>marital_divorced</th>\n",
       "      <th>marital_married</th>\n",
       "      <th>education_primary</th>\n",
       "      <th>education_secondary</th>\n",
       "      <th>education_tertiary</th>\n",
       "      <th>poutcome_failure</th>\n",
       "      <th>poutcome_success</th>\n",
       "    </tr>\n",
       "  </thead>\n",
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       "    <tr>\n",
       "      <th>1</th>\n",
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       "      <td>45</td>\n",
       "      <td>1467</td>\n",
       "      <td>1</td>\n",
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       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>55</td>\n",
       "      <td>2476</td>\n",
       "      <td>579</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0.000100</td>\n",
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       "      <td>0</td>\n",
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       "      <th>11157</th>\n",
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       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11159</th>\n",
       "      <td>32</td>\n",
       "      <td>29</td>\n",
       "      <td>156</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>0.000100</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
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       "      <td>1</td>\n",
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       "    </tr>\n",
       "    <tr>\n",
       "      <th>11161</th>\n",
       "      <td>34</td>\n",
       "      <td>0</td>\n",
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       "      <td>1</td>\n",
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       "      <td>...</td>\n",
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       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>11162 rows × 24 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       age  balance  duration  campaign  previous  recent_pdays  loan_cat  \\\n",
       "0       59     2343      1042         1         0      0.000100         0   \n",
       "1       56       45      1467         1         0      0.000100         0   \n",
       "2       41     1270      1389         1         0      0.000100         0   \n",
       "3       55     2476       579         1         0      0.000100         0   \n",
       "4       54      184       673         2         0      0.000100         0   \n",
       "...    ...      ...       ...       ...       ...           ...       ...   \n",
       "11157   33        1       257         1         0      0.000100         0   \n",
       "11158   39      733        83         4         0      0.000100         0   \n",
       "11159   32       29       156         2         0      0.000100         0   \n",
       "11160   43        0         9         2         5      0.005814         1   \n",
       "11161   34        0       628         1         0      0.000100         0   \n",
       "\n",
       "       ldefault_cat  housing_cat  deposit_cat  ...  job_self-employed  \\\n",
       "0                 0            1            1  ...                  0   \n",
       "1                 0            0            1  ...                  0   \n",
       "2                 0            1            1  ...                  0   \n",
       "3                 0            1            1  ...                  0   \n",
       "4                 0            0            1  ...                  0   \n",
       "...             ...          ...          ...  ...                ...   \n",
       "11157             0            1            0  ...                  0   \n",
       "11158             0            0            0  ...                  0   \n",
       "11159             0            0            0  ...                  0   \n",
       "11160             0            0            0  ...                  0   \n",
       "11161             0            0            0  ...                  0   \n",
       "\n",
       "       job_technician  job_white-collar  marital_divorced  marital_married  \\\n",
       "0                   0                 1                 0                1   \n",
       "1                   0                 1                 0                1   \n",
       "2                   1                 0                 0                1   \n",
       "3                   0                 0                 0                1   \n",
       "4                   0                 1                 0                1   \n",
       "...               ...               ...               ...              ...   \n",
       "11157               0                 0                 0                0   \n",
       "11158               0                 0                 0                1   \n",
       "11159               1                 0                 0                0   \n",
       "11160               1                 0                 0                1   \n",
       "11161               1                 0                 0                1   \n",
       "\n",
       "       education_primary  education_secondary  education_tertiary  \\\n",
       "0                      0                    1                   0   \n",
       "1                      0                    1                   0   \n",
       "2                      0                    1                   0   \n",
       "3                      0                    1                   0   \n",
       "4                      0                    0                   1   \n",
       "...                  ...                  ...                 ...   \n",
       "11157                  1                    0                   0   \n",
       "11158                  0                    1                   0   \n",
       "11159                  0                    1                   0   \n",
       "11160                  0                    1                   0   \n",
       "11161                  0                    1                   0   \n",
       "\n",
       "       poutcome_failure  poutcome_success  \n",
       "0                     0                 0  \n",
       "1                     0                 0  \n",
       "2                     0                 0  \n",
       "3                     0                 0  \n",
       "4                     0                 0  \n",
       "...                 ...               ...  \n",
       "11157                 0                 0  \n",
       "11158                 0                 0  \n",
       "11159                 0                 0  \n",
       "11160                 1                 0  \n",
       "11161                 0                 0  \n",
       "\n",
       "[11162 rows x 24 columns]"
      ]
     },
     "execution_count": 77,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "bank.drop([\"job_others\",\"marital_single\",\"poutcome_other\",\"education_unknown\"],axis=1,inplace=True)\n",
    "bank"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b12ed052",
   "metadata": {},
   "source": [
    "job=bank['job']\n",
    "job1=le.fit_transform(job)\n",
    "j1=binary.fit_transform(job1.reshape(-1, 1))\n",
    "j2=j1.toarray()\n",
    "job_df=pd.DataFrame(j2,columns=['w','b','t','o','p','s','e','h'])\n",
    "job_df"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3c8a66b4",
   "metadata": {},
   "source": [
    "binary=OneHotEncoder(categories='auto')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 79,
   "id": "6dfb7608",
   "metadata": {},
   "outputs": [
    {
     "ename": "AttributeError",
     "evalue": "'DataFrame' object has no attribute 'education'",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mAttributeError\u001b[0m                            Traceback (most recent call last)",
      "\u001b[0;32m/var/folders/x_/bydwqxmj22b17_nc_zdgjlhc0000gn/T/ipykernel_28965/3107248539.py\u001b[0m in \u001b[0;36m?\u001b[0;34m()\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mbank\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0meducation\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mvalue_counts\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
