Fraud Detection in Credit Card Transactions using ML Techniques.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Fraud Detection in Credit Card Transactions using ML Techniques\n",
    "*Oladimeji Salau*  \n",
    "https://github.com/dimtics"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Introduction\n",
    "The objective in this project is to build machine learning models to classify or identify fraudulent card transactions from a given card transactions data.  "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Data Description"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The [dataset](https://www.kaggle.com/mlg-ulb/creditcardfraud/data) contains two-days credit card transactions made in September 2013 by European cardholders. The dataset is highly unbalanced with a low percentage of fraudulent transactions within several records of normal transactions. The positive class (frauds) account for 0.172% (492 frauds out of 284,807 transactions) of all transactions.\n",
    "\n",
    "Features V1, V2, ... V28 are the principal components obtained with PCA, the only features which have not been transformed with PCA are 'Time' and 'Amount'. Feature 'Time' contains the seconds elapsed between each transaction and the first transaction in the dataset.  Feature 'Class' is the target variable with value 1 in case of fraud and 0 otherwise. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Import Libraries"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Import basic libraries \n",
    "import pandas as pd\n",
    "from pandas.plotting import scatter_matrix\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import os\n",
    "from imblearn.over_sampling import ADASYN \n",
    "from collections import Counter\n",
    "import seaborn as sn\n",
    "\n",
    "# plot functions\n",
    "import plot_functions as pf\n",
    "\n",
    "# scikit packages\n",
    "from sklearn.preprocessing import StandardScaler\n",
    "from sklearn.model_selection import train_test_split, cross_val_score\n",
    "from sklearn.linear_model import LogisticRegression\n",
    "from sklearn.ensemble import RandomForestClassifier, GradientBoostingClassifier\n",
    "from sklearn.naive_bayes import BernoulliNB \n",
    "from sklearn import metrics\n",
    "\n",
    "# settings\n",
    "%matplotlib inline\n",
    "sn.set_style(\"dark\")\n",
    "sn.set_palette(\"colorblind\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Load Data  \n",
    "The dataset used in this project is freely available at: https://www.kaggle.com/mlg-ulb/creditcardfraud/data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "df = pd.read_csv(\"data/creditcard.csv\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Time</th>\n",
       "      <th>V1</th>\n",
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       "      <th>V27</th>\n",
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       "      <td>-0.139097</td>\n",
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       "   Time        V1        V2        V3        V4        V5        V6        V7  \\\n",
       "0   0.0 -1.359807 -0.072781  2.536347  1.378155 -0.338321  0.462388  0.239599   \n",
