titanic_data_random_forest1.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "5745cb3a",
   "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>survived</th>\n",
       "      <th>pclass</th>\n",
       "      <th>sex</th>\n",
       "      <th>age</th>\n",
       "      <th>sibsp</th>\n",
       "      <th>parch</th>\n",
       "      <th>fare</th>\n",
       "      <th>embarked</th>\n",
       "      <th>class</th>\n",
       "      <th>who</th>\n",
       "      <th>adult_male</th>\n",
       "      <th>deck</th>\n",
       "      <th>embark_town</th>\n",
       "      <th>alive</th>\n",
       "      <th>alone</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>male</td>\n",
       "      <td>22.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>7.2500</td>\n",
       "      <td>S</td>\n",
       "      <td>Third</td>\n",
       "      <td>man</td>\n",
       "      <td>True</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Southampton</td>\n",
       "      <td>no</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>female</td>\n",
       "      <td>38.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>71.2833</td>\n",
       "      <td>C</td>\n",
       "      <td>First</td>\n",
       "      <td>woman</td>\n",
       "      <td>False</td>\n",
       "      <td>C</td>\n",
       "      <td>Cherbourg</td>\n",
       "      <td>yes</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>female</td>\n",
       "      <td>26.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>7.9250</td>\n",
       "      <td>S</td>\n",
       "      <td>Third</td>\n",
       "      <td>woman</td>\n",
       "      <td>False</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Southampton</td>\n",
       "      <td>yes</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>female</td>\n",
       "      <td>35.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>53.1000</td>\n",
       "      <td>S</td>\n",
       "      <td>First</td>\n",
       "      <td>woman</td>\n",
       "      <td>False</td>\n",
       "      <td>C</td>\n",
       "      <td>Southampton</td>\n",
       "      <td>yes</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>male</td>\n",
       "      <td>35.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>8.0500</td>\n",
       "      <td>S</td>\n",
       "      <td>Third</td>\n",
       "      <td>man</td>\n",
       "      <td>True</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Southampton</td>\n",
       "      <td>no</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   survived  pclass     sex   age  sibsp  parch     fare embarked  class  \\\n",
       "0         0       3    male  22.0      1      0   7.2500        S  Third   \n",
       "1         1       1  female  38.0      1      0  71.2833        C  First   \n",
       "2         1       3  female  26.0      0      0   7.9250        S  Third   \n",
       "3         1       1  female  35.0      1      0  53.1000        S  First   \n",
       "4         0       3    male  35.0      0      0   8.0500        S  Third   \n",
       "\n",
       "     who  adult_male deck  embark_town alive  alone  \n",
       "0    man        True  NaN  Southampton    no  False  \n",
       "1  woman       False    C    Cherbourg   yes  False  \n",
       "2  woman       False  NaN  Southampton   yes   True  \n",
       "3  woman       False    C  Southampton   yes  False  \n",
       "4    man        True  NaN  Southampton    no   True  "
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import pandas as pd# It is used for data manipulation for data cleaning purposes,even it is helpful to treat missing values\n",
    "import seaborn as sns# visualisation library based on matplotlib\n",
    "from scipy.stats import skew \n",
    "from scipy.stats import kurtosis\n",
    "from sklearn.preprocessing import LabelEncoder\n",
    "from sklearn.preprocessing import OneHotEncoder\n",
    "from sklearn.preprocessing import MinMaxScaler\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.linear_model import LogisticRegression\n",
    "from sklearn import metrics\n",
    "from sklearn.metrics import accuracy_score,precision_score,recall_score,f1_score,confusion_matrix,classification_report\n",
    "from sklearn.neighbors import KNeighborsClassifier  \n",
    "from sklearn.svm import SVC\n",
    "from sklearn.naive_bayes import GaussianNB\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",
    "import numpy as np\n",
    "# Load the Titanic dataset\n",
    "titanic_data = sns.load_dataset(\"titanic\")\n",
    "# Display the first few rows of the dataset\n",
    "titanic_data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "b60172c0",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "\n",
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       "\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>survived</th>\n",
       "      <th>pclass</th>\n",
       "      <th>age</th>\n",
       "      <th>sibsp</th>\n",
       "      <th>parch</th>\n",
       "      <th>fare</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>891.000000</td>\n",
       "      <td>891.000000</td>\n",
       "      <td>714.000000</td>\n",
       "      <td>891.000000</td>\n",
       "      <td>891.000000</td>\n",
       "      <td>891.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>0.383838</td>\n",
       "      <td>2.308642</td>\n",
       "      <td>29.699118</td>\n",
       "      <td>0.523008</td>\n",
       "      <td>0.381594</td>\n",
       "      <td>32.204208</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>0.486592</td>\n",
       "      <td>0.836071</td>\n",
       "      <td>14.526497</td>\n",
       "      <td>1.102743</td>\n",
       "      <td>0.806057</td>\n",
       "      <td>49.693429</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.420000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>20.125000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>7.910400</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>28.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>14.454200</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>38.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>31.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>80.000000</td>\n",
       "      <td>8.000000</td>\n",
       "      <td>6.000000</td>\n",
       "      <td>512.329200</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         survived      pclass         age       sibsp       parch        fare\n",
       "count  891.000000  891.000000  714.000000  891.000000  891.000000  891.000000\n",
       "mean     0.383838    2.308642   29.699118    0.523008    0.381594   32.204208\n",
       "std      0.486592    0.836071   14.526497    1.102743    0.806057   49.693429\n",
       "min      0.000000    1.000000    0.420000    0.000000    0.000000    0.000000\n",
       "25%      0.000000    2.000000   20.125000    0.000000    0.000000    7.910400\n",
       "50%      0.000000    3.000000   28.000000    0.000000    0.000000   14.454200\n",
       "75%      1.000000    3.000000   38.000000    1.000000    0.000000   31.000000\n",
       "max      1.000000    3.000000   80.000000    8.000000    6.000000  512.329200"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "titanic_data.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "6569533a",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "survived : [0 1]\n",
      "survived : survived\n",
      "0    549\n",
      "1    342\n",
      "Name: count, dtype: int64\n",
      "====================================\n",
      "pclass : [3 1 2]\n",
      "pclass : pclass\n",
      "3    491\n",
      "1    216\n",
      "2    184\n",
      "Name: count, dtype: int64\n",
      "====================================\n",
      "sex : ['male' 'female']\n",
      "sex : sex\n",
      "male      577\n",
      "female    314\n",
      "Name: count, dtype: int64\n",
      "====================================\n",
      "sibsp : [1 0 3 4 2 5 8]\n",
      "sibsp : sibsp\n",
      "0    608\n",
      "1    209\n",
      "2     28\n",
      "4     18\n",
      "3     16\n",
      "8      7\n",
      "5      5\n",
      "Name: count, dtype: int64\n",
      "====================================\n",
      "parch : [0 1 2 5 3 4 6]\n",
      "parch : parch\n",
      "0    678\n",
      "1    118\n",
      "2     80\n",
      "5      5\n",
      "3      5\n",
      "4      4\n",
      "6      1\n",
      "Name: count, dtype: int64\n",
      "====================================\n",
      "embarked : ['S' 'C' 'Q' nan]\n",
      "embarked : embarked\n",
      "S    644\n",
      "C    168\n",
      "Q     77\n",
      "Name: count, dtype: int64\n",
      "====================================\n",
      "class : ['Third', 'First', 'Second']\n",
      "Categories (3, object): ['First', 'Second', 'Third']\n",
      "class : class\n",
      "Third     491\n",
      "First     216\n",
      "Second    184\n",
      "Name: count, dtype: int64\n",
      "====================================\n",
      "who : ['man' 'woman' 'child']\n",
      "who : who\n",
      "man      537\n",
      "woman    271\n",
      "child     83\n",
      "Name: count, dtype: int64\n",
      "====================================\n",
      "adult_male : [ True False]\n",
      "adult_male : adult_male\n",
      "True     537\n",
      "False    354\n",
      "Name: count, dtype: int64\n",
      "====================================\n",
      "deck : [NaN, 'C', 'E', 'G', 'D', 'A', 'B', 'F']\n",
      "Categories (7, object): ['A', 'B', 'C', 'D', 'E', 'F', 'G']\n",
      "deck : deck\n",
      "C    59\n",
      "B    47\n",
      "D    33\n",
      "E    32\n",
      "A    15\n",
      "F    13\n",
      "G     4\n",
      "Name: count, dtype: int64\n",
      "====================================\n",
      "embark_town : ['Southampton' 'Cherbourg' 'Queenstown' nan]\n",
      "embark_town : embark_town\n",
      "Southampton    644\n",
      "Cherbourg      168\n",
      "Queenstown      77\n",
      "Name: count, dtype: int64\n",
      "====================================\n",
      "alive : ['no' 'yes']\n",
      "alive : alive\n",
      "no     549\n",
      "yes    342\n",
      "Name: count, dtype: int64\n",
      "====================================\n",
      "alone : [False  True]\n",
      "alone : alone\n",
      "True     537\n",
      "False    354\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 titanic_data.columns: #Iterates through each column in the DataFrame.\n",
    "    if len(titanic_data[column].unique()) <= 30:#Checks if the column's data type is 'category' (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} : {titanic_data[column].unique()}\") #Prints the name of the column and its unique values.\n",
    "        print(f\"{column} : {titanic_data[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": 5,
   "id": "2ecba663",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['survived', 'pclass', 'sex', 'age', 'sibsp', 'parch', 'fare',\n",
       "       'embarked', 'class', 'who', 'adult_male', 'deck', 'embark_town',\n",
       "       'alive', 'alone'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "titanic_data.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "828c975e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "sex\n",
       "male      577\n",
       "female    314\n",
       "Name: count, dtype: int64"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "titanic_data.sex.value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "68e832c3",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "who\n",
       "man      537\n",
       "woman    271\n",
       "child     83\n",
       "Name: count, dtype: int64"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "titanic_data.who.value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "e3566785",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "embarked\n",
       "S    644\n",
       "C    168\n",
       "Q     77\n",
       "Name: count, dtype: int64"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "titanic_data.embarked.value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "239d8319",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "embark_town\n",
       "Southampton    644\n",
       "Cherbourg      168\n",
       "Queenstown      77\n",
       "Name: count, dtype: int64"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "titanic_data.embark_town.value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "8f67ac65",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "alive\n",
       "no     549\n",
       "yes    342\n",
       "Name: count, dtype: int64"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "titanic_data.alive.value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "0cefd6fc",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "adult_male\n",
       "True     537\n",
       "False    354\n",
       "Name: count, dtype: int64"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "titanic_data.adult_male.value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "2a96072c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "deck\n",
       "C    59\n",
       "B    47\n",
       "D    33\n",
       "E    32\n",
       "A    15\n",
       "F    13\n",
       "G     4\n",
       "Name: count, dtype: int64"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "titanic_data.deck.value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "7ab14eb5",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "alone\n",
       "True     537\n",
       "False    354\n",
       "Name: count, dtype: int64"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "titanic_data.alone.value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "6796d4a8",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "class\n",
       "Third     491\n",
       "First     216\n",
       "Second    184\n",
       "Name: count, dtype: int64"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "titanic_data[\"class\"].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "3518e834",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(891, 15)"
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     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "titanic_data.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "c6c2b2ed",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "4.7792532923723545"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "skew(titanic_data[\"fare\"])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "ca2f6e9f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.4777174662568536"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "skew(titanic_data[\"survived\"])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "f8c54376",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "-0.6294858816096934"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "skew(titanic_data[\"pclass\"])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "46d836b2",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "survived         0\n",
       "pclass           0\n",
       "sex              0\n",
       "age            177\n",
       "sibsp            0\n",
       "parch            0\n",
       "fare             0\n",
       "embarked         2\n",
       "class            0\n",
       "who              0\n",
       "adult_male       0\n",
       "deck           688\n",
       "embark_town      2\n",
       "alive            0\n",