      "\u001b[0;32m~/anaconda3/envs/notebook/lib/python3.10/site-packages/pandas/core/generic.py\u001b[0m in \u001b[0;36m?\u001b[0;34m(self, name)\u001b[0m\n\u001b[1;32m   6200\u001b[0m             \u001b[0;32mand\u001b[0m \u001b[0mname\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_accessors\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   6201\u001b[0m             \u001b[0;32mand\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_info_axis\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_can_hold_identifiers_and_holds_name\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mname\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   6202\u001b[0m         ):\n\u001b[1;32m   6203\u001b[0m             \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mname\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 6204\u001b[0;31m         \u001b[0;32mreturn\u001b[0m \u001b[0mobject\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m__getattribute__\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mname\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
      "\u001b[0;31mAttributeError\u001b[0m: 'DataFrame' object has no attribute 'education'"
     ]
    }
   ],
   "source": [
    "bank.education.value_counts()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "33bfe965",
   "metadata": {},
   "source": [
    "education=bank['education']\n",
    "education1=le.fit_transform(education)\n",
    "e1=binary.fit_transform(education1.reshape(-1, 1))\n",
    "e2=e1.toarray()\n",
    "education_df=pd.DataFrame(e2,columns=['s','t','p','u'])\n",
    "education_df"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "fe591cea",
   "metadata": {},
   "source": [
    "binary=OneHotEncoder(categories='auto')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 80,
   "id": "86b6eaad",
   "metadata": {},
   "outputs": [
    {
     "ename": "AttributeError",
     "evalue": "'DataFrame' object has no attribute 'marital'",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mAttributeError\u001b[0m                            Traceback (most recent call last)",
      "\u001b[0;32m/var/folders/x_/bydwqxmj22b17_nc_zdgjlhc0000gn/T/ipykernel_28965/3789677141.py\u001b[0m in \u001b[0;36m?\u001b[0;34m()\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mbank\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mmarital\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mvalue_counts\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
      "\u001b[0;32m~/anaconda3/envs/notebook/lib/python3.10/site-packages/pandas/core/generic.py\u001b[0m in \u001b[0;36m?\u001b[0;34m(self, name)\u001b[0m\n\u001b[1;32m   6200\u001b[0m             \u001b[0;32mand\u001b[0m \u001b[0mname\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_accessors\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   6201\u001b[0m             \u001b[0;32mand\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_info_axis\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_can_hold_identifiers_and_holds_name\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mname\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   6202\u001b[0m         ):\n\u001b[1;32m   6203\u001b[0m             \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mname\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 6204\u001b[0;31m         \u001b[0;32mreturn\u001b[0m \u001b[0mobject\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m__getattribute__\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mname\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
      "\u001b[0;31mAttributeError\u001b[0m: 'DataFrame' object has no attribute 'marital'"
     ]
    }
   ],
   "source": [
    "bank.marital.value_counts()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d6d5b65f",
   "metadata": {},
   "source": [
    "marital=bank['marital']\n",
    "marital1=le.fit_transform(marital)\n",
    "m1=binary.fit_transform(marital1.reshape(-1, 1))\n",
    "m2=m1.toarray()\n",
    "marital_df=pd.DataFrame(m2,columns=['m','s','d'])\n",
    "marital_df"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "34d94400",
   "metadata": {},
   "source": [
    "binary=OneHotEncoder(categories='auto')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 81,
   "id": "7ef00ce4",
   "metadata": {},
   "outputs": [
    {
     "ename": "AttributeError",
     "evalue": "'DataFrame' object has no attribute 'poutcome'",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mAttributeError\u001b[0m                            Traceback (most recent call last)",
      "\u001b[0;32m/var/folders/x_/bydwqxmj22b17_nc_zdgjlhc0000gn/T/ipykernel_28965/1593810313.py\u001b[0m in \u001b[0;36m?\u001b[0;34m()\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mbank\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mpoutcome\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mvalue_counts\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
      "\u001b[0;32m~/anaconda3/envs/notebook/lib/python3.10/site-packages/pandas/core/generic.py\u001b[0m in \u001b[0;36m?\u001b[0;34m(self, name)\u001b[0m\n\u001b[1;32m   6200\u001b[0m             \u001b[0;32mand\u001b[0m \u001b[0mname\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_accessors\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   6201\u001b[0m             \u001b[0;32mand\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_info_axis\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_can_hold_identifiers_and_holds_name\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mname\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   6202\u001b[0m         ):\n\u001b[1;32m   6203\u001b[0m             \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mname\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 6204\u001b[0;31m         \u001b[0;32mreturn\u001b[0m \u001b[0mobject\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m__getattribute__\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mname\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
      "\u001b[0;31mAttributeError\u001b[0m: 'DataFrame' object has no attribute 'poutcome'"
     ]
    }
   ],
   "source": [
    "bank.poutcome.value_counts()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d17637fe",
   "metadata": {},
   "source": [
    "poutcome=bank['poutcome']\n",
    "poutcome1=le.fit_transform(poutcome)\n",
    "p1=binary.fit_transform(poutcome1.reshape(-1, 1))\n",
    "p2=p1.toarray()\n",
    "poutcome_df=pd.DataFrame(p2,columns=['o','s','f'])\n",
    "poutcome_df"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f01e804c",
   "metadata": {},
   "source": [
    "#we need to concatenate education_df,marital_df,poutcome_df,job_df to our original data\n",
    "data_updated=pd.concat([bank,education_df,marital_df,poutcome_df,job_df],axis=1)\n",
    "data_updated"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4dbbb25f",
   "metadata": {},
   "source": [
    "#dropping unecessary columns-education,marital,poutcome,job\n",
    "data_updated.drop([\"education\",\"marital\",\"poutcome\",\"job\"],axis=1,inplace=True)\n",
    "data_updated"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "24df8bb1",
   "metadata": {},
   "source": [
    "data_updated.isnull().sum()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f9c0e669",
   "metadata": {},
   "source": [
    "data_updated"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 82,
   "id": "64e33605",
   "metadata": {},
   "outputs": [],
   "source": [
    "#dividing the data set into x and y.\n",
    "x_encoded=bank.drop('deposit_cat',axis=1)\n",
    "y=bank['deposit_cat']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 83,
   "id": "f7a885e2",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0        1\n",
       "1        1\n",
       "2        1\n",
       "3        1\n",
       "4        1\n",
       "        ..\n",
       "11157    0\n",
       "11158    0\n",
       "11159    0\n",
       "11160    0\n",
       "11161    0\n",
       "Name: deposit_cat, Length: 11162, dtype: int64"
      ]
     },
     "execution_count": 83,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 84,
   "id": "ce47ef3f",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>age</th>\n",
       "      <th>balance</th>\n",
       "      <th>duration</th>\n",
       "      <th>campaign</th>\n",
       "      <th>previous</th>\n",
       "      <th>recent_pdays</th>\n",
       "      <th>loan_cat</th>\n",
       "      <th>ldefault_cat</th>\n",
       "      <th>housing_cat</th>\n",