       "1   0.0  1.191857  0.266151  0.166480  0.448154  0.060018 -0.082361 -0.078803   \n",
       "2   1.0 -1.358354 -1.340163  1.773209  0.379780 -0.503198  1.800499  0.791461   \n",
       "3   1.0 -0.966272 -0.185226  1.792993 -0.863291 -0.010309  1.247203  0.237609   \n",
       "4   2.0 -1.158233  0.877737  1.548718  0.403034 -0.407193  0.095921  0.592941   \n",
       "\n",
       "         V8        V9  ...         V21       V22       V23       V24  \\\n",
       "0  0.098698  0.363787  ...   -0.018307  0.277838 -0.110474  0.066928   \n",
       "1  0.085102 -0.255425  ...   -0.225775 -0.638672  0.101288 -0.339846   \n",
       "2  0.247676 -1.514654  ...    0.247998  0.771679  0.909412 -0.689281   \n",
       "3  0.377436 -1.387024  ...   -0.108300  0.005274 -0.190321 -1.175575   \n",
       "4 -0.270533  0.817739  ...   -0.009431  0.798278 -0.137458  0.141267   \n",
       "\n",
       "        V25       V26       V27       V28  Amount  Class  \n",
       "0  0.128539 -0.189115  0.133558 -0.021053  149.62      0  \n",
       "1  0.167170  0.125895 -0.008983  0.014724    2.69      0  \n",
       "2 -0.327642 -0.139097 -0.055353 -0.059752  378.66      0  \n",
       "3  0.647376 -0.221929  0.062723  0.061458  123.50      0  \n",
       "4 -0.206010  0.502292  0.219422  0.215153   69.99      0  \n",
       "\n",
       "[5 rows x 31 columns]"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# View top 5 records\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The dataset contains 284807 rows and 31 columns.\n"
     ]
    }
   ],
   "source": [
    "# determine the number of records in the dataset\n",
    "print('The dataset contains {0} rows and {1} columns.'.format(df.shape[0], df.shape[1]))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 284807 entries, 0 to 284806\n",
      "Data columns (total 31 columns):\n",
      "Time      284807 non-null float64\n",
      "V1        284807 non-null float64\n",
      "V2        284807 non-null float64\n",
      "V3        284807 non-null float64\n",
      "V4        284807 non-null float64\n",
      "V5        284807 non-null float64\n",
      "V6        284807 non-null float64\n",
      "V7        284807 non-null float64\n",
      "V8        284807 non-null float64\n",
      "V9        284807 non-null float64\n",
      "V10       284807 non-null float64\n",
      "V11       284807 non-null float64\n",
      "V12       284807 non-null float64\n",
      "V13       284807 non-null float64\n",
      "V14       284807 non-null float64\n",
      "V15       284807 non-null float64\n",
      "V16       284807 non-null float64\n",
      "V17       284807 non-null float64\n",
      "V18       284807 non-null float64\n",
      "V19       284807 non-null float64\n",
      "V20       284807 non-null float64\n",
      "V21       284807 non-null float64\n",
      "V22       284807 non-null float64\n",
      "V23       284807 non-null float64\n",
      "V24       284807 non-null float64\n",
      "V25       284807 non-null float64\n",
      "V26       284807 non-null float64\n",
      "V27       284807 non-null float64\n",