       "alone            0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "titanic_data.isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "3ab38c57",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
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       "      <td>1</td>\n",
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       "      <td>38.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>71.2833</td>\n",
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       "      <th>4</th>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>male</td>\n",
       "      <td>35.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>8.0500</td>\n",
       "      <td>S</td>\n",
       "      <td>Third</td>\n",
       "      <td>man</td>\n",
       "      <td>True</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Southampton</td>\n",
       "      <td>no</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   survived  pclass     sex   age  sibsp  parch     fare embarked  class  \\\n",
       "0         0       3    male  22.0      1      0   7.2500        S  Third   \n",
       "1         1       1  female  38.0      1      0  71.2833        C  First   \n",
       "2         1       3  female  26.0      0      0   7.9250        S  Third   \n",
       "3         1       1  female  35.0      1      0  53.1000        S  First   \n",
       "4         0       3    male  35.0      0      0   8.0500        S  Third   \n",
       "\n",
       "     who  adult_male deck  embark_town alive  alone  \n",
       "0    man        True  NaN  Southampton    no  False  \n",
       "1  woman       False    C    Cherbourg   yes  False  \n",
       "2  woman       False  NaN  Southampton   yes   True  \n",
       "3  woman       False    C  Southampton   yes  False  \n",
       "4    man        True  NaN  Southampton    no   True  "
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "titanic_data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "25f169c2",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/var/folders/x_/bydwqxmj22b17_nc_zdgjlhc0000gn/T/ipykernel_53706/1809904519.py:1: FutureWarning: The default of observed=False is deprecated and will be changed to True in a future version of pandas. Pass observed=False to retain current behavior or observed=True to adopt the future default and silence this warning.\n",
      "  deck_survival=titanic_data.groupby(\"deck\")[\"survived\"].mean().reset_index()\n"
     ]
    }
   ],
   "source": [
    "deck_survival=titanic_data.groupby(\"deck\")[\"survived\"].mean().reset_index()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "fb2af46b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>deck</th>\n",
       "      <th>survived</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>A</td>\n",
       "      <td>0.466667</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>B</td>\n",
       "      <td>0.744681</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>C</td>\n",
       "      <td>0.593220</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>D</td>\n",
       "      <td>0.757576</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>E</td>\n",
       "      <td>0.750000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>F</td>\n",
       "      <td>0.615385</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>G</td>\n",
       "      <td>0.500000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  deck  survived\n",
       "0    A  0.466667\n",
       "1    B  0.744681\n",
       "2    C  0.593220\n",
       "3    D  0.757576\n",
       "4    E  0.750000\n",
       "5    F  0.615385\n",
       "6    G  0.500000"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "deck_survival"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "cee272bf",
   "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/categorical.py:641: FutureWarning: The default of observed=False is deprecated and will be changed to True in a future version of pandas. Pass observed=False to retain current behavior or observed=True to adopt the future default and silence this warning.\n",
      "  grouped_vals = vals.groupby(grouper)\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<Axes: xlabel='deck', ylabel='survived'>"
      ]
     },
     "execution_count": 23,
     "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.barplot(data=deck_survival,x=\"deck\",y=\"survived\")#survival rate"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "1a4a5663",
   "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"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<Axes: xlabel='sibsp', ylabel='age'>"
      ]
     },
     "execution_count": 24,
     "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": [
    "#to find out the missing values of age\n",
    "sns.boxplot(x='sibsp', y='age',data=titanic_data,hue=\"survived\")#so sibsp is impacted by age column hence we can use parch"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "77dfa99d",
   "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"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<Axes: xlabel='parch', ylabel='age'>"
      ]
     },
     "execution_count": 25,
     "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": [
    "#to find out the missing values of age\n",
    "sns.boxplot(x='parch', y='age',data=titanic_data,hue=\"survived\")#so parch is impacted by age column hence we can use parch to replace missing values of age"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "be9ec388",
   "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='parch', ylabel='age'>"
      ]
     },
     "execution_count": 26,
     "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": [
    "#to find out the missing values of age\n",
    "sns.boxplot(x='parch', y='age',data=titanic_data)#so parch is impacted by age column hence we can use parch to replace missing values of age"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "9feb956e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "parch\n",
       "0    30.0\n",
       "1    23.0\n",
       "2    16.5\n",
       "3    24.0\n",
       "4    42.5\n",
       "5    39.0\n",
       "6    43.0\n",
       "Name: age, dtype: float64"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "titanic_data.groupby('parch')['age'].median()#median value of parch where the missing values of age "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "b57ee140",
   "metadata": {},
   "outputs": [],
   "source": [
    "def age1(col):\n",
    "    Age=col[0]\n",
    "    Parch=col[1]\n",
    "    if pd.isnull(Age):\n",
    "        if Parch == 0:\n",
    "            return 30\n",
    "        elif Parch==1:\n",
    "            return 23\n",
    "        elif Parch==2:\n",
    "            return 16\n",
    "        elif Parch==3:\n",
    "            return 24\n",
    "        elif Parch==4:\n",
    "            return 42\n",
    "        elif Parch==5:\n",
    "            return 39\n",
    "        else:\n",
    "            return 43\n",
    "    else:\n",
    "        return Age"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "f6e6b0e8",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/var/folders/x_/bydwqxmj22b17_nc_zdgjlhc0000gn/T/ipykernel_53706/3095843966.py:2: FutureWarning: Series.__getitem__ treating keys as positions is deprecated. In a future version, integer keys will always be treated as labels (consistent with DataFrame behavior). To access a value by position, use `ser.iloc[pos]`\n",
      "  Age=col[0]\n",
      "/var/folders/x_/bydwqxmj22b17_nc_zdgjlhc0000gn/T/ipykernel_53706/3095843966.py:3: FutureWarning: Series.__getitem__ treating keys as positions is deprecated. In a future version, integer keys will always be treated as labels (consistent with DataFrame behavior). To access a value by position, use `ser.iloc[pos]`\n",
      "  Parch=col[1]\n"
     ]
    }
   ],
   "source": [
    "titanic_data['age']=titanic_data[['age', 'parch']].apply(age1, axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "fa241786",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "survived         0\n",
       "pclass           0\n",
       "sex              0\n",
       "age              0\n",
       "sibsp            0\n",
       "parch            0\n",
       "fare             0\n",
       "embarked         2\n",
       "class            0\n",
       "who              0\n",
       "adult_male       0\n",
       "deck           688\n",
       "embark_town      2\n",
       "alive            0\n",
       "alone            0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "titanic_data.isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "ee6736e9",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(891, 15)"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "titanic_data.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "id": "820cd42c",
   "metadata": {},
   "outputs": [],
   "source": [
    "#to treat the missing values of deck column.We have 688 missing values for deck hence computing missing values with mode will make the data set biased and skewed & will lead to incorrect predictions while building the model \n",
    "#So we will tag the misssing values of deck column as a seperate segment known as \"others\"\n",
    "titanic_data[\"deck\"]=titanic_data[\"deck\"].cat.add_categories(\"other\").fillna(\"other\") "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "id": "dd5fd54f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "survived       0\n",
       "pclass         0\n",
       "sex            0\n",
       "age            0\n",
       "sibsp          0\n",
       "parch          0\n",
       "fare           0\n",
       "embarked       2\n",
       "class          0\n",
       "who            0\n",
       "adult_male     0\n",
       "deck           0\n",
       "embark_town    2\n",
       "alive          0\n",
       "alone          0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "titanic_data.isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "id": "f04b73f5",
   "metadata": {},
   "outputs": [],
   "source": [
    "#computing the missing values of embarked column with its mode\n",
    "titanic_data[\"embarked\"]=titanic_data[\"embarked\"].fillna(\"S\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "id": "7529e8f7",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "survived          int64\n",
       "pclass            int64\n",
       "sex              object\n",
       "age             float64\n",
       "sibsp             int64\n",
       "parch             int64\n",
       "fare            float64\n",
       "embarked         object\n",
       "class          category\n",
       "who              object\n",
       "adult_male         bool\n",
       "deck           category\n",
       "embark_town      object\n",
       "alive            object\n",
       "alone              bool\n",
       "dtype: object"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "titanic_data.dtypes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "id": "ecc90da4",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "parch\n",
       "0    678\n",
       "1    118\n",
       "2     80\n",
       "5      5\n",
       "3      5\n",
       "4      4\n",
       "6      1\n",
       "Name: count, dtype: int64"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "titanic_data.parch.value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "id": "a995191c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['survived', 'pclass', 'sex', 'age', 'sibsp', 'parch', 'fare',\n",
       "       'embarked', 'class', 'who', 'adult_male', 'deck', 'embark_town',\n",
       "       'alive', 'alone'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "titanic_data.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "id": "9e41e193",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "survived\n",
       "0    549\n",
       "1    342\n",
       "Name: count, dtype: int64"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "titanic_data[\"survived\"].value_counts()#checking dataset is balanced or not.Here the data set is not balanced and we have to balance it with the help of SMOTE."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "id": "90ae7b0f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.4500727184947494"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "skew(titanic_data[\"age\"])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "id": "d16a6a6d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "3.6891276847759507"
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "skew(titanic_data[\"sibsp\"])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "id": "a18b053d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2.7444867379203735"
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "skew(titanic_data[\"parch\"])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "id": "722a8262",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "-1.7717860224331319"
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "kurtosis(titanic_data[\"survived\"])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "id": "62347d12",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "33.20428925264474"
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "kurtosis(titanic_data[\"fare\"])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "id": "d63cce68",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "-1.2795696818700388"
      ]
     },
     "execution_count": 44,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "kurtosis(titanic_data[\"pclass\"])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "id": "4b15a36a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.863043344665706"
      ]
     },
     "execution_count": 45,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "kurtosis(titanic_data[\"age\"])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "id": "0d601ad1",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "17.77351174692092"
      ]
     },
     "execution_count": 46,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "kurtosis(titanic_data[\"sibsp\"])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "id": "f18db860",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "9.716613236667277"
      ]
     },
     "execution_count": 47,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "kurtosis(titanic_data[\"parch\"])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "id": "69fbb41f",
   "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"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<Axes: >"
      ]
     },
     "execution_count": 48,
     "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.boxplot(titanic_data)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "id": "16a9bd71",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/var/folders/x_/bydwqxmj22b17_nc_zdgjlhc0000gn/T/ipykernel_53706/2221036958.py:1: UserWarning: \n",
      "\n",
      "`distplot` is a deprecated function and will be removed in seaborn v0.14.0.\n",
      "\n",
      "Please adapt your code to use either `displot` (a figure-level function with\n",
      "similar flexibility) or `histplot` (an axes-level function for histograms).\n",
      "\n",
      "For a guide to updating your code to use the new functions, please see\n",
      "https://gist.github.com/mwaskom/de44147ed2974457ad6372750bbe5751\n",
      "\n",