       "      <th>job_blue-collar</th>\n",
       "      <th>...</th>\n",
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       "      <th>poutcome_success</th>\n",
       "    </tr>\n",
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       "      <td>0.000100</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11157</th>\n",
       "      <td>33</td>\n",
       "      <td>1</td>\n",
       "      <td>257</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0.000100</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11158</th>\n",
       "      <td>39</td>\n",
       "      <td>733</td>\n",
       "      <td>83</td>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "      <td>0.000100</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11159</th>\n",
       "      <td>32</td>\n",
       "      <td>29</td>\n",
       "      <td>156</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>0.000100</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11160</th>\n",
       "      <td>43</td>\n",
       "      <td>0</td>\n",
       "      <td>9</td>\n",
       "      <td>2</td>\n",
       "      <td>5</td>\n",
       "      <td>0.005814</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11161</th>\n",
       "      <td>34</td>\n",
       "      <td>0</td>\n",
       "      <td>628</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0.000100</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>11162 rows × 23 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       age  balance  duration  campaign  previous  recent_pdays  loan_cat  \\\n",
       "0       59     2343      1042         1         0      0.000100         0   \n",
       "1       56       45      1467         1         0      0.000100         0   \n",
       "2       41     1270      1389         1         0      0.000100         0   \n",
       "3       55     2476       579         1         0      0.000100         0   \n",
       "4       54      184       673         2         0      0.000100         0   \n",
       "...    ...      ...       ...       ...       ...           ...       ...   \n",
       "11157   33        1       257         1         0      0.000100         0   \n",
       "11158   39      733        83         4         0      0.000100         0   \n",
       "11159   32       29       156         2         0      0.000100         0   \n",
       "11160   43        0         9         2         5      0.005814         1   \n",
       "11161   34        0       628         1         0      0.000100         0   \n",
       "\n",
       "       ldefault_cat  housing_cat  job_blue-collar  ...  job_self-employed  \\\n",
       "0                 0            1                0  ...                  0   \n",
       "1                 0            0                0  ...                  0   \n",
       "2                 0            1                0  ...                  0   \n",
       "3                 0            1                0  ...                  0   \n",
       "4                 0            0                0  ...                  0   \n",
       "...             ...          ...              ...  ...                ...   \n",
       "11157             0            1                1  ...                  0   \n",
       "11158             0            0                0  ...                  0   \n",
       "11159             0            0                0  ...                  0   \n",
       "11160             0            0                0  ...                  0   \n",
       "11161             0            0                0  ...                  0   \n",
       "\n",
       "       job_technician  job_white-collar  marital_divorced  marital_married  \\\n",
       "0                   0                 1                 0                1   \n",
       "1                   0                 1                 0                1   \n",
       "2                   1                 0                 0                1   \n",
       "3                   0                 0                 0                1   \n",
       "4                   0                 1                 0                1   \n",
       "...               ...               ...               ...              ...   \n",
       "11157               0                 0                 0                0   \n",
       "11158               0                 0                 0                1   \n",
       "11159               1                 0                 0                0   \n",
       "11160               1                 0                 0                1   \n",
       "11161               1                 0                 0                1   \n",
       "\n",
       "       education_primary  education_secondary  education_tertiary  \\\n",
       "0                      0                    1                   0   \n",
       "1                      0                    1                   0   \n",
       "2                      0                    1                   0   \n",
       "3                      0                    1                   0   \n",
       "4                      0                    0                   1   \n",
       "...                  ...                  ...                 ...   \n",
       "11157                  1                    0                   0   \n",
       "11158                  0                    1                   0   \n",
       "11159                  0                    1                   0   \n",
       "11160                  0                    1                   0   \n",
       "11161                  0                    1                   0   \n",
       "\n",
       "       poutcome_failure  poutcome_success  \n",
       "0                     0                 0  \n",
       "1                     0                 0  \n",
       "2                     0                 0  \n",
       "3                     0                 0  \n",
       "4                     0                 0  \n",
       "...                 ...               ...  \n",
       "11157                 0                 0  \n",
       "11158                 0                 0  \n",
       "11159                 0                 0  \n",
       "11160                 1                 0  \n",
       "11161                 0                 0  \n",
       "\n",
       "[11162 rows x 23 columns]"
      ]
     },
     "execution_count": 84,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x_encoded"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 85,
   "id": "3e8211ef",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{1: 5289, 0: 5873}"
      ]
     },
     "execution_count": 85,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "num_before=dict(Counter(y))\n",
    "num_before"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 86,
   "id": "93692791",
   "metadata": {},
   "outputs": [],
   "source": [
    "smote=SMOTE()#saving the instance"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 87,
   "id": "1b0e547c",
   "metadata": {},
   "outputs": [],
   "source": [
    "x_smote,y_smote=smote.fit_resample(x_encoded,y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 88,
   "id": "5b819a9e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{1: 5873, 0: 5873}"
      ]
     },
     "execution_count": 88,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "num_after=dict(Counter(y_smote))\n",
    "num_after"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 89,
   "id": "25ca3cd4",
   "metadata": {},
   "outputs": [],
   "source": [
    "after_smote=list(num_after.values())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 90,
   "id": "c909886b",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/souravghoshhansda/anaconda3/envs/notebook/lib/python3.10/site-packages/seaborn/_oldcore.py:1498: FutureWarning: is_categorical_dtype is deprecated and will be removed in a future version. Use isinstance(dtype, CategoricalDtype) instead\n",
      "  if pd.api.types.is_categorical_dtype(vector):\n",
      "/Users/souravghoshhansda/anaconda3/envs/notebook/lib/python3.10/site-packages/seaborn/_oldcore.py:1498: FutureWarning: is_categorical_dtype is deprecated and will be removed in a future version. Use isinstance(dtype, CategoricalDtype) instead\n",
      "  if pd.api.types.is_categorical_dtype(vector):\n",