      "V28       284807 non-null float64\n",
      "Amount    284807 non-null float64\n",
      "Class     284807 non-null int64\n",
      "dtypes: float64(30), int64(1)\n",
      "memory usage: 67.4 MB\n"
     ]
    }
   ],
   "source": [
    "# check for missing values and data types of the columns\n",
    "df.info()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Explore label class"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Normal transactions count:  284315\n",
      "Fraudulent transactions count:  492\n"
     ]
    }
   ],
   "source": [
    "print('Normal transactions count: ', df['Class'].value_counts().values[0])\n",
    "print('Fraudulent transactions count: ', df['Class'].value_counts().values[1])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Separate feature data (predictors) from labels"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "# feature data (predictors)\n",
    "X = df.iloc[:, :-1]\n",
    "\n",
    "# label class\n",
    "y = df['Class']"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Standardize data\n",
    "Scale the data to have zero mean and unit variance."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "scaler = StandardScaler()\n",
    "scaled_X = scaler.fit_transform(X)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Partition data into train and test sets"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Partition data into train and test sets\n",
    "X_train, X_test, y_train, y_test = train_test_split(scaled_X, y, test_size=0.33, random_state=42)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Data Sampling  \n",
    "As noted earlier, the dataset is unbalanced. Training unbalanced dataset with learning algorithms may lead to misclassification of minority class. Therefore, to compensate for the unbalancedness, we will use ADASYN oversampling method as implemented in imbalanced-learn package to resample the dataset.  \n",
    "ADASYN (ADAptive SYNthetic) is an [oversampling technique](https://www.datasciencecentral.com/profiles/blogs/handling-imbalanced-data-sets-in-supervised-learning-using-family) that adaptively generates minority data samples according to their distributions using K nearest neighbor. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Original dataset shape Counter({0: 190477, 1: 343})\n",
      "Resampled dataset shape Counter({0: 190477, 1: 190471})\n"
     ]
    }
   ],
   "source": [
    "# apply the ADASYN over-sampling\n",
    "ada = ADASYN(random_state=42)\n",
    "print('Original dataset shape {}'.format(Counter(y_train)))\n",
    "X_res, y_res = ada.fit_sample(X_train, y_train)\n",
    "print('Resampled dataset shape {}'.format(Counter(y_res)))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Train Models  \n",
    "Three machine learning algorithms: Logistic Regression, Naive Baye, and RandomForest classifiers were trained using the processed feature data."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "X_train, y_train = X_res, y_res \n",
    "\n",
    "# Train LogisticRegression Model\n",
    "LGR_Classifier = LogisticRegression()\n",
    "LGR_Classifier.fit(X_train, y_train);\n",
    "\n",
    "# Train Decision Tree Model\n",