      "  sns.distplot(titanic_data[\"survived\"])#survived is discrete variable so this dist plot is not appropriate\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:1119: FutureWarning: use_inf_as_na option is deprecated and will be removed in a future version. Convert inf values to NaN before operating instead.\n",
      "  with pd.option_context('mode.use_inf_as_na', True):\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<Axes: xlabel='survived', ylabel='Density'>"
      ]
     },
     "execution_count": 49,
     "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.distplot(titanic_data[\"survived\"])#survived is discrete variable so this dist plot is not appropriate"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "id": "16dad4c7",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/var/folders/x_/bydwqxmj22b17_nc_zdgjlhc0000gn/T/ipykernel_53706/1427637919.py:1: UserWarning: \n",
      "\n",
      "`distplot` is a deprecated function and will be removed in seaborn v0.14.0.\n",
      "\n",
      "Please adapt your code to use either `displot` (a figure-level function with\n",
      "similar flexibility) or `histplot` (an axes-level function for histograms).\n",
      "\n",
      "For a guide to updating your code to use the new functions, please see\n",
      "https://gist.github.com/mwaskom/de44147ed2974457ad6372750bbe5751\n",
      "\n",
      "  sns.distplot(titanic_data[\"fare\"])\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:1119: FutureWarning: use_inf_as_na option is deprecated and will be removed in a future version. Convert inf values to NaN before operating instead.\n",
      "  with pd.option_context('mode.use_inf_as_na', True):\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<Axes: xlabel='fare', ylabel='Density'>"
      ]
     },
     "execution_count": 50,
     "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.distplot(titanic_data[\"fare\"])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "id": "259f8f4f",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/var/folders/x_/bydwqxmj22b17_nc_zdgjlhc0000gn/T/ipykernel_53706/1734181900.py:1: UserWarning: \n",
      "\n",
      "`distplot` is a deprecated function and will be removed in seaborn v0.14.0.\n",
      "\n",
      "Please adapt your code to use either `displot` (a figure-level function with\n",
      "similar flexibility) or `histplot` (an axes-level function for histograms).\n",
      "\n",
      "For a guide to updating your code to use the new functions, please see\n",
      "https://gist.github.com/mwaskom/de44147ed2974457ad6372750bbe5751\n",
      "\n",
      "  sns.distplot(titanic_data[\"pclass\"])#pclass is discrete variable so this dist plot is not appropriate\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:1119: FutureWarning: use_inf_as_na option is deprecated and will be removed in a future version. Convert inf values to NaN before operating instead.\n",
      "  with pd.option_context('mode.use_inf_as_na', True):\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<Axes: xlabel='pclass', ylabel='Density'>"
      ]
     },
     "execution_count": 51,
     "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.distplot(titanic_data[\"pclass\"])#pclass is discrete variable so this dist plot is not appropriate"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "id": "6fcd055e",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/var/folders/x_/bydwqxmj22b17_nc_zdgjlhc0000gn/T/ipykernel_53706/1387134588.py:1: UserWarning: \n",
      "\n",
      "`distplot` is a deprecated function and will be removed in seaborn v0.14.0.\n",
      "\n",
      "Please adapt your code to use either `displot` (a figure-level function with\n",
      "similar flexibility) or `histplot` (an axes-level function for histograms).\n",
      "\n",
      "For a guide to updating your code to use the new functions, please see\n",
      "https://gist.github.com/mwaskom/de44147ed2974457ad6372750bbe5751\n",
      "\n",
      "  sns.distplot(titanic_data[\"age\"])\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:1119: FutureWarning: use_inf_as_na option is deprecated and will be removed in a future version. Convert inf values to NaN before operating instead.\n",
      "  with pd.option_context('mode.use_inf_as_na', True):\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<Axes: xlabel='age', ylabel='Density'>"
      ]
     },
     "execution_count": 52,
     "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.distplot(titanic_data[\"age\"])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "id": "c57917c6",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/var/folders/x_/bydwqxmj22b17_nc_zdgjlhc0000gn/T/ipykernel_53706/4051946788.py:1: UserWarning: \n",
      "\n",
      "`distplot` is a deprecated function and will be removed in seaborn v0.14.0.\n",
      "\n",
      "Please adapt your code to use either `displot` (a figure-level function with\n",
      "similar flexibility) or `histplot` (an axes-level function for histograms).\n",
      "\n",
      "For a guide to updating your code to use the new functions, please see\n",
      "https://gist.github.com/mwaskom/de44147ed2974457ad6372750bbe5751\n",
      "\n",
      "  sns.distplot(titanic_data[\"sibsp\"])#sibsp is discrete variable so this dist plot is not appropriate\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:1119: FutureWarning: use_inf_as_na option is deprecated and will be removed in a future version. Convert inf values to NaN before operating instead.\n",
      "  with pd.option_context('mode.use_inf_as_na', True):\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<Axes: xlabel='sibsp', ylabel='Density'>"
      ]
     },
     "execution_count": 53,
     "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.distplot(titanic_data[\"sibsp\"])#sibsp is discrete variable so this dist plot is not appropriate"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "id": "716c1d03",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/var/folders/x_/bydwqxmj22b17_nc_zdgjlhc0000gn/T/ipykernel_53706/513920398.py:1: UserWarning: \n",
      "\n",
      "`distplot` is a deprecated function and will be removed in seaborn v0.14.0.\n",
      "\n",
      "Please adapt your code to use either `displot` (a figure-level function with\n",
      "similar flexibility) or `histplot` (an axes-level function for histograms).\n",
      "\n",
      "For a guide to updating your code to use the new functions, please see\n",
      "https://gist.github.com/mwaskom/de44147ed2974457ad6372750bbe5751\n",
      "\n",
      "  sns.distplot(titanic_data[\"parch\"])#parch is discrete variable so this dist plot is not appropriate\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:1119: FutureWarning: use_inf_as_na option is deprecated and will be removed in a future version. Convert inf values to NaN before operating instead.\n",
      "  with pd.option_context('mode.use_inf_as_na', True):\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<Axes: xlabel='parch', ylabel='Density'>"
      ]
     },
     "execution_count": 54,
     "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.distplot(titanic_data[\"parch\"])#parch is discrete variable so this dist plot is not appropriate"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "id": "e2d0417a",
   "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"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<Axes: xlabel='age', ylabel='fare'>"
      ]
     },
     "execution_count": 55,
     "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.scatterplot(y='fare',x='age',data=titanic_data)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "id": "282dbc6e",
   "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"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<Axes: xlabel='fare', ylabel='pclass'>"
      ]
     },
     "execution_count": 56,
     "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.scatterplot(y='pclass',x='fare',data=titanic_data)#one continuous variable and one discrete variable so box plot or bar plot should be appropriate instead of scatter plot which is for 2 continuous variables"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "id": "1559a23c",
   "metadata": {},
   "outputs": [],
   "source": [
    "#delete class,fare,embark_town,who,adult_male,alive,alone\n",
    "titanic_data.drop([\"class\",\"fare\",\"embark_town\",\"who\",\"adult_male\",\"alive\",\"alone\"],axis=1,inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "id": "c8e6c72d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>survived</th>\n",
       "      <th>pclass</th>\n",
       "      <th>sex</th>\n",
       "      <th>age</th>\n",
       "      <th>sibsp</th>\n",
       "      <th>parch</th>\n",
       "      <th>embarked</th>\n",
       "      <th>deck</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>male</td>\n",
       "      <td>22.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>S</td>\n",
       "      <td>other</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>female</td>\n",
       "      <td>38.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>C</td>\n",
       "      <td>C</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>female</td>\n",
       "      <td>26.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>S</td>\n",
       "      <td>other</td>\n",
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       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>female</td>\n",
       "      <td>35.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>S</td>\n",
       "      <td>C</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>male</td>\n",
       "      <td>35.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>S</td>\n",
       "      <td>other</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   survived  pclass     sex   age  sibsp  parch embarked   deck\n",
       "0         0       3    male  22.0      1      0        S  other\n",
       "1         1       1  female  38.0      1      0        C      C\n",
       "2         1       3  female  26.0      0      0        S  other\n",
       "3         1       1  female  35.0      1      0        S      C\n",
       "4         0       3    male  35.0      0      0        S  other"
      ]
     },
     "execution_count": 58,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "titanic_data.head()#columns to be encoded-sex,embarked and deck\n",
    "#sex-label encoding\n",
    "#embarked- label encoding / one hot encoding\n",
    "#deck-one hot encoding"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "id": "14dcfd83",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "deck\n",
       "other    688\n",
       "C         59\n",
       "B         47\n",
       "D         33\n",
       "E         32\n",
       "A         15\n",
       "F         13\n",
       "G          4\n",
       "Name: count, dtype: int64"
      ]
     },
     "execution_count": 59,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "titanic_data.deck.value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "id": "ba6a1595",
   "metadata": {},
   "outputs": [
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     },
     "execution_count": 60,
     "metadata": {},
     "output_type": "execute_result"
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   ],
   "source": [
    "#applying label encoding to sex column\n",
    "le=LabelEncoder()#saving the instance\n",
    "gender=titanic_data['sex']\n",
    "gender1=le.fit_transform(gender)\n",
    "gender1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "id": "9aa59110",
   "metadata": {},
   "outputs": [
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       "     gender\n",
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     "execution_count": 61,
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   "source": [
    "#changing from an array to data frame\n",
    "gender_df=pd.DataFrame(gender1,columns=['gender'])\n",
    "gender_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "id": "a378cb5c",
   "metadata": {},
   "outputs": [],
   "source": [
    "#applying one hot encoding to embarked column\n",
    "binary=OneHotEncoder(categories='auto')#it creates an instance of the one hot encoder class with the categories parameter set to Auto which means it will automatically determine the categories of the data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "id": "bc0d2bd9",
   "metadata": {},
   "outputs": [
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       "       c    q    s\n",
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     },
     "execution_count": 63,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "embarked=titanic_data['embarked']\n",
    "embarked1=le.fit_transform(embarked)\n",
    "e1=binary.fit_transform(embarked1.reshape(-1, 1))\n",
    "e2=e1.toarray()\n",
    "embarked_df=pd.DataFrame(e2,columns=['c','q','s'])\n",
    "embarked_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "id": "68daa63c",
   "metadata": {},
   "outputs": [],
   "source": [
    "#applying one hot encoding to deck column\n",
    "binary1=OneHotEncoder(categories='auto')#it creates an instance of the one hot encoder class with the categories parameter set to Auto which means it will automatically determine the categories of the data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "id": "82358e63",
   "metadata": {},
   "outputs": [
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       "      <td>1.0</td>\n",
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       "  </tbody>\n",
       "</table>\n",
       "<p>891 rows × 8 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       a    b    c    d    e    f    g  other\n",
       "0    0.0  0.0  0.0  0.0  0.0  0.0  0.0    1.0\n",
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       "..   ...  ...  ...  ...  ...  ...  ...    ...\n",
       "886  0.0  0.0  0.0  0.0  0.0  0.0  0.0    1.0\n",
       "887  0.0  1.0  0.0  0.0  0.0  0.0  0.0    0.0\n",
       "888  0.0  0.0  0.0  0.0  0.0  0.0  0.0    1.0\n",
       "889  0.0  0.0  1.0  0.0  0.0  0.0  0.0    0.0\n",
       "890  0.0  0.0  0.0  0.0  0.0  0.0  0.0    1.0\n",
       "\n",
       "[891 rows x 8 columns]"
      ]
     },
     "execution_count": 65,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "deck=titanic_data['deck']\n",
    "deck1=le.fit_transform(deck)\n",
    "d1=binary1.fit_transform(deck1.reshape(-1, 1))\n",
    "d2=d1.toarray()\n",
    "deck_df=pd.DataFrame(d2,columns=['a','b','c','d','e','f','g','other'])\n",
    "deck_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "id": "ab51607e",
   "metadata": {},
   "outputs": [
    {
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      ],
      "text/plain": [
       "     survived  pclass     sex   age  sibsp  parch embarked   deck  gender  \\\n",
       "0           0       3    male  22.0      1      0        S  other       1   \n",
       "1           1       1  female  38.0      1      0        C      C       0   \n",
       "2           1       3  female  26.0      0      0        S  other       0   \n",
       "3           1       1  female  35.0      1      0        S      C       0   \n",
       "4           0       3    male  35.0      0      0        S  other       1   \n",
       "..        ...     ...     ...   ...    ...    ...      ...    ...     ...   \n",
       "886         0       2    male  27.0      0      0        S  other       1   \n",
       "887         1       1  female  19.0      0      0        S      B       0   \n",