      "/Users/souravghoshhansda/anaconda3/envs/notebook/lib/python3.10/site-packages/seaborn/_oldcore.py:1765: FutureWarning: unique with argument that is not not a Series, Index, ExtensionArray, or np.ndarray is deprecated and will raise in a future version.\n",
      "  order = pd.unique(vector)\n",
      "/Users/souravghoshhansda/anaconda3/envs/notebook/lib/python3.10/site-packages/seaborn/_oldcore.py:1498: FutureWarning: is_categorical_dtype is deprecated and will be removed in a future version. Use isinstance(dtype, CategoricalDtype) instead\n",
      "  if pd.api.types.is_categorical_dtype(vector):\n",
      "/Users/souravghoshhansda/anaconda3/envs/notebook/lib/python3.10/site-packages/seaborn/_oldcore.py:1498: FutureWarning: is_categorical_dtype is deprecated and will be removed in a future version. Use isinstance(dtype, CategoricalDtype) instead\n",
      "  if pd.api.types.is_categorical_dtype(vector):\n",
      "/Users/souravghoshhansda/anaconda3/envs/notebook/lib/python3.10/site-packages/seaborn/_oldcore.py:1498: FutureWarning: is_categorical_dtype is deprecated and will be removed in a future version. Use isinstance(dtype, CategoricalDtype) instead\n",
      "  if pd.api.types.is_categorical_dtype(vector):\n",
      "/Users/souravghoshhansda/anaconda3/envs/notebook/lib/python3.10/site-packages/seaborn/_oldcore.py:1765: FutureWarning: unique with argument that is not not a Series, Index, ExtensionArray, or np.ndarray is deprecated and will raise in a future version.\n",
      "  order = pd.unique(vector)\n",
      "/Users/souravghoshhansda/anaconda3/envs/notebook/lib/python3.10/site-packages/seaborn/_oldcore.py:1498: FutureWarning: is_categorical_dtype is deprecated and will be removed in a future version. Use isinstance(dtype, CategoricalDtype) instead\n",
      "  if pd.api.types.is_categorical_dtype(vector):\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1500x600 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "labels = [\"1\",\"0\"]\n",
    "plt.figure(figsize=(15,6))\n",
    "plt.subplot(1,2,1)\n",
    "sns.barplot(x=labels, y=list(num_before.values()))\n",
    "plt.title(\"Target - Numbers Before Balancing\")\n",
    "plt.subplot(1,2,2)\n",
    "sns.barplot(x=labels, y=after_smote)\n",
    "plt.title(\"Target - Numbers After Balancing\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 91,
   "id": "e6b0e27f",
   "metadata": {},
   "outputs": [],
   "source": [
    "#train-test split\n",
    "x_train,x_test,y_train,y_test=train_test_split(x_smote,y_smote,test_size=0.2,random_state=0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 92,
   "id": "0e2a7ba9",
   "metadata": {},
   "outputs": [],
   "source": [
    "#Feature Scaling on x data(x_train & x_test)\n",
    "scaler = MinMaxScaler()\n",
    "x_train = scaler.fit_transform(x_train)\n",
    "x_test = scaler.transform(x_test)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "63161a73",
   "metadata": {},
   "source": [
    "x_train,x_test,y_train,y_test=train_test_split(scaled_data,y,test_size=0.2,random_state=0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 93,
   "id": "bb7f68a0",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(array([[0.45454545, 0.09315646, 0.02010828, ..., 0.        , 0.        ,\n",
       "         0.        ],\n",
       "        [0.25974026, 0.09435374, 0.07218355, ..., 1.        , 0.        ,\n",
       "         1.        ],\n",
       "        [0.12987013, 0.09315646, 0.19721578, ..., 1.        , 0.        ,\n",
       "         0.        ],\n",
       "        ...,\n",
       "        [0.23376623, 0.09559184, 0.00309358, ..., 0.        , 0.        ,\n",
       "         0.        ],\n",
       "        [0.16883117, 0.09389116, 0.05774684, ..., 0.        , 0.        ,\n",
       "         0.        ],\n",
       "        [0.54545455, 0.1147619 , 0.18716164, ..., 0.        , 0.        ,\n",
       "         0.        ]]),\n",
       " array([[0.24675325, 0.10008163, 0.30394432, ..., 0.        , 0.        ,\n",
       "         0.        ],\n",
       "        [0.22077922, 0.15231293, 0.27635989, ..., 1.        , 1.        ,\n",
       "         0.        ],\n",
       "        [0.25974026, 0.10084354, 0.02345965, ..., 0.        , 0.        ,\n",
       "         0.        ],\n",
       "        ...,\n",
       "        [0.07792208, 0.0914966 , 0.2572828 , ..., 0.        , 0.        ,\n",
       "         0.        ],\n",
       "        [0.24675325, 0.09767347, 0.0484661 , ..., 1.        , 1.        ,\n",
       "         0.        ],\n",
       "        [0.15584416, 0.09506122, 0.19541119, ..., 0.        , 0.        ,\n",
       "         0.        ]]),\n",
       " 1347     1\n",
       " 4091     1\n",
       " 463      1\n",
       " 9745     0\n",
       " 5083     1\n",
       "         ..\n",
       " 4859     1\n",
       " 3264     1\n",
       " 9845     0\n",
       " 10799    0\n",
       " 2732     1\n",
       " Name: deposit_cat, Length: 9396, dtype: int64,\n",
       " 11475    1\n",
       " 8734     0\n",
       " 6288     0\n",
       " 9659     0\n",
       " 8056     0\n",
       "         ..\n",
       " 4439     1\n",
       " 9924     0\n",
       " 4153     1\n",
       " 6800     0\n",
       " 1999     1\n",
       " Name: deposit_cat, Length: 2350, dtype: int64)"
      ]
     },
     "execution_count": 93,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x_train,x_test,y_train,y_test"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 94,
   "id": "95fd503c",
   "metadata": {},
   "outputs": [],
   "source": [
    "models = []\n",
    "models.append(('Logistic Regression' ,LogisticRegression(random_state=42)))\n",
    "models.append(('Naive bayes', GaussianNB()))\n",
    "#models.append(('KNN', KNeighborsClassifier()))\n",
    "models.append(('CART', DecisionTreeClassifier(random_state = 42)))\n",
    "models.append(('Random Forest', RandomForestClassifier(random_state = 42)))\n",
    "models.append(('SVM', SVC(gamma='auto', random_state = 42)))\n",
    "models.append(('XGBoost', XGBClassifier(random_state = 42)))\n",
    "models.append(('GradientBoosting', GradientBoostingClassifier(random_state = 42)))\n",
    "results = []\n",
    "names = []"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 95,
   "id": "f5de6de1",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Logistic Regression : 0.7991489361702128\n",
      "Naive bayes : 0.7451063829787234\n",
      "CART : 0.754468085106383\n",
      "Random Forest : 0.8187234042553192\n",
      "SVM : 0.7778723404255319\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/souravghoshhansda/anaconda3/envs/notebook/lib/python3.10/site-packages/xgboost/data.py:427: FutureWarning: is_sparse is deprecated and will be removed in a future version. Check `isinstance(dtype, pd.SparseDtype)` instead.\n",
      "  if is_sparse(data):\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "XGBoost : 0.8212765957446808\n",
      "GradientBoosting : 0.816595744680851\n"
     ]
    }
   ],
   "source": [
    "for name, model in models:\n",
    "        model.fit(x_train, y_train)\n",
    "        y_pred = model.predict(x_test)\n",
    "        accuracy = accuracy_score(y_test, y_pred)\n",
    "        print(\"{} : {}\".format(name,accuracy))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 96,
   "id": "ff8cf693",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Precision_score\n",
      "Logistic Regression : 81.64%\n",
      "Precision_score\n",
      "Naive bayes : 77.77%\n",
      "Precision_score\n",
      "CART : 74.61%\n",
      "Precision_score\n",
      "Random Forest : 80.59%\n",
      "Precision_score\n",
      "SVM : 80.04%\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/souravghoshhansda/anaconda3/envs/notebook/lib/python3.10/site-packages/xgboost/data.py:427: FutureWarning: is_sparse is deprecated and will be removed in a future version. Check `isinstance(dtype, pd.SparseDtype)` instead.\n",
      "  if is_sparse(data):\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Precision_score\n",
      "XGBoost : 81.11%\n",
      "Precision_score\n",
      "GradientBoosting : 80.51%\n"
     ]
    }
   ],
   "source": [
    "for name, model in models:\n",
    "        model.fit(x_train, y_train)\n",
    "        y_pred = model.predict(x_test)\n",
    "        precision = precision_score(y_test, y_pred)*100\n",
    "        print('Precision_score')\n",