    "RDF_Classifier = RandomForestClassifier(random_state=0)\n",
    "RDF_Classifier.fit(X_train, y_train);\n",
    "\n",
    "# Train Bernoulli Naive Baye Model\n",
    "BNB_Classifier = BernoulliNB()\n",
    "BNB_Classifier.fit(X_train, y_train);"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Evaluate Models  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "========================== Model Evaluation Results ========================\n",
      "\n",
      "===== RandomForest Classifier =====\n",
      "\n",
      "Cross Validation Mean Score:  92.5%\n",
      "\n",
      "Model Accuracy:  100.0%\n",
      "\n",
      "Confusion Matrix:\n",
      " [[190477      0]\n",
      " [     0 190471]]\n",
      "\n",
      "Classification Report:\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "          0       1.00      1.00      1.00    190477\n",
      "          1       1.00      1.00      1.00    190471\n",
      "\n",
      "avg / total       1.00      1.00      1.00    380948\n",
      "\n",
      "\n",
      "===== LogisticRegression =====\n",
      "\n",
      "Cross Validation Mean Score:  87.4%\n",
      "\n",
      "Model Accuracy:  90.10000000000001%\n",
      "\n",
      "Confusion Matrix:\n",
      " [[174053  16424]\n",
      " [ 21296 169175]]\n",
      "\n",
      "Classification Report:\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "          0       0.89      0.91      0.90    190477\n",
      "          1       0.91      0.89      0.90    190471\n",
      "\n",
      "avg / total       0.90      0.90      0.90    380948\n",
      "\n",
      "\n",
      "===== Naive Baiye Classifier =====\n",
      "\n",
      "Cross Validation Mean Score:  83.6%\n",
      "\n",
      "Model Accuracy:  84.7%\n",
      "\n",
      "Confusion Matrix:\n",
      " [[170272  20205]\n",
      " [ 38005 152466]]\n",
      "\n",
      "Classification Report:\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "          0       0.82      0.89      0.85    190477\n",
      "          1       0.88      0.80      0.84    190471\n",
      "\n",
      "avg / total       0.85      0.85      0.85    380948\n",
      "\n",
      "\n"
     ]
    }
   ],
   "source": [
    "# Evaluate models\n",
    "modlist = [('RandomForest Classifier', RDF_Classifier),('LogisticRegression', LGR_Classifier),\n",
    "('Naive Baiye Classifier', BNB_Classifier)] \n",
    "\n",
    "models = [j for j in modlist]\n",
    "\n",
    "print()\n",
    "print('========================== Model Evaluation Results ========================' \"\\n\")  \n",
    "\n",
    "for i, v in models:\n",
    "    scores = cross_val_score(v, X_train, y_train, cv=10)\n",
    "    accuracy = metrics.accuracy_score(y_train, v.predict(X_train))\n",
    "    confusion_matrix = metrics.confusion_matrix(y_train, v.predict(X_train))\n",
    "    classification = metrics.classification_report(y_train, v.predict(X_train))\n",
    "    print('===== {} ====='.format(i))\n",
    "    print()\n",
    "    print (\"Cross Validation Mean Score: \", '{}%'.format(np.round(scores.mean(), 3) * 100))  \n",
    "    print() \n",