       "888         0       3  female  16.0      1      2        S  other       0   \n",
       "889         1       1    male  26.0      0      0        C      C       1   \n",
       "890         0       3    male  32.0      0      0        Q  other       1   \n",
       "\n",
       "       c    q    s    a    b    c    d    e    f    g  other  \n",
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       "..   ...  ...  ...  ...  ...  ...  ...  ...  ...  ...    ...  \n",
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       "\n",
       "[891 rows x 20 columns]"
      ]
     },
     "execution_count": 66,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#we need to concatenate gender_df,embarked_df,deck_df to our original data\n",
    "data_updated=pd.concat([titanic_data,gender_df,embarked_df,deck_df],axis=1)\n",
    "data_updated"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "id": "1b29d80d",
   "metadata": {},
   "outputs": [
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       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>38.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>26.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>35.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>35.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</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",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>886</th>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>27.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>887</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>19.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>888</th>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>16.0</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>889</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>26.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>890</th>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>32.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>891 rows × 15 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     survived  pclass   age  sibsp  parch  gender    q    s    a    b    d  \\\n",
       "0           0       3  22.0      1      0       1  0.0  1.0  0.0  0.0  0.0   \n",
       "1           1       1  38.0      1      0       0  0.0  0.0  0.0  0.0  0.0   \n",
       "2           1       3  26.0      0      0       0  0.0  1.0  0.0  0.0  0.0   \n",
       "3           1       1  35.0      1      0       0  0.0  1.0  0.0  0.0  0.0   \n",
       "4           0       3  35.0      0      0       1  0.0  1.0  0.0  0.0  0.0   \n",
       "..        ...     ...   ...    ...    ...     ...  ...  ...  ...  ...  ...   \n",
       "886         0       2  27.0      0      0       1  0.0  1.0  0.0  0.0  0.0   \n",
       "887         1       1  19.0      0      0       0  0.0  1.0  0.0  1.0  0.0   \n",
       "888         0       3  16.0      1      2       0  0.0  1.0  0.0  0.0  0.0   \n",
       "889         1       1  26.0      0      0       1  0.0  0.0  0.0  0.0  0.0   \n",
       "890         0       3  32.0      0      0       1  1.0  0.0  0.0  0.0  0.0   \n",
       "\n",
       "       e    f    g  other  \n",
       "0    0.0  0.0  0.0    1.0  \n",
       "1    0.0  0.0  0.0    0.0  \n",
       "2    0.0  0.0  0.0    1.0  \n",
       "3    0.0  0.0  0.0    0.0  \n",
       "4    0.0  0.0  0.0    1.0  \n",
       "..   ...  ...  ...    ...  \n",
       "886  0.0  0.0  0.0    1.0  \n",
       "887  0.0  0.0  0.0    0.0  \n",
       "888  0.0  0.0  0.0    1.0  \n",
       "889  0.0  0.0  0.0    0.0  \n",
       "890  0.0  0.0  0.0    1.0  \n",
       "\n",
       "[891 rows x 15 columns]"
      ]
     },
     "execution_count": 67,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#dropping unecessary columns-sex,embarked,deck,c(embarked),c(deck)\n",
    "data_updated.drop([\"sex\",\"embarked\",\"deck\",\"c\",\"c\"],axis=1,inplace=True)\n",
    "data_updated"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "id": "90d65fa9",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "survived    0\n",
       "pclass      0\n",
       "age         0\n",
       "sibsp       0\n",
       "parch       0\n",
       "gender      0\n",
       "q           0\n",
       "s           0\n",
       "a           0\n",
       "b           0\n",
       "d           0\n",
       "e           0\n",
       "f           0\n",
       "g           0\n",
       "other       0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 68,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data_updated.isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "id": "2a6c0689",
   "metadata": {},
   "outputs": [],
   "source": [
    "#dividing the data set into x and y.\n",
    "x=data_updated.drop('survived',axis=1)\n",
    "y=data_updated['survived']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "id": "70e0d10d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>pclass</th>\n",
       "      <th>age</th>\n",
       "      <th>sibsp</th>\n",
       "      <th>parch</th>\n",
       "      <th>gender</th>\n",
       "      <th>q</th>\n",
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       "      <th>b</th>\n",
       "      <th>d</th>\n",
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       "      <th>other</th>\n",
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       "      <th>0</th>\n",
       "      <td>0.0375</td>\n",
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       "      <td>0.0375</td>\n",
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       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.0125</td>\n",
       "      <td>0.4375</td>\n",
       "      <td>0.0125</td>\n",
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       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.0375</td>\n",
       "      <td>0.4375</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.0125</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.0125</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0000</td>\n",
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       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
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       "    <tr>\n",
       "      <th>886</th>\n",
       "      <td>0.0250</td>\n",
       "      <td>0.3375</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.0125</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.0125</td>\n",
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       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0125</td>\n",
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       "    <tr>\n",
       "      <th>887</th>\n",
       "      <td>0.0125</td>\n",
       "      <td>0.2375</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.0000</td>\n",
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       "      <td>0.0</td>\n",
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       "    <tr>\n",
       "      <th>888</th>\n",
       "      <td>0.0375</td>\n",
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       "      <td>0.0125</td>\n",
       "      <td>0.025</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.0000</td>\n",
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       "      <td>0.0</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0125</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>889</th>\n",
       "      <td>0.0125</td>\n",
       "      <td>0.3250</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.0125</td>\n",
       "      <td>0.0000</td>\n",
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       "      <td>0.0</td>\n",
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       "    <tr>\n",
       "      <th>890</th>\n",
       "      <td>0.0375</td>\n",
       "      <td>0.4000</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.0125</td>\n",
       "      <td>0.0125</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0125</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>891 rows × 14 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     pclass     age   sibsp  parch  gender       q       s    a       b    d  \\\n",
       "0    0.0375  0.2750  0.0125  0.000  0.0125  0.0000  0.0125  0.0  0.0000  0.0   \n",
       "1    0.0125  0.4750  0.0125  0.000  0.0000  0.0000  0.0000  0.0  0.0000  0.0   \n",
       "2    0.0375  0.3250  0.0000  0.000  0.0000  0.0000  0.0125  0.0  0.0000  0.0   \n",
       "3    0.0125  0.4375  0.0125  0.000  0.0000  0.0000  0.0125  0.0  0.0000  0.0   \n",
       "4    0.0375  0.4375  0.0000  0.000  0.0125  0.0000  0.0125  0.0  0.0000  0.0   \n",
       "..      ...     ...     ...    ...     ...     ...     ...  ...     ...  ...   \n",
       "886  0.0250  0.3375  0.0000  0.000  0.0125  0.0000  0.0125  0.0  0.0000  0.0   \n",
       "887  0.0125  0.2375  0.0000  0.000  0.0000  0.0000  0.0125  0.0  0.0125  0.0   \n",
       "888  0.0375  0.2000  0.0125  0.025  0.0000  0.0000  0.0125  0.0  0.0000  0.0   \n",
       "889  0.0125  0.3250  0.0000  0.000  0.0125  0.0000  0.0000  0.0  0.0000  0.0   \n",
       "890  0.0375  0.4000  0.0000  0.000  0.0125  0.0125  0.0000  0.0  0.0000  0.0   \n",
       "\n",
       "       e    f    g   other  \n",
       "0    0.0  0.0  0.0  0.0125  \n",
       "1    0.0  0.0  0.0  0.0000  \n",
       "2    0.0  0.0  0.0  0.0125  \n",
       "3    0.0  0.0  0.0  0.0000  \n",
       "4    0.0  0.0  0.0  0.0125  \n",
       "..   ...  ...  ...     ...  \n",
       "886  0.0  0.0  0.0  0.0125  \n",
       "887  0.0  0.0  0.0  0.0000  \n",
       "888  0.0  0.0  0.0  0.0125  \n",
       "889  0.0  0.0  0.0  0.0000  \n",
       "890  0.0  0.0  0.0  0.0125  \n",
       "\n",
       "[891 rows x 14 columns]"
      ]
     },
     "execution_count": 70,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#applying feature scaling on x(features)\n",
    "x=(x-np.min(x))/(np.max(x)-np.min(x))\n",
    "#scaled_value = (value - min_value) / (max_value - min_value) which is min-max scaler feature selection\n",
    "x\n",
    "#this method of doing min-max scaling is inappropriate because it is unable to retain the hierrarchical values for the categorical variables having either 0 or 1 as possible values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "id": "2f8ec5cb",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th></th>\n",
       "      <th>pclass</th>\n",
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       "<p>891 rows × 14 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     pclass       age  sibsp     parch  gender    q    s    a    b    d    e  \\\n",
       "0       1.0  0.271174  0.125  0.000000     1.0  0.0  1.0  0.0  0.0  0.0  0.0   \n",
       "1       0.0  0.472229  0.125  0.000000     0.0  0.0  0.0  0.0  0.0  0.0  0.0   \n",
       "2       1.0  0.321438  0.000  0.000000     0.0  0.0  1.0  0.0  0.0  0.0  0.0   \n",
       "3       0.0  0.434531  0.125  0.000000     0.0  0.0  1.0  0.0  0.0  0.0  0.0   \n",
       "4       1.0  0.434531  0.000  0.000000     1.0  0.0  1.0  0.0  0.0  0.0  0.0   \n",
       "..      ...       ...    ...       ...     ...  ...  ...  ...  ...  ...  ...   \n",
       "886     0.5  0.334004  0.000  0.000000     1.0  0.0  1.0  0.0  0.0  0.0  0.0   \n",
       "887     0.0  0.233476  0.000  0.000000     0.0  0.0  1.0  0.0  1.0  0.0  0.0   \n",
       "888     1.0  0.195778  0.125  0.333333     0.0  0.0  1.0  0.0  0.0  0.0  0.0   \n",
       "889     0.0  0.321438  0.000  0.000000     1.0  0.0  0.0  0.0  0.0  0.0  0.0   \n",
       "890     1.0  0.396833  0.000  0.000000     1.0  1.0  0.0  0.0  0.0  0.0  0.0   \n",
       "\n",
       "       f    g  other  \n",
       "0    0.0  0.0    1.0  \n",
       "1    0.0  0.0    0.0  \n",
       "2    0.0  0.0    1.0  \n",
       "3    0.0  0.0    0.0  \n",
       "4    0.0  0.0    1.0  \n",
       "..   ...  ...    ...  \n",
       "886  0.0  0.0    1.0  \n",
       "887  0.0  0.0    0.0  \n",
       "888  0.0  0.0    1.0  \n",
       "889  0.0  0.0    0.0  \n",
       "890  0.0  0.0    1.0  \n",
       "\n",
       "[891 rows x 14 columns]"
      ]
     },
     "execution_count": 71,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "scaler=MinMaxScaler()#saving the instance\n",
    "scaled_data=scaler.fit_transform(x)\n",
    "scaled_data=pd.DataFrame(scaled_data,columns=x.columns)\n",
    "scaled_data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "id": "d7b22490",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(891, 14)"
      ]
     },
     "execution_count": 72,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "scaled_data.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "id": "83a65729",
   "metadata": {},
   "outputs": [],
   "source": [
    "#train-test split\n",
    "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": 74,
   "id": "948d1f40",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(     pclass       age  sibsp     parch  gender    q    s    a    b    d    e  \\\n",
       " 140     1.0  0.195778  0.000  0.333333     0.0  0.0  0.0  0.0  0.0  0.0  0.0   \n",
       " 439     0.5  0.384267  0.000  0.000000     1.0  0.0  1.0  0.0  0.0  0.0  0.0   \n",
       " 817     0.5  0.384267  0.125  0.166667     1.0  0.0  0.0  0.0  0.0  0.0  0.0   \n",
       " 378     1.0  0.246042  0.000  0.000000     1.0  0.0  0.0  0.0  0.0  0.0  0.0   \n",
       " 491     1.0  0.258608  0.000  0.000000     1.0  0.0  1.0  0.0  0.0  0.0  0.0   \n",
       " ..      ...       ...    ...       ...     ...  ...  ...  ...  ...  ...  ...   \n",
       " 835     0.0  0.484795  0.125  0.166667     0.0  0.0  0.0  0.0  0.0  0.0  1.0   \n",
       " 192     1.0  0.233476  0.125  0.000000     0.0  0.0  1.0  0.0  0.0  0.0  0.0   \n",
       " 629     1.0  0.371701  0.000  0.000000     1.0  1.0  0.0  0.0  0.0  0.0  0.0   \n",
       " 559     1.0  0.447097  0.125  0.000000     0.0  0.0  1.0  0.0  0.0  0.0  0.0   \n",
       " 684     0.5  0.748681  0.125  0.166667     1.0  0.0  1.0  0.0  0.0  0.0  0.0   \n",
       " \n",
       "        f    g  other  \n",
       " 140  0.0  0.0    1.0  \n",
       " 439  0.0  0.0    1.0  \n",
       " 817  0.0  0.0    1.0  \n",
       " 378  0.0  0.0    1.0  \n",
       " 491  0.0  0.0    1.0  \n",
       " ..   ...  ...    ...  \n",
       " 835  0.0  0.0    0.0  \n",
       " 192  0.0  0.0    1.0  \n",
       " 629  0.0  0.0    1.0  \n",
       " 559  0.0  0.0    1.0  \n",
       " 684  0.0  0.0    1.0  \n",
       " \n",
       " [712 rows x 14 columns],\n",
       "      pclass       age  sibsp     parch  gender    q    s    a    b    d    e  \\\n",
       " 495     1.0  0.371701  0.000  0.000000     1.0  0.0  0.0  0.0  0.0  0.0  0.0   \n",
       " 648     1.0  0.371701  0.000  0.000000     1.0  0.0  1.0  0.0  0.0  0.0  0.0   \n",
       " 278     1.0  0.082684  0.500  0.166667     1.0  1.0  0.0  0.0  0.0  0.0  0.0   \n",
       " 31      0.0  0.371701  0.125  0.000000     0.0  0.0  0.0  0.0  1.0  0.0  0.0   \n",
       " 255     1.0  0.359135  0.000  0.333333     0.0  0.0  0.0  0.0  0.0  0.0  0.0   \n",
       " ..      ...       ...    ...       ...     ...  ...  ...  ...  ...  ...  ...   \n",
       " 780     1.0  0.158080  0.000  0.000000     0.0  0.0  0.0  0.0  0.0  0.0  0.0   \n",
       " 837     1.0  0.371701  0.000  0.000000     1.0  0.0  1.0  0.0  0.0  0.0  0.0   \n",
       " 215     0.0  0.384267  0.125  0.000000     0.0  0.0  0.0  0.0  0.0  1.0  0.0   \n",
       " 833     1.0  0.283740  0.000  0.000000     1.0  0.0  1.0  0.0  0.0  0.0  0.0   \n",