    "        print(\"{} : {:.2f}%\".format(name,precision))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 97,
   "id": "dd05b09d",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Recall_score\n",
      "Logistic Regression : 76.11%\n",
      "Recall_score\n",
      "Naive bayes : 67.16%\n",
      "Recall_score\n",
      "CART : 75.59%\n",
      "Recall_score\n",
      "Random Forest : 82.97%\n",
      "Recall_score\n",
      "SVM : 72.81%\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/souravghoshhansda/anaconda3/envs/notebook/lib/python3.10/site-packages/xgboost/data.py:427: FutureWarning: is_sparse is deprecated and will be removed in a future version. Check `isinstance(dtype, pd.SparseDtype)` instead.\n",
      "  if is_sparse(data):\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Recall_score\n",
      "XGBoost : 82.80%\n",
      "Recall_score\n",
      "GradientBoosting : 82.54%\n"
     ]
    }
   ],
   "source": [
    "for name, model in models:\n",
    "        model.fit(x_train, y_train)\n",
    "        y_pred = model.predict(x_test)\n",
    "        recall = recall_score(y_test, y_pred)*100\n",
    "        print('Recall_score')\n",
    "        print(\"{} : {:.2f}%\".format(name,recall))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 98,
   "id": "43ffc5dd",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "f1_score\n",
      "Logistic Regression : 78.78%\n",
      "f1_score\n",
      "Naive bayes : 72.07%\n",
      "f1_score\n",
      "CART : 75.10%\n",
      "f1_score\n",
      "Random Forest : 81.76%\n",
      "f1_score\n",
      "SVM : 76.25%\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/souravghoshhansda/anaconda3/envs/notebook/lib/python3.10/site-packages/xgboost/data.py:427: FutureWarning: is_sparse is deprecated and will be removed in a future version. Check `isinstance(dtype, pd.SparseDtype)` instead.\n",
      "  if is_sparse(data):\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "f1_score\n",
      "XGBoost : 81.94%\n",
      "f1_score\n",
      "GradientBoosting : 81.51%\n"
     ]
    }
   ],
   "source": [
    "for name, model in models:\n",
    "        model.fit(x_train, y_train)\n",
    "        y_pred = model.predict(x_test)\n",
    "        f1 = f1_score(y_test, y_pred)*100\n",
    "        print('f1_score')\n",
    "        print(\"{} : {:.2f}%\".format(name,f1))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 99,
   "id": "93a8d04b",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Logistic Regression : [[1002  197]\n",
      " [ 275  876]]\n",
      "----------------------------------\n",
      "Naive bayes : [[978 221]\n",
      " [378 773]]\n",
      "----------------------------------\n",
      "CART : [[903 296]\n",
      " [281 870]]\n",
      "----------------------------------\n",
      "Random Forest : [[969 230]\n",
      " [196 955]]\n",
      "----------------------------------\n",
      "SVM : [[990 209]\n",
      " [313 838]]\n",
      "----------------------------------\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/souravghoshhansda/anaconda3/envs/notebook/lib/python3.10/site-packages/xgboost/data.py:427: FutureWarning: is_sparse is deprecated and will be removed in a future version. Check `isinstance(dtype, pd.SparseDtype)` instead.\n",
      "  if is_sparse(data):\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "XGBoost : [[977 222]\n",
      " [198 953]]\n",
      "----------------------------------\n",
      "GradientBoosting : [[969 230]\n",
      " [201 950]]\n",
      "----------------------------------\n"
     ]
    }
   ],
   "source": [
    "for name, model in models:\n",
    "        model.fit(x_train, y_train)\n",
    "        y_pred = model.predict(x_test)\n",
    "        confusion = confusion_matrix(y_test, y_pred)\n",
    "        print(\"{} : {}\".format(name,confusion))\n",
    "        print('----------------------------------')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 100,
   "id": "27dacdc3",
   "metadata": {},
   "outputs": [],
   "source": [
    "#applying decision tree with hyper parameter tuning\n",
    "dt_classifier = DecisionTreeClassifier()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 101,
   "id": "386117eb",
   "metadata": {},
   "outputs": [],
   "source": [
    "param_grid = {\n",
    "    'max_depth': [None, 5, 10, 15],            # Maximum depth of the tree\n",
    "    'min_samples_split': [2, 5, 10],           # Minimum number of samples required to split an internal node\n",
    "    'min_samples_leaf': [1, 2, 4],             # Minimum number of samples required to be at a leaf node\n",
    "    'max_features': [None, 'sqrt', 'log2'],   # Number of features to consider at each split\n",
    "    'criterion': ['gini', 'entropy'],         # Split criterion: Gini impurity or entropy\n",
    "    'splitter': ['best', 'random'],            # Strategy to choose the split at each node\n",
    "    'max_leaf_nodes': [None, 10, 20, 30]       # Maximum number of leaf nodes in the tree\n",
    "}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 102,
   "id": "757419dc",
   "metadata": {},
   "outputs": [],
   "source": [
    "grid_search = GridSearchCV(dt_classifier, param_grid, cv=5, scoring='accuracy')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 103,
   "id": "e5512091",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "             param_grid={&#x27;criterion&#x27;: [&#x27;gini&#x27;, &#x27;entropy&#x27;],\n",
       "                         &#x27;max_depth&#x27;: [None, 5, 10, 15],\n",
       "                         &#x27;max_features&#x27;: [None, &#x27;sqrt&#x27;, &#x27;log2&#x27;],\n",
       "                         &#x27;max_leaf_nodes&#x27;: [None, 10, 20, 30],\n",
       "                         &#x27;min_samples_leaf&#x27;: [1, 2, 4],\n",
       "                         &#x27;min_samples_split&#x27;: [2, 5, 10],\n",
       "                         &#x27;splitter&#x27;: [&#x27;best&#x27;, &#x27;random&#x27;]},\n",
       "             scoring=&#x27;accuracy&#x27;)</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item sk-dashed-wrapped\"><div class=\"sk-label-container\"><div class=\"sk-label sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-1\" type=\"checkbox\" ><label for=\"sk-estimator-id-1\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">GridSearchCV</label><div class=\"sk-toggleable__content\"><pre>GridSearchCV(cv=5, estimator=DecisionTreeClassifier(),\n",
       "             param_grid={&#x27;criterion&#x27;: [&#x27;gini&#x27;, &#x27;entropy&#x27;],\n",
       "                         &#x27;max_depth&#x27;: [None, 5, 10, 15],\n",
       "                         &#x27;max_features&#x27;: [None, &#x27;sqrt&#x27;, &#x27;log2&#x27;],\n",
       "                         &#x27;max_leaf_nodes&#x27;: [None, 10, 20, 30],\n",
       "                         &#x27;min_samples_leaf&#x27;: [1, 2, 4],\n",
       "                         &#x27;min_samples_split&#x27;: [2, 5, 10],\n",
       "                         &#x27;splitter&#x27;: [&#x27;best&#x27;, &#x27;random&#x27;]},\n",
       "             scoring=&#x27;accuracy&#x27;)</pre></div></div></div><div class=\"sk-parallel\"><div class=\"sk-parallel-item\"><div class=\"sk-item\"><div class=\"sk-label-container\"><div class=\"sk-label sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-2\" type=\"checkbox\" ><label for=\"sk-estimator-id-2\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">estimator: DecisionTreeClassifier</label><div class=\"sk-toggleable__content\"><pre>DecisionTreeClassifier()</pre></div></div></div><div class=\"sk-serial\"><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-3\" type=\"checkbox\" ><label for=\"sk-estimator-id-3\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">DecisionTreeClassifier</label><div class=\"sk-toggleable__content\"><pre>DecisionTreeClassifier()</pre></div></div></div></div></div></div></div></div></div></div>"
      ],
      "text/plain": [
       "GridSearchCV(cv=5, estimator=DecisionTreeClassifier(),\n",
       "             param_grid={'criterion': ['gini', 'entropy'],\n",
       "                         'max_depth': [None, 5, 10, 15],\n",
       "                         'max_features': [None, 'sqrt', 'log2'],\n",
       "                         'max_leaf_nodes': [None, 10, 20, 30],\n",
       "                         'min_samples_leaf': [1, 2, 4],\n",