    "    print (\"Model Accuracy: \", '{}%'.format(np.round(accuracy, 3) * 100)) \n",
    "    print()\n",
    "    print(\"Confusion Matrix:\" \"\\n\", confusion_matrix)\n",
    "    print()\n",
    "    print(\"Classification Report:\" \"\\n\", classification) \n",
    "    print()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Test Models"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "========================== Model Test Results ========================\n",
      "\n",
      "=== RandomForest Classifier ===\n",
      "Model Accuracy:  100.0%\n",
      "\n",
      "Confusion Matrix:\n",
      " [[93816    22]\n",
      " [   24   125]]\n",
      "\n"
     ]
    },
    {
     "data": {
      "image/png": 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OAeYADwLfTMZXEhHJiubsvl0OfBJYB1xLOGdJRCQrmhNKaXffRribSQbQ7puIZE1zQukvZjYbGGJmtwN/z3JNIpLDmnNG91Vm9nngNWCBuz+R/bJEJFc1p6P7q4TLS9YAfZJpEZGsaM4pAfsn31PAgcCH/GuMJRGRFtWc3bdv1/xsZilAu28ikjW7DCUzK6ozOZgw+qSISFY0Z/fNCaMFpIBK4MasViQiOa05ofQdd78/65WIiNC885TOy3oVIiKJ5rSUOpnZ64TduDSAu38lq1WJSM5qTihNz3oVIiKJpobD/ZW7n+buc1qzIBHJbU31KfVvtSpERBJN7b6NNLPvN7TA3a/KUj0ikuOaCqUKQue2iEiraSqUVrv7L1utEhERmu5TerXVqhARSTQaSu5+RWsWIiICusWSiERGoSQiUVEoiUhUFEoiEhWFkohERaEkIlFRKIlIVBRKIhIVhZKIREWhJCJRUSiJSFQUSiISFYWSiERFoSQiUVEoiUhUFEoiEhWFkohERaEkIlFRKIlIVBRKIhIVhZKIREWhJCJRUSiJSFSaukOudDAF5ZUMe3Ie2/oU186rGNibTcMHMuKJV1h69AS29ekOQM93V1CwtYr1Hx+xe9vYspWBL5eSymQgA2smGdt7dK1dPuAVJ11UwLoDR7bMi+rg+r22kM5lmymorCJVnWZ7cWeqOxWx9sD96nyWKVLVaSoG9mL9J/bbo+0Ubq6g5C9v8f7USUDjn2Ov0mX0XLSK6s6FAKw5ZOfPtyUolHJMVc+uLD/qoJ3mFZRXki7MZ9BLpSw95mAy+XvegO47/z02jNmHLUP603XVh/R7czGrJo8DoOfClXTasIXKAT336jXkknUTRgHQY/EqijZV1IZ5QXnlzp9lJsO+f3qdzWXlVPUubmx1Der+3mp6/3M5+du2185r7HPs/OFmVh+2f+0/r2xQKAkAVd27UNm/F33nL2bdQaMafEzntRvoN/+9neaV2b5sGdKvdnrthJGkC8OvVSqdqQ24zus20nndJjaOGkzRpoosvYrclapOk0qnyRTs/A+lZM588nZU105X9ejGB4eM2ekx6aIClh11ECMef6l2XmOfY6eycvq88z4FlVWUl/Sl7IBhLf5aFEo5pmhjBUOeeb12evVhH6v9ef34EQx9+lW2fLChwedu7d/rI62s+tKdigAo3FRBvzcWsXLyOPIrt9H3H0tYOXkc3Zd+0AKvQqDuZ5kik4INY4awvfvOu1Irjxy/y/Vs2affR+Y19DkCbB46gA1jSkgXFFDy4ltUrVjX4PP3RouHkpnlA38AugEnuHvZXq7vBqDU3e9pZPk9wEPu/tRurvdid//J3tTWHjW2+waQyc9j9aFjGTz3HTaOLPnIc5vTUgLosqaMAfPeZfUnx4Z+CA+7BvvMmV/bN1LVoyub9hvcwq8utzT0WdbXnJZSY+p/jmQybLAhpItCbGwp6UunsvL4QwkYDPRz94OzsO6WdDWQc6G0K9v6dGfT8IH0XrCUjaN3DqbmtJS6rCljwKsLWTFlPDu6dQZggw1hgw0B/tU3okBqHc1pKTWkoc8xb3s1w578O0umHkKmIJ+ua8qy8jlmI5T+BxhtZncAI4Bi4OvAV4GJQHdggbufY2YzgdXufruZjQVud/cpZnYyITTWAkVAqZlNAS5w92kAZrba3QfVbNTMCoHbgdGEUx2udvfnzWw+MAcYD2SALwIXA33M7GfufmEW3oN27cOPDaV4xfo9eu6A1xaSSqcZ9NICAKq6d+WDSdaS5UkraOxzXDd+BPs++yaZvBQVg3qzpaRvi287G6F0IfAQsArY6u6XmlkPoMzdP2dmecDbZrZPE+v4ITAJ+BD4fTO3ey6wzt2/bmZ9gReAA4AewGx3v8TMHgCOdffrzOySXAukHcVdWHb0RxuwH5mfl8fSY/asofv+sYc0uVwtpD1T/31r7LPcG4tP/Lfanxv7HDePGMTmEYMaXNZSst3R7cn3SmCAmc0Gygmtp8J6j00BmNlAYJO7r0+m5zay7lS96Y8Dk83s0GS6IAkngJqe3WVA5z15ISLSOrJ9Rnc6+X4ssK+7nw5cBXQhhMpWQh8UwITk+3qgp5n1T6ZrIrv2sWY2DOhTb1ulhBbRlGR7vwZqOtkzDdRWP9REJAKtdZnJK8B+ZvYS8AiwGCgBfgVMNbPngIMA3H0HcA7wRzP7M6FPCWAesMHMXga+B+x8GAjuAMaa2RxgLvC+u6dp3Dtmdn+LvDoRaTGpTKahRkRuS808X2+KSJZlZt7R4N6KLsgVkagolEQkKgolEYmKQklEoqJQEpGoKJREJCoKJRGJikJJRKKiUBKRqCiURCQqCiURiYpCSUSiolASkagolEQkKgolEYmKQklEoqJQEpGoKJREJCoKJRGJikJJRKKiUBKRqCiURCQqCiURiYpCSUSiolASkagolEQkKgolEYmKQklEoqJQEpGoKJREJCoKJRGJikJJRKKiUBKRqCiURCQqCiURiYpCSUSiolASkagolEQkKgolEYmKQklEoqJQEpGoKJREJCoKJRGJikJJRKKiUBKRqCiURCQqCiURiYpCSUSiolASkaikMplMW9cgIlJLLSURiYpCSUSiolASkagolEQkKgolAcDMurV1DdJyzCzV1jXsKR19E8xsJjAYuNLdN7ZxObIHzOyzQIW7z23rWvaWWkoCcBPQA7jazHq2dTGyRxYAj5vZK2Y2HNpva0mhlMPMLB/A3TcBXwMGADMVTO2HmeUBuPsK4HfACOCQZF4meUy7CiftvuUoM8tz97SZ7QNcACx39zvM7G5gPXCdu5e1bZXSlDqfYV+gEMgAXYAXgMuBv7n7yrascU8olHKYmQ0C7gV+A5wNPOfuV5nZI0Ap8J2a/7YSFzPLd/dqMxsAPAZsBZ4h7IqPSeYtB0509w/artLdp1DKQXWa89cDS4B7gBuAKcBc4FtA7/b4XzaXJIF0GfAK8E/gXGAF8HOgM1CQ7Na1K+pTyiE1fUhAftICehjYmHy/CfgpcBTQTYEUp3r9Q9OA84B33P0d4FeAAf8HWNseAwnUUsoZZpZy94yZlQB3AL8F3gQ+BL4MbAKOAy5198VtV6k0pk4fUi+gk7uvMbP/AqYCR7n7VjM7mNA/uKZtq91zCqUcUOeXuRswE9gG9CL0OSwiHK05CPimu/+jzQqVXUr+qTxK6Mw+GjiG0Kl9AnCIu1e0YXktoqCtC5DsSlpIaTMbCPwnsN3drzazkYSjbjuAB4Fr3H1rW9YqDTOzUcB7QAr4b2AW8FfgVMI/kulmliac0rGkrepsKepT6sCSFlImae7/iHC4eKKZnejui4BfAH2AZQqkOJnZ+cAVwAHuvgN4C/g48AjhwMRKM/u0u1/l7kvarNAWpFDqoOrssvUhdGK/4+4XEfqTTjKzae5eCnzX3de3abHSlPsJ/X5fM7NDCLveJwB/BEoIJ70uabPqskCh1AHVCaQBwFlAJ+BQMxvq7o8QfqGPNrNid9/epsXKrmxz96uAdYQw+gfwFKHrZQYwzd3fa8P6Wpw6ujuopIX0NHCVuz9tZj8i7Kpd4+5LzKy7u29u2yqlIcmlI5lk13s20NndTzSz7yYPecbdXzSzHsklQh2KWkodSM11UImBhBCaCODulwHbCRfdFiiQ4uXuaUL/H+5+OtDVzO529+8l8483s8KOGEigllKHUeeyg/7AaKAKKCd0cD/t7jcljxvYns9h6cjM7EzC5T2vATcDr7v7PcmyecBCd59mZv3cfV3bVZpdCqUOJLm49i7gXWAY4TDyLYRD/ne7+21tWJ40wcyGAd8EVhHOQSoBjgeecveHzOxU4NvAZzv6gQntvrVzNZcdJJeQ/BB4wt0vBs4gtJj2JVyK8Ic2K1KalOxOvw88BBwBHAusJBz2/6qZ3UW4YPpLHT2QQKHUrtU5D6kbYeTI94E/Qe0YSQsIF2W+mfzSS4TcfUdypvZ1wN+B4cAnCUfcrgE+AC7Plc9QZ3S3U3UO+5cQ/qMuI1zDdqSZnQUcTrh85KdtWKY0oeYzTCaPBOa6+3fNbDRhpIahwE3uPqPNimwD6lNqx5IztW8mtI6eIPQnnQT8gPALfV1y9bhEKhmgrRfQn/D5TXH3D8zsSuBg4KKO3KndEO2+tVPJ4f8vE4aq+Geyu3YOYWydAuAsBVK78AVCX9J7hFbtC2Y2HTiZcI5ZTgUSqKXUrplZP+B8oC/wgLu/ambdgX4d7SzfjqLOEDKdCBdHp83sYkI4nQ2MAnoDC9z9n21YaptRKLVzSTCdRWgx3e7ub7RxSbILZtaVcPjfgd8mHd3XE/qVzkouls5Z2n1r55Lm/QOEq8c1WmSkas62T87EriCc2HoQYYA2gPnA24Sz7nOaWkodRM0Z3W1dh3xUvTvH/D9gIfAz4IvAAYR77g0FzmyvQ9i2JIWSSCtILv+ZTRg65jxCMM0ExgHjCWdul7ZZgRFRKIlkWXK2/blAF3f/kZndRjg5cjkwXUdJd6Y+JZEWZmZ5Zvad5OehhNsgvZBMPwRMB75LOGra4S8b2V0KJZGW9x1gUvLzDwj30FtAGEVyIyGULgPO0YgNH6VQEml5DwD7mtmDhDvXViY3bthMuNmnARe7u7dhjdFSKIm0vMVANWGAvTRhXO1jgWJ3/yVhCFv1IzVCoSTSwpKLbD8PnAaMJBxdGwpMTTq9depGE3T0TSSLzOwzhLvJvALMcPcP27ik6CmURLLMzCYDS3NlPKS9pVASkaioT0lEoqJQEpGoKJREJCoKJRGJim4cIHvFzKYADwPvABnCHVwfcPdb92BdNxBuxvgG8AV3/7+NPO5E4GV33+X4UWb2ecLJimfXq/kCd5/WyHPOBsY2Z8D+3XmsNI9CSVrCszV/4Mkwr25m97n7hj1ZWTJ6ZlMjaF4KXIAGteuQFErS0roTzljeYWbPA2sJY04fRxjYbDSh2+Bqd3/ezE4Grk4eVwSU1m3JmNnXgW8A+cBjhPuiHQjca2afIoxR/hVCK+0hd7/FzPYn3BlkS/JV1lixyfjYJwGFhItlT0oWHWZmzxAGYJvp7r83syMJ92arBhYl25YWpj4laQmfMbPnzexZwsWol7h7ebLsQXf/LPA1YJ27H0EYcbHmfnQ/BD4LHANU1F2pmQ0AZgCTCbcb6gnMIbSivkoYZP804FPJ15fMzIBZwDXJduc2VnQyRG1fwq2wJxOC6ZBk8ZakruOAnySXh9wJnOTuRwIrCAP9SwtTS0lawrON9c8QBscH+Dgw2cwOTaYLkivnN9XcitrM6gfIfsBb7l6ZTF+ePK5m+ThgGPBMMt2bEFQHEC7rAPgrsH+DhYUhaquA2WZWDgwhBBPAi+6eAT4ws41AP8JdiB9Ott8FeJrQYpIWpJaSZFvNHWBLgdnuPoVwxfyvCbtVPZOhYuFfrZQai4CxST8VZvZIMs51mvC764TB9j+drPce4B/Jtg5rZJ21zGw88CV3Pw24JFlnqu7zzGwQUEy4hfZy4IvJtq4Dnmv+2yDNpVCS1nIHIWDmEHap3nf3KsINNP9oZn8m9CnVcve1hEHS5pjZ34DXkoH15wL3Em5V/gzwopnNI/RXrQAuBK5K+oQOpXELgS3Jc/8ErAJKkmVdkt3R/wXOT27KcCnw+6RFdyHhDjLSwnTtm4hERS0lEYmKQklEoqJQEpGoKJREJCoKJRGJikJJRKKiUBKRqCiURCQq/x/fjENgHMCw/wAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Classification Report:\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "          0       1.00      1.00      1.00     93838\n",