       " 372     1.0  0.233476  0.000  0.000000     1.0  0.0  1.0  0.0  0.0  0.0  0.0   \n",
       " \n",
       "        f    g  other  \n",
       " 495  0.0  0.0    1.0  \n",
       " 648  0.0  0.0    1.0  \n",
       " 278  0.0  0.0    1.0  \n",
       " 31   0.0  0.0    0.0  \n",
       " 255  0.0  0.0    1.0  \n",
       " ..   ...  ...    ...  \n",
       " 780  0.0  0.0    1.0  \n",
       " 837  0.0  0.0    1.0  \n",
       " 215  0.0  0.0    0.0  \n",
       " 833  0.0  0.0    1.0  \n",
       " 372  0.0  0.0    1.0  \n",
       " \n",
       " [179 rows x 14 columns],\n",
       " 140    0\n",
       " 439    0\n",
       " 817    0\n",
       " 378    0\n",
       " 491    0\n",
       "       ..\n",
       " 835    1\n",
       " 192    1\n",
       " 629    0\n",
       " 559    1\n",
       " 684    0\n",
       " Name: survived, Length: 712, dtype: int64,\n",
       " 495    0\n",
       " 648    0\n",
       " 278    0\n",
       " 31     1\n",
       " 255    1\n",
       "       ..\n",
       " 780    1\n",
       " 837    0\n",
       " 215    1\n",
       " 833    0\n",
       " 372    0\n",
       " Name: survived, Length: 179, dtype: int64)"
      ]
     },
     "execution_count": 74,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x_train,x_test,y_train,y_test"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "id": "88413fd0",
   "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": 76,
   "id": "40192563",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Logistic Regression : 0.8212290502793296\n",
      "Naive bayes : 0.7374301675977654\n",
      "CART : 0.8100558659217877\n",
      "Random Forest : 0.8044692737430168\n",
      "SVM : 0.7877094972067039\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/souravghoshhansda/anaconda3/envs/notebook/lib/python3.10/site-packages/xgboost/data.py:299: FutureWarning: is_sparse is deprecated and will be removed in a future version. Check `isinstance(dtype, pd.SparseDtype)` instead.\n",
      "  if is_sparse(dtype):\n",
      "/Users/souravghoshhansda/anaconda3/envs/notebook/lib/python3.10/site-packages/xgboost/data.py:301: FutureWarning: is_categorical_dtype is deprecated and will be removed in a future version. Use isinstance(dtype, CategoricalDtype) instead\n",
      "  elif is_categorical_dtype(dtype) and enable_categorical:\n",
      "/Users/souravghoshhansda/anaconda3/envs/notebook/lib/python3.10/site-packages/xgboost/data.py:332: FutureWarning: is_categorical_dtype is deprecated and will be removed in a future version. Use isinstance(dtype, CategoricalDtype) instead\n",
      "  if is_categorical_dtype(dtype)\n",
      "/Users/souravghoshhansda/anaconda3/envs/notebook/lib/python3.10/site-packages/xgboost/data.py:323: FutureWarning: is_categorical_dtype is deprecated and will be removed in a future version. Use isinstance(dtype, CategoricalDtype) instead\n",
      "  return is_int or is_bool or is_float or is_categorical_dtype(dtype)\n",
      "/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",
      "/Users/souravghoshhansda/anaconda3/envs/notebook/lib/python3.10/site-packages/xgboost/data.py:299: FutureWarning: is_sparse is deprecated and will be removed in a future version. Check `isinstance(dtype, pd.SparseDtype)` instead.\n",
      "  if is_sparse(dtype):\n",
      "/Users/souravghoshhansda/anaconda3/envs/notebook/lib/python3.10/site-packages/xgboost/data.py:301: FutureWarning: is_categorical_dtype is deprecated and will be removed in a future version. Use isinstance(dtype, CategoricalDtype) instead\n",
      "  elif is_categorical_dtype(dtype) and enable_categorical:\n",
      "/Users/souravghoshhansda/anaconda3/envs/notebook/lib/python3.10/site-packages/xgboost/data.py:332: FutureWarning: is_categorical_dtype is deprecated and will be removed in a future version. Use isinstance(dtype, CategoricalDtype) instead\n",
      "  if is_categorical_dtype(dtype)\n",
      "/Users/souravghoshhansda/anaconda3/envs/notebook/lib/python3.10/site-packages/xgboost/data.py:323: FutureWarning: is_categorical_dtype is deprecated and will be removed in a future version. Use isinstance(dtype, CategoricalDtype) instead\n",
      "  return is_int or is_bool or is_float or is_categorical_dtype(dtype)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "XGBoost : 0.8379888268156425\n",
      "GradientBoosting : 0.8156424581005587\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": 80,
   "id": "b3620660",
   "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()</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-2\" type=\"checkbox\" checked><label for=\"sk-estimator-id-2\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">DecisionTreeClassifier</label><div class=\"sk-toggleable__content\"><pre>DecisionTreeClassifier()</pre></div></div></div></div></div>"
      ],
      "text/plain": [
       "DecisionTreeClassifier()"
      ]
     },
     "execution_count": 80,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dt=DecisionTreeClassifier()\n",
    "dt.fit(x_train,y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 81,
   "id": "5d918a29",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0, 0, 0, 1, 1, 0, 1, 1, 0, 1, 0, 1, 0, 1, 1, 0, 0, 0, 0, 1, 0, 1,\n",
       "       0, 0, 1, 1, 0, 1, 1, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n",
       "       1, 0, 0, 1, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 1, 0, 1, 0,\n",
       "       1, 0, 0, 1, 1, 1, 0, 0, 0, 1, 0, 0, 0, 1, 0, 1, 1, 0, 0, 1, 1, 0,\n",
       "       1, 1, 0, 0, 1, 1, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 1, 1, 1, 0, 0, 1,\n",
       "       0, 1, 0, 0, 0, 1, 0, 1, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0,\n",
       "       0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 1, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0,\n",
       "       1, 0, 0, 0, 1, 1, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 1, 0,\n",
       "       1, 0, 0])"
      ]
     },
     "execution_count": 81,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_pred_dt=dt.predict(x_test)\n",
    "y_pred_dt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 82,
   "id": "77b0e9bb",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.8100558659217877"
      ]
     },
     "execution_count": 82,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#finding the evaluation metrics\n",
    "accuracy_dt=accuracy_score(y_test,y_pred_dt)\n",
    "accuracy_dt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 83,
   "id": "8dce2838",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.9480337078651685"
      ]
     },
     "execution_count": 83,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_score_dt=dt.score(x_train,y_train)\n",
    "train_score_dt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 84,
   "id": "b028a707",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.8100558659217877"
      ]
     },
     "execution_count": 84,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "test_score_dt=dt.score(x_test,y_test)\n",
    "test_score_dt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 85,
   "id": "d2107b88",
   "metadata": {},
   "outputs": [],
   "source": [
    "#applying decision tree with hyper parameter tuning\n",
    "dt_classifier = DecisionTreeClassifier()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 86,
   "id": "d6197495",
   "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": 87,
   "id": "55799011",
   "metadata": {},
   "outputs": [],
   "source": [
    "grid_search = GridSearchCV(dt_classifier, param_grid, cv=5, scoring='accuracy')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 88,
   "id": "62228313",
   "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>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><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-3\" type=\"checkbox\" ><label for=\"sk-estimator-id-3\" 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-4\" type=\"checkbox\" ><label for=\"sk-estimator-id-4\" 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-5\" type=\"checkbox\" ><label for=\"sk-estimator-id-5\" 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": 88,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "grid_search.fit(x_train, y_train) "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 89,
   "id": "434719b9",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'criterion': 'entropy',\n",
       " 'max_depth': 10,\n",
       " 'max_features': 'sqrt',\n",
       " 'max_leaf_nodes': 20,\n",
       " 'min_samples_leaf': 2,\n",
       " 'min_samples_split': 2,\n",
       " 'splitter': 'best'}"
      ]
     },
     "execution_count": 89,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "best_params = grid_search.best_params_\n",
    "best_params"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 90,
   "id": "5b0fce14",
   "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>DecisionTreeClassifier(criterion=&#x27;entropy&#x27;, max_depth=10, max_features=&#x27;sqrt&#x27;,\n",
       "                       max_leaf_nodes=20, min_samples_leaf=2)</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-6\" type=\"checkbox\" checked><label for=\"sk-estimator-id-6\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">DecisionTreeClassifier</label><div class=\"sk-toggleable__content\"><pre>DecisionTreeClassifier(criterion=&#x27;entropy&#x27;, max_depth=10, max_features=&#x27;sqrt&#x27;,\n",
       "                       max_leaf_nodes=20, min_samples_leaf=2)</pre></div></div></div></div></div>"
      ],
      "text/plain": [
       "DecisionTreeClassifier(criterion='entropy', max_depth=10, max_features='sqrt',\n",
       "                       max_leaf_nodes=20, min_samples_leaf=2)"
      ]
     },
     "execution_count": 90,
     "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": 91,
   "id": "1197e98d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0, 0, 0, 1, 1, 0, 1, 1, 1, 1, 0, 1, 0, 1, 1, 0, 0, 0, 0, 1, 0, 1,\n",
       "       0, 0, 0, 1, 0, 0, 1, 0, 0, 1, 0, 0, 0, 1, 0, 0, 0, 0, 1, 0, 0, 0,\n",
       "       1, 0, 0, 1, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0,\n",
       "       1, 0, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 0, 0, 1, 1, 1,\n",
       "       1, 0, 1, 0, 0, 1, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 1, 0, 0, 1,\n",
       "       0, 1, 0, 0, 1, 1, 0, 1, 0, 1, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 1, 0,\n",
       "       0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 1, 1, 0, 0, 1, 1, 0, 0, 0, 0, 0,\n",
       "       1, 0, 0, 0, 1, 1, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 1, 0,\n",
       "       1, 0, 0])"
      ]
     },
     "execution_count": 91,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_pred_tune=best_dt_classifier.predict(x_test)\n",
    "y_pred_tune"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 92,
   "id": "e1124cc6",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.8435754189944135"
      ]
     },
     "execution_count": 92,
     "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": 93,
   "id": "b0f67f63",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.8435754189944135"
      ]
     },
     "execution_count": 93,
     "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": 94,
   "id": "573684ac",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.824438202247191"
      ]
     },
     "execution_count": 94,
     "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": 95,
   "id": "e5024920",
   "metadata": {},
   "outputs": [],
   "source": [
    "#applying decision tree with hyper parameter tuning\n",
    "dt_classifier1 = DecisionTreeClassifier()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 96,
   "id": "2be5be87",
   "metadata": {},
   "outputs": [],
   "source": [
    "param_grid1 = {\n",
    "    'max_depth': [2,3],            # Maximum depth of the tree\n",
    "    'max_leaf_nodes': [4,6]       # Maximum number of leaf nodes in the tree\n",
    "}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 97,
   "id": "a9a2fed8",
   "metadata": {},
   "outputs": [],
   "source": [
    "grid_search1 = GridSearchCV(dt_classifier1, param_grid1, cv=3, scoring='accuracy')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 98,
   "id": "f30fe911",
   "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>GridSearchCV(cv=3, estimator=DecisionTreeClassifier(),\n",
       "             param_grid={&#x27;max_depth&#x27;: [2, 3], &#x27;max_leaf_nodes&#x27;: [4, 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-7\" type=\"checkbox\" ><label for=\"sk-estimator-id-7\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">GridSearchCV</label><div class=\"sk-toggleable__content\"><pre>GridSearchCV(cv=3, estimator=DecisionTreeClassifier(),\n",
       "             param_grid={&#x27;max_depth&#x27;: [2, 3], &#x27;max_leaf_nodes&#x27;: [4, 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-8\" type=\"checkbox\" ><label for=\"sk-estimator-id-8\" 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-9\" type=\"checkbox\" ><label for=\"sk-estimator-id-9\" 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=3, estimator=DecisionTreeClassifier(),\n",
       "             param_grid={'max_depth': [2, 3], 'max_leaf_nodes': [4, 6]},\n",
       "             scoring='accuracy')"
      ]
     },
     "execution_count": 98,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "grid_search1.fit(x_train, y_train) "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 99,
   "id": "3dee5bfa",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'max_depth': 3, 'max_leaf_nodes': 6}"
      ]
     },
     "execution_count": 99,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "best_params = grid_search1.best_params_\n",
    "best_params"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 100,
   "id": "ffdfa398",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style>#sk-container-id-6 {color: black;}#sk-container-id-6 pre{padding: 0;}#sk-container-id-6 div.sk-toggleable {background-color: white;}#sk-container-id-6 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-6 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-6 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-6 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-6 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-6 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-6 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-6 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-6 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-6 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-6 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-6 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-6 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-6 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-6 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-6 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-6 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-6 div.sk-item {position: relative;z-index: 1;}#sk-container-id-6 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-6 div.sk-item::before, #sk-container-id-6 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-6 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-6 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-6 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-6 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-6 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-6 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-6 div.sk-label-container {text-align: center;}#sk-container-id-6 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-6 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-6\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>DecisionTreeClassifier(max_depth=3, max_leaf_nodes=6)</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-10\" type=\"checkbox\" checked><label for=\"sk-estimator-id-10\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">DecisionTreeClassifier</label><div class=\"sk-toggleable__content\"><pre>DecisionTreeClassifier(max_depth=3, max_leaf_nodes=6)</pre></div></div></div></div></div>"