       "                         'min_samples_split': [2, 5, 10],\n",
       "                         'splitter': ['best', 'random']},\n",
       "             scoring='accuracy')"
      ]
     },
     "execution_count": 103,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "grid_search.fit(x_train, y_train) "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 104,
   "id": "ee68de58",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'criterion': 'gini',\n",
       " 'max_depth': None,\n",
       " 'max_features': None,\n",
       " 'max_leaf_nodes': 30,\n",
       " 'min_samples_leaf': 1,\n",
       " 'min_samples_split': 2,\n",
       " 'splitter': 'best'}"
      ]
     },
     "execution_count": 104,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "best_params = grid_search.best_params_\n",
    "best_params"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 105,
   "id": "c144aa00",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style>#sk-container-id-2 {color: black;}#sk-container-id-2 pre{padding: 0;}#sk-container-id-2 div.sk-toggleable {background-color: white;}#sk-container-id-2 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-2 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-2 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-2 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-2 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-2 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-2 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-2 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-2 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-2 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-2 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-2 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-2 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-2 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-2 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-2 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-2 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-2 div.sk-item {position: relative;z-index: 1;}#sk-container-id-2 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-2 div.sk-item::before, #sk-container-id-2 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-2 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-2 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-2 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-2 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-2 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-2 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-2 div.sk-label-container {text-align: center;}#sk-container-id-2 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-2 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-2\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>DecisionTreeClassifier(max_leaf_nodes=30)</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-4\" type=\"checkbox\" checked><label for=\"sk-estimator-id-4\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">DecisionTreeClassifier</label><div class=\"sk-toggleable__content\"><pre>DecisionTreeClassifier(max_leaf_nodes=30)</pre></div></div></div></div></div>"
      ],
      "text/plain": [
       "DecisionTreeClassifier(max_leaf_nodes=30)"
      ]
     },
     "execution_count": 105,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#we are creating a new decision tree classifier with the best hyper parameters\n",
    "best_dt_classifier = DecisionTreeClassifier(**best_params)\n",
    "best_dt_classifier.fit(x_train,y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 106,
   "id": "c7d89922",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([1, 1, 0, ..., 1, 0, 1])"
      ]
     },
     "execution_count": 106,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_pred_tune=best_dt_classifier.predict(x_test)\n",
    "y_pred_tune"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 107,
   "id": "626d2aae",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.8051063829787234"
      ]
     },
     "execution_count": 107,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "accuracy_decision_tune= accuracy_score(y_test, y_pred_tune)\n",
    "accuracy_decision_tune"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 108,
   "id": "18f0b08c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.8265219242230737"
      ]
     },
     "execution_count": 108,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_score_decision_tune=best_dt_classifier.score(x_train,y_train)\n",
    "train_score_decision_tune"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 109,
   "id": "214d80b5",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.8051063829787234"
      ]
     },
     "execution_count": 109,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "test_score_decision_tune=best_dt_classifier.score(x_test,y_test)\n",
    "test_score_decision_tune"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 110,
   "id": "bdb95762",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "83.49261511728932"
      ]
     },
     "execution_count": 110,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#decision tree with hyper parameter tuning model gives an accuracy score of 78.68% with a good fit hence finding out the evaluation metrics for this model.\n",
    "recall_decision = recall_score(y_test, y_pred_tune)*100\n",
    "recall_decision"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 111,
   "id": "a6e64882",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "78.19365337672905"
      ]
     },
     "execution_count": 111,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "precision_decision = precision_score(y_test, y_pred_tune)*100\n",
    "precision_decision"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 112,
   "id": "b2a1a488",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "80.75630252100841"
      ]
     },
     "execution_count": 112,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "f1_decision = f1_score(y_test, y_pred_tune)*100\n",
    "f1_decision"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 113,
   "id": "7f98a3db",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[931, 268],\n",
       "       [190, 961]])"
      ]
     },
     "execution_count": 113,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "confusion_decision = confusion_matrix(y_test, y_pred_tune)\n",
    "confusion_decision"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 114,
   "id": "64ceda06",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.83      0.78      0.80      1199\n",
      "           1       0.78      0.83      0.81      1151\n",
      "\n",
      "    accuracy                           0.81      2350\n",
      "   macro avg       0.81      0.81      0.81      2350\n",
      "weighted avg       0.81      0.81      0.81      2350\n",
      "\n"
     ]
    }
   ],
   "source": [
    "classification_report__decision=classification_report(y_test, y_pred_tune)\n",
    "print(classification_report__decision)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "20a2be8c",
   "metadata": {},
   "source": [
    "Because there is no overfitting scenario and decision tree with hyper-parameter tuning give an optimized accuracy score of 79.89% along with stable values of the evaluation metrics, so this is our concluding model."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 115,
