SIT719_Prac05_Task01_SupervisedML_fraudCreditCardData_detection_various_algorithms.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "#import libraries\n",
    "\n",
    "import pandas as pd\n",
    "import os\n",
    "from sklearn.metrics import confusion_matrix\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "link_to_data = 'https://raw.githubusercontent.com/SIT719/2020-S2/master/data/payment_fraud.csv'\n",
    "dfp = pd.read_csv(link_to_data, error_bad_lines=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "df = pd.get_dummies(dfp, columns=['paymentMethod'])\n",
    "\n",
    "from sklearn.model_selection import train_test_split\n",
    "\n",
    "X_train, X_test, y_train, y_test = train_test_split(\n",
    "    df.drop('label', axis=1), df['label'],\n",
    "    test_size=0.33, random_state=17)\n",
    "\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Logistic Regression"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.999922738159623\n",
      "[[12753     0]\n",
      " [    1   189]]\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "c:\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\logistic.py:433: FutureWarning: Default solver will be changed to 'lbfgs' in 0.22. Specify a solver to silence this warning.\n",
      "  FutureWarning)\n"
     ]
    }
   ],
   "source": [
    "from sklearn.linear_model import LogisticRegression\n",
    "clf = LogisticRegression()\n",
    "clf.fit(X_train, y_train)\n",
    "\n",
    "\n",
    "y_pred = clf.predict(X_test)\n",
    "\n",
    "from sklearn.metrics import accuracy_score\n",
    "\n",
    "print(accuracy_score(y_pred, y_test))\n",
    "print(confusion_matrix(y_test, y_pred))\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Naive Bayes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.999922738159623\n",
      "[[12753     0]\n",
      " [    1   189]]\n"
     ]
    }
   ],
   "source": [
    "from sklearn.naive_bayes import GaussianNB\n",
    "gnb = GaussianNB()\n",
    "y_pred = gnb.fit(X_train, y_train).predict(X_test)\n",
    "print(accuracy_score(y_pred, y_test))\n",
    "print(confusion_matrix(y_test, y_pred))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Decision Tree"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1.0\n",
      "[[12753     0]\n",
      " [    0   190]]\n"
     ]
    }
   ],
   "source": [
    "from sklearn.tree import DecisionTreeClassifier # Import Decision Tree Classifier\n",
    "from sklearn import metrics #Import scikit-learn metrics module for accuracy calculation\n",
    "\n",
    "# Create Decision Tree classifer object\n",
    "clf = DecisionTreeClassifier()\n",
    "\n",
    "# Train Decision Tree Classifer\n",
    "clf = clf.fit(X_train,y_train)\n",
    "\n",
    "#Predict the response for test dataset\n",
    "y_pred = clf.predict(X_test)\n",
    "\n",
    "print(accuracy_score(y_pred, y_test))\n",
    "print(confusion_matrix(y_test, y_pred))\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Random Forest"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1.0\n",
      "[[12753     0]\n",
      " [    0   190]]\n"
     ]
    }
   ],
   "source": [
    "from sklearn.ensemble import RandomForestRegressor\n",
    "\n",
    "regressor = RandomForestRegressor(n_estimators=20, random_state=0)\n",
    "regressor.fit(X_train, y_train)\n",
    "y_pred = regressor.predict(X_test)\n",
    "print(accuracy_score(y_pred, y_test))\n",
    "print(confusion_matrix(y_test, y_pred))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# SVM-SVC"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1.0\n",
      "[[12753     0]\n",
      " [    0   190]]\n"
     ]
    }
   ],
   "source": [
    "from sklearn.svm import SVC \n",
    "clf = SVC(kernel='linear') \n",
    "clf.fit(X_train, y_train)\n",
    "\n",
    "y_pred = clf.predict(X_test)\n",
    "\n",
    "print(accuracy_score(y_pred, y_test))\n",
    "print(confusion_matrix(y_test, y_pred))\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# MLP"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.999922738159623\n",
      "[[12753     0]\n",
      " [    1   189]]\n"
     ]
    }
   ],
   "source": [
    "from sklearn.neural_network import MLPClassifier\n",
    "clf = MLPClassifier(solver='lbfgs', alpha=1e-5,\n",
    "                    hidden_layer_sizes=(5, 2), random_state=1)\n",
    "clf.fit(X_train, y_train)\n",
    "\n",
    "y_pred = clf.predict(X_test)\n",
    "\n",
    "print(accuracy_score(y_pred, y_test))\n",
    "print(confusion_matrix(y_test, y_pred))\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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