      ],
      "text/plain": [
       "DecisionTreeClassifier(max_depth=3, max_leaf_nodes=6)"
      ]
     },
     "execution_count": 100,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#we are creating a new decision tree classifier with the best hyper parameters\n",
    "best_dt_classifier1 = DecisionTreeClassifier(**best_params)\n",
    "best_dt_classifier1.fit(x_train,y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 101,
   "id": "24096986",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0, 0, 0, 1, 1, 0, 1, 1, 0, 1, 0, 1, 0, 1, 1, 1, 0, 0, 0, 1, 0, 1,\n",
       "       0, 0, 1, 1, 0, 1, 1, 1, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n",
       "       1, 0, 0, 1, 0, 0, 0, 0, 1, 0, 0, 0, 0, 1, 0, 0, 0, 1, 1, 0, 1, 0,\n",
       "       1, 0, 1, 1, 1, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 1, 1, 0, 0, 1, 1, 1,\n",
       "       1, 0, 0, 0, 1, 1, 1, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 0, 1,\n",
       "       0, 1, 0, 1, 0, 1, 1, 1, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0,\n",
       "       0, 1, 0, 0, 0, 1, 0, 0, 0, 1, 0, 1, 1, 1, 0, 1, 1, 0, 0, 1, 1, 0,\n",
       "       1, 0, 1, 0, 1, 1, 0, 0, 1, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 1, 0,\n",
       "       1, 0, 0])"
      ]
     },
     "execution_count": 101,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_pred_tune=best_dt_classifier1.predict(x_test)\n",
    "y_pred_tune"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 102,
   "id": "a6f3c1fe",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.8044692737430168"
      ]
     },
     "execution_count": 102,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "accuracy_decision_tune1= accuracy_score(y_test, y_pred_tune)\n",
    "accuracy_decision_tune1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 103,
   "id": "848f8f48",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.8044692737430168"
      ]
     },
     "execution_count": 103,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "test_score_decision_tune1=best_dt_classifier1.score(x_test,y_test)\n",
    "test_score_decision_tune1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 104,
   "id": "2228c507",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.8089887640449438"
      ]
     },
     "execution_count": 104,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_score_decision_tune1=best_dt_classifier1.score(x_train,y_train)\n",
    "train_score_decision_tune1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 105,
   "id": "c2c4976e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style>#sk-container-id-7 {color: black;}#sk-container-id-7 pre{padding: 0;}#sk-container-id-7 div.sk-toggleable {background-color: white;}#sk-container-id-7 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-7 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-7 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-7 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-7 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-7 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-7 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-7 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-7 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-7 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-7 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-7 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-7 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-7 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-7 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-7 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-7 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-7 div.sk-item {position: relative;z-index: 1;}#sk-container-id-7 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-7 div.sk-item::before, #sk-container-id-7 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-7 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-7 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-7 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-7 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-7 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-7 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-7 div.sk-label-container {text-align: center;}#sk-container-id-7 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-7 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-7\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>LogisticRegression(solver=&#x27;liblinear&#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\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-11\" type=\"checkbox\" checked><label for=\"sk-estimator-id-11\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">LogisticRegression</label><div class=\"sk-toggleable__content\"><pre>LogisticRegression(solver=&#x27;liblinear&#x27;)</pre></div></div></div></div></div>"
      ],
      "text/plain": [
       "LogisticRegression(solver='liblinear')"
      ]
     },
     "execution_count": 105,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "lr=LogisticRegression(solver='liblinear')#solver='liblinear' is used for algorithm optimization, however it is optional\n",
    "lr.fit(x_train,y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 106,
   "id": "dd37eed1",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0, 0, 0, 1, 1, 0, 1, 1, 1, 1, 0, 1, 0, 1, 1, 1, 0, 0, 0, 0, 0, 1,\n",
       "       0, 0, 1, 1, 0, 1, 1, 1, 0, 1, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0,\n",
       "       1, 0, 0, 1, 0, 0, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 0, 1, 0,\n",
       "       1, 0, 1, 1, 1, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 1, 0, 0, 1, 1, 0,\n",
       "       1, 0, 0, 0, 0, 1, 1, 0, 1, 0, 0, 1, 0, 0, 0, 0, 1, 1, 1, 0, 0, 1,\n",
       "       0, 1, 0, 1, 0, 1, 1, 1, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0,\n",
       "       0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 1, 1, 1, 0, 1, 1, 0, 0, 1, 1, 0,\n",
       "       1, 0, 1, 0, 1, 1, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 1, 0,\n",
       "       1, 0, 0])"
      ]
     },
     "execution_count": 106,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_pred=lr.predict(x_test)\n",
    "y_pred"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 107,
   "id": "5a8004c6",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(179,)"
      ]
     },
     "execution_count": 107,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_pred.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 108,
   "id": "35ab90fa",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.8212290502793296"
      ]
     },
     "execution_count": 108,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#finding the evaluation metrics\n",
    "accuracy_logistic=accuracy_score(y_test,y_pred)\n",
    "accuracy_logistic"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 109,
   "id": "fc9b71f6",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.8033707865168539"
      ]
     },
     "execution_count": 109,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_score=lr.score(x_train,y_train)\n",
    "train_score"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 110,
   "id": "c74469ad",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.8212290502793296"
      ]
     },
     "execution_count": 110,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "test_score=lr.score(x_test,y_test)\n",
    "test_score"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 111,
   "id": "486b24b1",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.7761194029850746"
      ]
     },
     "execution_count": 111,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "precision_logistic=precision_score(y_test,y_pred)\n",
    "precision_logistic"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 112,
   "id": "1092f925",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.7536231884057971"
      ]
     },
     "execution_count": 112,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "recall_logistic=recall_score(y_test,y_pred)\n",
    "recall_logistic"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 113,
   "id": "b05efd96",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[95, 15],\n",
       "       [17, 52]])"
      ]
     },
     "execution_count": 113,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "confusion_matrix_logistic=confusion_matrix(y_test,y_pred)\n",
    "confusion_matrix_logistic"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 114,
   "id": "d9b734ae",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.85      0.86      0.86       110\n",
      "           1       0.78      0.75      0.76        69\n",
      "\n",
      "    accuracy                           0.82       179\n",
      "   macro avg       0.81      0.81      0.81       179\n",
      "weighted avg       0.82      0.82      0.82       179\n",
      "\n"
     ]
    }
   ],
   "source": [
    "classification_report_logistic=classification_report(y_test,y_pred)\n",
    "print(classification_report_logistic)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 115,
   "id": "48f2def0",
   "metadata": {},
   "outputs": [],
   "source": [
    "#TN=95,95 people in the titanic ship has died(considering negative cases as not survived) and model has identified that correctly\n",
    "#FP=15,15 people have survived but model has  identified incorrectly, positive but falsely\n",
    "#FN=17, 17 people have died but model has identified incorrectly, negative but falsely\n",
    "#TP=52, 52 people in the titanic ship has survived(considering positive cases as survived) and model has identified that correctly"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 116,
   "id": "94882cc1",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style>#sk-container-id-8 {color: black;}#sk-container-id-8 pre{padding: 0;}#sk-container-id-8 div.sk-toggleable {background-color: white;}#sk-container-id-8 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-8 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-8 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-8 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-8 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-8 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-8 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-8 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-8 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-8 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-8 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-8 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-8 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-8 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-8 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-8 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-8 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-8 div.sk-item {position: relative;z-index: 1;}#sk-container-id-8 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-8 div.sk-item::before, #sk-container-id-8 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-8 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-8 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-8 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-8 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-8 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-8 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-8 div.sk-label-container {text-align: center;}#sk-container-id-8 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-8 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-8\" 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-12\" type=\"checkbox\" checked><label for=\"sk-estimator-id-12\" 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": 116,
     "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": 117,
   "id": "84cece27",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0, 0, 0, 1, 1, 0, 1, 1, 0, 1, 0, 1, 0, 1, 1, 1, 0, 0, 0, 1, 0, 1,\n",
       "       0, 0, 1, 1, 0, 1, 1, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0,\n",
       "       1, 0, 0, 1, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 1, 0, 1, 0,\n",
       "       1, 0, 1, 1, 0, 1, 0, 0, 0, 1, 1, 1, 0, 1, 0, 1, 1, 0, 0, 1, 1, 0,\n",
       "       1, 1, 0, 0, 1, 1, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 0, 0, 1,\n",
       "       0, 1, 0, 0, 1, 1, 0, 1, 0, 1, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 1, 0,\n",
       "       0, 1, 0, 0, 0, 1, 0, 0, 0, 1, 0, 1, 1, 0, 0, 0, 1, 0, 0, 0, 0, 0,\n",
       "       1, 0, 0, 0, 1, 1, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 1, 0,\n",
       "       1, 0, 0])"
      ]
     },
     "execution_count": 117,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_pred_rf=rf.predict(x_test)\n",
    "y_pred_rf"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 118,
   "id": "78c763e0",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.7932960893854749"
      ]
     },
     "execution_count": 118,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "accuracy_rf= accuracy_score(y_test, y_pred_rf)\n",
    "accuracy_rf"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 119,
   "id": "47a7204f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.7932960893854749"
      ]
     },
     "execution_count": 119,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "test_score_rf=rf.score(x_test,y_test)\n",
    "test_score_rf"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 120,
   "id": "b47306f7",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.9480337078651685"
      ]
     },
     "execution_count": 120,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_score_rf=rf.score(x_train,y_train)\n",
    "train_score_rf"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 136,
   "id": "239580e4",
   "metadata": {},
   "outputs": [],
   "source": [
    "rf_tuned=RandomForestClassifier(random_state=0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 137,
   "id": "e5310e81",
   "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": 138,
   "id": "99397308",
   "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": 139,