   "id": "d8228f09",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style>#sk-container-id-3 {color: black;}#sk-container-id-3 pre{padding: 0;}#sk-container-id-3 div.sk-toggleable {background-color: white;}#sk-container-id-3 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-3 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-3 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-3 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-3 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-3 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-3 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-3 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-3 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-3 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-3 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-3 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-3 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-3 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-3 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-3 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-3 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-3 div.sk-item {position: relative;z-index: 1;}#sk-container-id-3 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-3 div.sk-item::before, #sk-container-id-3 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-3 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-3 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-3 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-3 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-3 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-3 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-3 div.sk-label-container {text-align: center;}#sk-container-id-3 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-3 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-3\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>RandomForestClassifier(random_state=0)</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-5\" type=\"checkbox\" checked><label for=\"sk-estimator-id-5\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">RandomForestClassifier</label><div class=\"sk-toggleable__content\"><pre>RandomForestClassifier(random_state=0)</pre></div></div></div></div></div>"
      ],
      "text/plain": [
       "RandomForestClassifier(random_state=0)"
      ]
     },
     "execution_count": 115,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#applying default random forest\n",
    "rf=RandomForestClassifier(random_state=0)\n",
    "rf.fit(x_train,y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 116,
   "id": "6164290f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([1, 1, 0, ..., 1, 1, 1])"
      ]
     },
     "execution_count": 116,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_pred_rf=rf.predict(x_test)\n",
    "y_pred_rf"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 117,
   "id": "7ef715e0",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.8123404255319149"
      ]
     },
     "execution_count": 117,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "accuracy_rf= accuracy_score(y_test, y_pred_rf)\n",
    "accuracy_rf"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 118,
   "id": "41ab44be",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.8123404255319149"
      ]
     },
     "execution_count": 118,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "test_score_rf=rf.score(x_test,y_test)\n",
    "test_score_rf"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 119,
   "id": "58a2c3f9",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.9998935717326521"
      ]
     },
     "execution_count": 119,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_score_rf=rf.score(x_train,y_train)\n",
    "train_score_rf"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 120,
   "id": "b8d6d0c6",
   "metadata": {},
   "outputs": [],
   "source": [
    "rf_tuned=RandomForestClassifier(random_state=0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 121,
   "id": "85e04fe8",
   "metadata": {},
   "outputs": [],
   "source": [
    "param_grid = {\n",
    "    'max_depth': [2,3,5],            # Maximum depth of the tree\n",
    "    'min_samples_split': [2, 3, 6],           # Minimum number of samples required to split an internal node\n",
    "    'min_samples_leaf': [1, 2, 4],             # Minimum number of samples required to be at a leaf node\n",
    "    'max_features': [None, 'sqrt', 'log2'],   # Number of features to consider at each split\n",
    "    'criterion': ['gini', 'entropy'],         # Split criterion: Gini impurity or entropy\n",
    "    'max_leaf_nodes': [None, 10, 20, 30]       # Maximum number of leaf nodes in the tree\n",
    "}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 122,
   "id": "2b4c1d5c",
   "metadata": {},
   "outputs": [],
   "source": [
    "rf_gridsearch=GridSearchCV(rf_tuned, param_grid,cv=5, scoring='accuracy')\n",
    "#['bootstrap', 'ccp_alpha', 'class_weight', 'criterion', 'max_depth', 'max_features', 'max_leaf_nodes', 'max_samples', 'min_impurity_decrease', 'min_samples_leaf', 'min_samples_split', 'min_weight_fraction_leaf', 'n_estimators', 'n_jobs', 'oob_score', 'random_state', 'verbose', 'warm_start']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 123,
   "id": "8a69b887",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style>#sk-container-id-4 {color: black;}#sk-container-id-4 pre{padding: 0;}#sk-container-id-4 div.sk-toggleable {background-color: white;}#sk-container-id-4 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-4 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-4 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-4 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-4 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-4 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-4 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-4 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-4 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-4 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-4 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-4 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-4 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-4 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-4 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-4 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-4 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-4 div.sk-item {position: relative;z-index: 1;}#sk-container-id-4 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-4 div.sk-item::before, #sk-container-id-4 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-4 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-4 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-4 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-4 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-4 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-4 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-4 div.sk-label-container {text-align: center;}#sk-container-id-4 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-4 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-4\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>GridSearchCV(cv=5, estimator=RandomForestClassifier(random_state=0),\n",
       "             param_grid={&#x27;criterion&#x27;: [&#x27;gini&#x27;, &#x27;entropy&#x27;],\n",
       "                         &#x27;max_depth&#x27;: [2, 3, 5],\n",
       "                         &#x27;max_features&#x27;: [None, &#x27;sqrt&#x27;, &#x27;log2&#x27;],\n",
       "                         &#x27;max_leaf_nodes&#x27;: [None, 10, 20, 30],\n",
       "                         &#x27;min_samples_leaf&#x27;: [1, 2, 4],\n",
       "                         &#x27;min_samples_split&#x27;: [2, 3, 6]},\n",
       "             scoring=&#x27;accuracy&#x27;)</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item sk-dashed-wrapped\"><div class=\"sk-label-container\"><div class=\"sk-label sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-6\" type=\"checkbox\" ><label for=\"sk-estimator-id-6\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">GridSearchCV</label><div class=\"sk-toggleable__content\"><pre>GridSearchCV(cv=5, estimator=RandomForestClassifier(random_state=0),\n",
       "             param_grid={&#x27;criterion&#x27;: [&#x27;gini&#x27;, &#x27;entropy&#x27;],\n",
       "                         &#x27;max_depth&#x27;: [2, 3, 5],\n",
       "                         &#x27;max_features&#x27;: [None, &#x27;sqrt&#x27;, &#x27;log2&#x27;],\n",
       "                         &#x27;max_leaf_nodes&#x27;: [None, 10, 20, 30],\n",
       "                         &#x27;min_samples_leaf&#x27;: [1, 2, 4],\n",
       "                         &#x27;min_samples_split&#x27;: [2, 3, 6]},\n",
       "             scoring=&#x27;accuracy&#x27;)</pre></div></div></div><div class=\"sk-parallel\"><div class=\"sk-parallel-item\"><div class=\"sk-item\"><div class=\"sk-label-container\"><div class=\"sk-label sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-7\" type=\"checkbox\" ><label for=\"sk-estimator-id-7\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">estimator: RandomForestClassifier</label><div class=\"sk-toggleable__content\"><pre>RandomForestClassifier(random_state=0)</pre></div></div></div><div class=\"sk-serial\"><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-8\" type=\"checkbox\" ><label for=\"sk-estimator-id-8\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">RandomForestClassifier</label><div class=\"sk-toggleable__content\"><pre>RandomForestClassifier(random_state=0)</pre></div></div></div></div></div></div></div></div></div></div>"