      "          1       0.85      0.84      0.84       149\n",
      "\n",
      "avg / total       1.00      1.00      1.00     93987\n",
      "\n",
      "\n",
      "=== LogisticRegression ===\n",
      "Model Accuracy:  91.2%\n",
      "\n",
      "Confusion Matrix:\n",
      " [[85582  8256]\n",
      " [    7   142]]\n",
      "\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Classification Report:\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "          0       1.00      0.91      0.95     93838\n",
      "          1       0.02      0.95      0.03       149\n",
      "\n",
      "avg / total       1.00      0.91      0.95     93987\n",
      "\n",
      "\n",
      "=== Naive Baiye Classifier ===\n",
      "Model Accuracy:  89.4%\n",
      "\n",
      "Confusion Matrix:\n",
      " [[83840  9998]\n",
      " [   10   139]]\n",
      "\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Classification Report:\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "          0       1.00      0.89      0.94     93838\n",
      "          1       0.01      0.93      0.03       149\n",
      "\n",
      "avg / total       1.00      0.89      0.94     93987\n",
      "\n",
      "\n",
      "============================= ROC Curve ===============================\n",
      "\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x432 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Test models\n",
    "classdict = {'normal':0, 'fraudulent':1}\n",
    "print()\n",
    "print('========================== Model Test Results ========================' \"\\n\")   \n",
    "\n",
    "for i, v in models:\n",
    "    accuracy = metrics.accuracy_score(y_test, v.predict(X_test))\n",
    "    confusion_matrix = metrics.confusion_matrix(y_test, v.predict(X_test))\n",
    "    classification = metrics.classification_report(y_test, v.predict(X_test))   \n",
    "    print('=== {} ==='.format(i))\n",
    "    print (\"Model Accuracy: \",  '{}%'.format(np.round(accuracy, 3) * 100))\n",
    "    print()\n",
    "    print(\"Confusion Matrix:\" \"\\n\", confusion_matrix)\n",
    "    print()\n",
    "    pf.plot_confusion_matrix(confusion_matrix, classes = list(classdict.keys()), title='Confusion Matrix Plot', cmap=plt.cm.summer)\n",
    "    print() \n",
    "    print(\"Classification Report:\" \"\\n\", classification) \n",
    "    print() \n",
    "\n",
    "print('============================= ROC Curve ===============================' \"\\n\")      \n",
    "pf.plot_roc_auc(arg1=models, arg2=X_test, arg3=y_test)"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python [conda env:datalytics]",
   "language": "python",
   "name": "conda-env-datalytics-py"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
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