   "id": "562e8f64",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style>#sk-container-id-11 {color: black;}#sk-container-id-11 pre{padding: 0;}#sk-container-id-11 div.sk-toggleable {background-color: white;}#sk-container-id-11 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-11 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-11 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-11 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-11 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-11 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-11 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-11 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-11 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-11 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-11 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-11 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-11 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-11 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-11 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-11 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-11 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-11 div.sk-item {position: relative;z-index: 1;}#sk-container-id-11 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-11 div.sk-item::before, #sk-container-id-11 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-11 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-11 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-11 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-11 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-11 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-11 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-11 div.sk-label-container {text-align: center;}#sk-container-id-11 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-11 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-11\" 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-17\" type=\"checkbox\" ><label for=\"sk-estimator-id-17\" 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-18\" type=\"checkbox\" ><label for=\"sk-estimator-id-18\" 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-19\" type=\"checkbox\" ><label for=\"sk-estimator-id-19\" 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": 139,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "rf_gridsearch.fit(x_train, y_train) "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 140,
   "id": "47562690",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'criterion': 'entropy',\n",
       " 'max_depth': 5,\n",
       " 'max_features': 'sqrt',\n",
       " 'max_leaf_nodes': 20,\n",
       " 'min_samples_leaf': 2,\n",
       " 'min_samples_split': 6}"
      ]
     },
     "execution_count": 140,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "best_params_rf = rf_gridsearch.best_params_\n",
    "best_params_rf"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 141,
   "id": "0444f55d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style>#sk-container-id-12 {color: black;}#sk-container-id-12 pre{padding: 0;}#sk-container-id-12 div.sk-toggleable {background-color: white;}#sk-container-id-12 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-12 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-12 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-12 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-12 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-12 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-12 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-12 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-12 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-12 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-12 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-12 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-12 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-12 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-12 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-12 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-12 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-12 div.sk-item {position: relative;z-index: 1;}#sk-container-id-12 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-12 div.sk-item::before, #sk-container-id-12 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-12 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-12 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-12 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-12 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-12 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-12 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-12 div.sk-label-container {text-align: center;}#sk-container-id-12 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-12 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-12\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>RandomForestClassifier(criterion=&#x27;entropy&#x27;, max_depth=5, max_leaf_nodes=20,\n",
       "                       min_samples_leaf=2, min_samples_split=6)</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-20\" type=\"checkbox\" checked><label for=\"sk-estimator-id-20\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">RandomForestClassifier</label><div class=\"sk-toggleable__content\"><pre>RandomForestClassifier(criterion=&#x27;entropy&#x27;, max_depth=5, max_leaf_nodes=20,\n",
       "                       min_samples_leaf=2, min_samples_split=6)</pre></div></div></div></div></div>"
      ],
      "text/plain": [
       "RandomForestClassifier(criterion='entropy', max_depth=5, max_leaf_nodes=20,\n",
       "                       min_samples_leaf=2, min_samples_split=6)"
      ]
     },
     "execution_count": 141,
     "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": 142,
   "id": "0d5b8811",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0, 0, 0, 1, 1, 0, 1, 1, 0, 1, 0, 1, 0, 1, 1, 1, 0, 0, 0, 0, 0, 1,\n",
       "       0, 0, 0, 1, 0, 0, 1, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n",
       "       1, 0, 0, 1, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0,\n",
       "       1, 0, 1, 1, 1, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 1, 0, 0, 1, 1, 0,\n",
       "       1, 0, 0, 0, 1, 1, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 1, 0, 0, 1,\n",
       "       0, 1, 0, 0, 0, 1, 0, 1, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0,\n",
       "       0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 1, 1, 0, 0, 1, 1, 0, 0, 0, 0, 0,\n",
       "       1, 0, 1, 0, 1, 1, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 1, 0,\n",
       "       1, 0, 0])"
      ]
     },
     "execution_count": 142,
     "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": 143,
   "id": "5ca63c40",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.8156424581005587"
      ]
     },
     "execution_count": 143,
     "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": 144,
   "id": "fcc20b59",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.8497191011235955"
      ]
     },
     "execution_count": 144,
     "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": 145,
   "id": "02f88c09",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.8156424581005587"
      ]
     },
     "execution_count": 145,
     "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": 146,
   "id": "04140623",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "62.31884057971014"
      ]
     },
     "execution_count": 146,
     "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": 147,
   "id": "5912f2ea",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "86.0"
      ]
     },
     "execution_count": 147,
     "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": 148,
   "id": "c18c2ee6",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "72.26890756302521"
      ]
     },
     "execution_count": 148,
     "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": 149,
   "id": "64fa7776",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[103,   7],\n",
       "       [ 26,  43]])"
      ]
     },
     "execution_count": 149,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "confusion_rf = confusion_matrix(y_test, y_pred_rf_tune)\n",
    "confusion_rf"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 150,
   "id": "1a33e5d4",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.80      0.94      0.86       110\n",
      "           1       0.86      0.62      0.72        69\n",
      "\n",
      "    accuracy                           0.82       179\n",
      "   macro avg       0.83      0.78      0.79       179\n",
      "weighted avg       0.82      0.82      0.81       179\n",
      "\n"
     ]
    }
   ],
   "source": [
    "classification_report__random=classification_report(y_test, y_pred_rf_tune)\n",
    "print(classification_report__random)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 206,
   "id": "dc076bf4",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(891, 8)"
      ]
     },
     "execution_count": 206,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "titanic_data.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 207,
   "id": "75d3777d",
   "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='survived', ylabel='count'>"
      ]
     },
     "execution_count": 207,
     "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='survived',data=titanic_data)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 208,
   "id": "0680afa0",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "342"
      ]
     },
     "execution_count": 208,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "count_survived=len(titanic_data[titanic_data.survived==1])\n",
    "count_survived"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 209,
   "id": "a1da6957",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "549"
      ]
     },
     "execution_count": 209,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "count_not_survived=len(titanic_data[titanic_data.survived==0])\n",
    "count_not_survived"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 210,
   "id": "eabbd4c5",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Survived %: 38.38%\n"
     ]
    }
   ],
   "source": [
    "#find the percentage of passengers surviving\n",
    "print(\"Survived %: {:.2f}%\".format((count_survived / (len(titanic_data.survived))*100)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 211,
   "id": "21be0c5d",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Not Survived %: 61.62%\n"
     ]
    }
   ],
   "source": [
    "#find the percentage of passengers not surviving\n",
    "print(\"Not Survived %: {:.2f}%\".format((count_not_survived / (len(titanic_data.survived))*100)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 212,
   "id": "e5a6870c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "314"
      ]
     },
     "execution_count": 212,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "female_passengers=len(titanic_data[titanic_data.sex=='female'])\n",
    "female_passengers"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 213,
   "id": "f660b2b8",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "577"
      ]
     },
     "execution_count": 213,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "male_passengers=len(titanic_data[titanic_data.sex=='male'])\n",
    "male_passengers"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 214,
   "id": "f9b2bbf4",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Female passengers  %: 35.24%\n"
     ]
    }
   ],
   "source": [
    "#find the percentage of female passengers\n",
    "print(\"Female passengers  %: {:.2f}%\".format((female_passengers / (len(titanic_data.survived))*100)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 215,
   "id": "f218c799",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Male passengers  %: 64.76%\n"
     ]
    }
   ],
   "source": [
    "#find the percentage of male passengers\n",
    "print(\"Male passengers  %: {:.2f}%\".format((male_passengers / (len(titanic_data.survived))*100)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 216,
   "id": "5a04dd8a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "109"
      ]
     },
     "execution_count": 216,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# male passengers who survived\n",
    "male_passengers_survived = len(titanic_data[(titanic_data['sex'] == 'male') & (titanic_data['survived'] == 1)])\n",
    "male_passengers_survived"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 217,
   "id": "0af614f3",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "233"
      ]
     },
     "execution_count": 217,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# female passengers who survived\n",
    "female_passengers_survived = len(titanic_data[(titanic_data['sex'] == 'female') & (titanic_data['survived'] == 1)])\n",
    "female_passengers_survived"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 218,
   "id": "a435adeb",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Male passengers  %: 18.89%\n"
     ]
    }
   ],
   "source": [
    "#find the percentage of male passengers who survived\n",
    "print(\"Male passengers  %: {:.2f}%\".format((male_passengers_survived / male_passengers*100)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 219,
   "id": "d22a0e42",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Female passengers  %: 74.20%\n"
     ]
    }
   ],
   "source": [
    "#find the percentage of female passengers who survived\n",
    "print(\"Female passengers  %: {:.2f}%\".format((female_passengers_survived / female_passengers*100)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 220,
   "id": "50546d82",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "6"
      ]
     },
     "execution_count": 220,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "male_passengers_survived_age_more_50 = len(titanic_data[(titanic_data['sex'] == 'male') & (titanic_data['age'] > 50) & (titanic_data['survived'] == 1)])\n",
    "male_passengers_survived_age_more_50"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 221,
   "id": "c2ecaba0",
   "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>survived</th>\n",
       "      <th>pclass</th>\n",
       "      <th>sex</th>\n",
       "      <th>age</th>\n",
       "      <th>sibsp</th>\n",
       "      <th>parch</th>\n",
       "      <th>embarked</th>\n",
       "      <th>deck</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>449</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>male</td>\n",
       "      <td>52.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>S</td>\n",
       "      <td>C</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>570</th>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>male</td>\n",
       "      <td>62.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>S</td>\n",
       "      <td>other</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>587</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>male</td>\n",
       "      <td>60.0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>C</td>\n",
       "      <td>B</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>630</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>male</td>\n",
       "      <td>80.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>S</td>\n",
       "      <td>A</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>647</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>male</td>\n",
       "      <td>56.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>C</td>\n",
       "      <td>A</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>857</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>male</td>\n",
       "      <td>51.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>S</td>\n",