      ],
      "text/plain": [
       "GridSearchCV(cv=5, estimator=RandomForestClassifier(random_state=0),\n",
       "             param_grid={'criterion': ['gini', 'entropy'],\n",
       "                         'max_depth': [2, 3, 5],\n",
       "                         'max_features': [None, 'sqrt', 'log2'],\n",
       "                         'max_leaf_nodes': [None, 10, 20, 30],\n",
       "                         'min_samples_leaf': [1, 2, 4],\n",
       "                         'min_samples_split': [2, 3, 6]},\n",
       "             scoring='accuracy')"
      ]
     },
     "execution_count": 123,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "rf_gridsearch.fit(x_train, y_train) "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 124,
   "id": "370c7cb3",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'criterion': 'gini',\n",
       " 'max_depth': 5,\n",
       " 'max_features': 'sqrt',\n",
       " 'max_leaf_nodes': 30,\n",
       " 'min_samples_leaf': 4,\n",
       " 'min_samples_split': 2}"
      ]
     },
     "execution_count": 124,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "best_params_rf = rf_gridsearch.best_params_\n",
    "best_params_rf"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 125,
   "id": "d5ec57ac",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style>#sk-container-id-5 {color: black;}#sk-container-id-5 pre{padding: 0;}#sk-container-id-5 div.sk-toggleable {background-color: white;}#sk-container-id-5 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-5 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-5 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-5 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-5 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-5 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-5 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-5 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-5 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-5 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-5 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-5 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-5 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-5 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-5 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-5 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-5 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-5 div.sk-item {position: relative;z-index: 1;}#sk-container-id-5 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-5 div.sk-item::before, #sk-container-id-5 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-5 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-5 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-5 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-5 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-5 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-5 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-5 div.sk-label-container {text-align: center;}#sk-container-id-5 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-5 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-5\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>RandomForestClassifier(max_depth=5, max_leaf_nodes=30, min_samples_leaf=4)</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-9\" type=\"checkbox\" checked><label for=\"sk-estimator-id-9\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">RandomForestClassifier</label><div class=\"sk-toggleable__content\"><pre>RandomForestClassifier(max_depth=5, max_leaf_nodes=30, min_samples_leaf=4)</pre></div></div></div></div></div>"
      ],
      "text/plain": [
       "RandomForestClassifier(max_depth=5, max_leaf_nodes=30, min_samples_leaf=4)"
      ]
     },
     "execution_count": 125,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#we are creating a new random classifier with the best hyper parameters\n",
    "best_rf_classifier = RandomForestClassifier(**best_params_rf)\n",
    "best_rf_classifier.fit(x_train,y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 126,
   "id": "fb2bb047",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([1, 1, 0, ..., 1, 1, 1])"
      ]
     },
     "execution_count": 126,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_pred_rf_tune=best_rf_classifier.predict(x_test)\n",
    "y_pred_rf_tune"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 127,
   "id": "17052ea1",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.8068085106382978"
      ]
     },
     "execution_count": 127,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "accuracy_rf_tune= accuracy_score(y_test, y_pred_rf_tune)\n",
    "accuracy_rf_tune"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 128,
   "id": "2140833f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.8254576415495956"
      ]
     },
     "execution_count": 128,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_score_rf_tune=best_rf_classifier.score(x_train,y_train)\n",
    "train_score_rf_tune"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 129,
   "id": "3a1cca1b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.8068085106382978"
      ]
     },
     "execution_count": 129,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "test_score_rf_tune=best_rf_classifier.score(x_test,y_test)\n",
    "test_score_rf_tune"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 130,
   "id": "2d8c2c97",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "81.05994787141616"
      ]
     },
     "execution_count": 130,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#random forest with hyper parameter tuning model gives an accuracy score of 78.68% with a good fit hence finding out the evaluation metrics for this model.\n",
    "recall_rf = recall_score(y_test, y_pred_rf_tune)*100\n",
    "recall_rf"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 131,
   "id": "1e075ba8",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "79.81180496150556"
      ]
     },
     "execution_count": 131,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "precision_rf = precision_score(y_test, y_pred_rf_tune)*100\n",
    "precision_rf"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 132,
   "id": "3213f4b0",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "80.43103448275862"
      ]
     },
     "execution_count": 132,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "f1_rf = f1_score(y_test, y_pred_rf_tune)*100\n",
    "f1_rf"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 133,
   "id": "8da543ab",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[963, 236],\n",
       "       [218, 933]])"
      ]
     },
     "execution_count": 133,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "confusion_rf = confusion_matrix(y_test, y_pred_rf_tune)\n",
    "confusion_rf"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 134,
   "id": "ee7fcbd5",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.82      0.80      0.81      1199\n",
      "           1       0.80      0.81      0.80      1151\n",
      "\n",
      "    accuracy                           0.81      2350\n",
      "   macro avg       0.81      0.81      0.81      2350\n",
      "weighted avg       0.81      0.81      0.81      2350\n",
      "\n"
     ]
    }
   ],
   "source": [
    "classification_report__random=classification_report(y_test, y_pred_rf_tune)\n",
    "print(classification_report__random)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a3b97a33",
   "metadata": {},
   "source": [
    "the random forest gives accuracy:0.8029556650246306,recall:79.1828793774319,f1-score:78.72340425531915,precision:78.26923076923077.\n",
    "for 0 the f1-score is 0.82 and for 1 the f1-score is 0.79.\n",
    "\n",
    "\n",
    "We will be trying svm and knn models further."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f64b7fd4",
   "metadata": {},
   "source": [
    "can be done with smote"
   ]
  }
 ],
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