       "      <td>E</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     survived  pclass   sex   age  sibsp  parch embarked   deck\n",
       "449         1       1  male  52.0      0      0        S      C\n",
       "570         1       2  male  62.0      0      0        S  other\n",
       "587         1       1  male  60.0      1      1        C      B\n",
       "630         1       1  male  80.0      0      0        S      A\n",
       "647         1       1  male  56.0      0      0        C      A\n",
       "857         1       1  male  51.0      0      0        S      E"
      ]
     },
     "execution_count": 221,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "male_passengers_survived_age_more_50_data = titanic_data[(titanic_data['sex'] == 'male') & (titanic_data['age'] > 50) & (titanic_data['survived'] == 1)]\n",
    "male_passengers_survived_age_more_50_data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 222,
   "id": "3ff4cebb",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "\n",
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       "</style>\n",
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>survived</th>\n",
       "      <th>pclass</th>\n",
       "      <th>sex</th>\n",
       "      <th>age</th>\n",
       "      <th>sibsp</th>\n",
       "      <th>parch</th>\n",
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       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>male</td>\n",
       "      <td>30.00</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>S</td>\n",
       "      <td>other</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>male</td>\n",
       "      <td>34.00</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>S</td>\n",
       "      <td>D</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>male</td>\n",
       "      <td>28.00</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>S</td>\n",
       "      <td>A</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>36</th>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>male</td>\n",
       "      <td>30.00</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>C</td>\n",
       "      <td>other</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>55</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>male</td>\n",
       "      <td>30.00</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>S</td>\n",
       "      <td>C</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>831</th>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>male</td>\n",
       "      <td>0.83</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>S</td>\n",
       "      <td>other</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>838</th>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>male</td>\n",
       "      <td>32.00</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>S</td>\n",
       "      <td>other</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>839</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>male</td>\n",
       "      <td>30.00</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>C</td>\n",
       "      <td>C</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>869</th>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>male</td>\n",
       "      <td>4.00</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>S</td>\n",
       "      <td>other</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>889</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>male</td>\n",
       "      <td>26.00</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>C</td>\n",
       "      <td>C</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>103 rows × 8 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     survived  pclass   sex    age  sibsp  parch embarked   deck\n",
       "17          1       2  male  30.00      0      0        S  other\n",
       "21          1       2  male  34.00      0      0        S      D\n",
       "23          1       1  male  28.00      0      0        S      A\n",
       "36          1       3  male  30.00      0      0        C  other\n",
       "55          1       1  male  30.00      0      0        S      C\n",
       "..        ...     ...   ...    ...    ...    ...      ...    ...\n",
       "831         1       2  male   0.83      1      1        S  other\n",
       "838         1       3  male  32.00      0      0        S  other\n",
       "839         1       1  male  30.00      0      0        C      C\n",
       "869         1       3  male   4.00      1      1        S  other\n",
       "889         1       1  male  26.00      0      0        C      C\n",
       "\n",
       "[103 rows x 8 columns]"
      ]
     },
     "execution_count": 222,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "male_passengers_survived_age_less_50_data = titanic_data[(titanic_data['sex'] == 'male') & (titanic_data['age'] <= 50) & (titanic_data['survived'] == 1)]\n",
    "male_passengers_survived_age_less_50_data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 223,
   "id": "e5615a6d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
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       "      <td>1</td>\n",
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       "    <tr>\n",
       "      <th>366</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>female</td>\n",
       "      <td>60.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>C</td>\n",
       "      <td>D</td>\n",
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       "    <tr>\n",
       "      <th>483</th>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
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       "      <td>0</td>\n",
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       "    </tr>\n",
       "    <tr>\n",
       "      <th>570</th>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>male</td>\n",
       "      <td>62.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>S</td>\n",
       "      <td>other</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>587</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>male</td>\n",
       "      <td>60.0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>C</td>\n",
       "      <td>B</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>829</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>female</td>\n",
       "      <td>62.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>S</td>\n",
       "      <td>B</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     survived  pclass     sex   age  sibsp  parch embarked   deck\n",
       "275         1       1  female  63.0      1      0        S      D\n",
       "366         1       1  female  60.0      1      0        C      D\n",
       "483         1       3  female  63.0      0      0        S  other\n",
       "570         1       2    male  62.0      0      0        S  other\n",
       "587         1       1    male  60.0      1      1        C      B\n",
       "829         1       1  female  62.0      0      0        S      B"
      ]
     },
     "execution_count": 223,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data1 = titanic_data[(titanic_data['age'] >= 60) & (titanic_data['age'] <= 70) & (titanic_data['survived'] == 1)]\n",
    "data1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 224,
   "id": "04d5c2b7",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "      <th>366</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>female</td>\n",
       "      <td>60.0</td>\n",
       "      <td>1</td>\n",
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       "      <td>1</td>\n",
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       "      <td>0</td>\n",
       "      <td>0</td>\n",
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       "    </tr>\n",
       "    <tr>\n",
       "      <th>570</th>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
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       "      <td>62.0</td>\n",
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       "    <tr>\n",
       "      <th>587</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>male</td>\n",
       "      <td>60.0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>C</td>\n",
       "      <td>B</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>829</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>female</td>\n",
       "      <td>62.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>S</td>\n",
       "      <td>B</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     survived  pclass     sex   age  sibsp  parch embarked   deck\n",
       "275         1       1  female  63.0      1      0        S      D\n",
       "366         1       1  female  60.0      1      0        C      D\n",
       "483         1       3  female  63.0      0      0        S  other\n",
       "570         1       2    male  62.0      0      0        S  other\n",
       "587         1       1    male  60.0      1      1        C      B\n",
       "829         1       1  female  62.0      0      0        S      B"
      ]
     },
     "execution_count": 224,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "titanic_data[titanic_data['age'].between(60, 70) & (titanic_data['survived'] == 1)]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 225,
   "id": "e674ed66",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "103"
      ]
     },
     "execution_count": 225,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "male_passengers_survived_age_less_50 = len(titanic_data[(titanic_data['sex'] == 'male') & (titanic_data['age'] <= 50) & (titanic_data['survived'] == 1)])\n",
    "male_passengers_survived_age_less_50"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 226,
   "id": "c0ed14bc",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Male passengers survived having age less than 50  %: 17.85%\n"
     ]
    }
   ],
   "source": [
    "print(\"Male passengers survived having age less than 50  %: {:.2f}%\".format((male_passengers_survived_age_less_50 / male_passengers*100)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 227,
   "id": "ad7432fb",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Male passengers survived having age more than 50  %: 1.04%\n"
     ]
    }
   ],
   "source": [
    "print(\"Male passengers survived having age more than 50  %: {:.2f}%\".format((male_passengers_survived_age_more_50 / male_passengers*100)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 228,
   "id": "c21003f2",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "41"
      ]
     },
     "execution_count": 228,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "male_passengers_not_survived_age_more_50 = len(titanic_data[(titanic_data['sex'] == 'male') & (titanic_data['age'] > 50) & (titanic_data['survived'] == 0)])\n",
    "male_passengers_not_survived_age_more_50"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 229,
   "id": "dc510e2d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "427"
      ]
     },
     "execution_count": 229,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "male_passengers_not_survived_age_less_50 = len(titanic_data[(titanic_data['sex'] == 'male') & (titanic_data['age'] <= 50) & (titanic_data['survived'] == 0)])\n",
    "male_passengers_not_survived_age_less_50"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 230,
   "id": "3efbbe07",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Male passengers not survived having age less than 50  %: 74.00%\n"
     ]
    }
   ],
   "source": [
    "print(\"Male passengers not survived having age less than 50  %: {:.2f}%\".format((male_passengers_not_survived_age_less_50/ male_passengers*100)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 231,
   "id": "0abe7eb9",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Male passengers not survived having age more than 50  %: 7.11%\n"
     ]
    }
   ],
   "source": [
    "print(\"Male passengers not survived having age more than 50  %: {:.2f}%\".format((male_passengers_not_survived_age_more_50/ male_passengers*100)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 232,
   "id": "8a776e6a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "16"
      ]
     },
     "execution_count": 232,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "female_passengers_survived_age_more_50 = len(titanic_data[(titanic_data['sex'] == 'female') & (titanic_data['age'] > 50) & (titanic_data['survived'] == 1)])\n",
    "female_passengers_survived_age_more_50"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 233,
   "id": "65029065",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "217"
      ]
     },
     "execution_count": 233,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "female_passengers_survived_age_less_50 = len(titanic_data[(titanic_data['sex'] == 'female') & (titanic_data['age'] <= 50) & (titanic_data['survived'] == 1)])\n",
    "female_passengers_survived_age_less_50"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 234,
   "id": "8d5783fa",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Female passengers survived having age less than 50  %: 69.11%\n"
     ]
    }
   ],
   "source": [
    "print(\"Female passengers survived having age less than 50  %: {:.2f}%\".format((female_passengers_survived_age_less_50 / female_passengers*100)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 235,
   "id": "2647aede",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Female passengers  survived having age more than 50  %: 5.10%\n"
     ]
    }
   ],
   "source": [
    "print(\"Female passengers  survived having age more than 50  %: {:.2f}%\".format((female_passengers_survived_age_more_50 / female_passengers*100)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 236,
   "id": "22306e35",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1"
      ]
     },
     "execution_count": 236,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "female_passengers_not_survived_age_more_50 = len(titanic_data[(titanic_data['sex'] == 'female') & (titanic_data['age'] > 50) & (titanic_data['survived'] == 0)])\n",
    "female_passengers_not_survived_age_more_50"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 237,
   "id": "eb1ddb6c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "80"
      ]
     },
     "execution_count": 237,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "female_passengers_not_survived_age_less_50 = len(titanic_data[(titanic_data['sex'] == 'female') & (titanic_data['age'] <= 50) & (titanic_data['survived'] == 0)])\n",
    "female_passengers_not_survived_age_less_50\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 238,
   "id": "34854885",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Female passengers not survived having age less than 50  %: 25.48%\n"
     ]
    }
   ],
   "source": [
    "print(\"Female passengers not survived having age less than 50  %: {:.2f}%\".format((female_passengers_not_survived_age_less_50/ female_passengers*100)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 239,
   "id": "3390645f",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Female passengers not survived having age more than 50  %: 0.32%\n"
     ]
    }
   ],
   "source": [
    "print(\"Female passengers not survived having age more than 50  %: {:.2f}%\".format((female_passengers_not_survived_age_more_50/ female_passengers*100)))"
   ]
  }
 ],
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