Online Fraud Detection.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "bd9e2763",
   "metadata": {},
   "source": [
    "## Online Payment Fraud"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "933c4179",
   "metadata": {},
   "outputs": [],
   "source": [
    "# libraries to read data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "5b1460f0",
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "f8b14344",
   "metadata": {},
   "outputs": [],
   "source": [
    "# libraries for visualization\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "0b0dd2e8",
   "metadata": {},
   "outputs": [],
   "source": [
    "import seaborn as sns\n",
    "import matplotlib\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "import cufflinks as cf\n",
    "import plotly.offline as pyo\n",
    "import plotly.express as px\n",
    "import plotly.graph_objs as go\n",
    "import chart_studio.plotly as py"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "9ce3bf2a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "        <script type=\"text/javascript\">\n",
       "        window.PlotlyConfig = {MathJaxConfig: 'local'};\n",
       "        if (window.MathJax) {MathJax.Hub.Config({SVG: {font: \"STIX-Web\"}});}\n",
       "        if (typeof require !== 'undefined') {\n",
       "        require.undef(\"plotly\");\n",
       "        requirejs.config({\n",
       "            paths: {\n",
       "                'plotly': ['https://cdn.plot.ly/plotly-2.6.3.min']\n",
       "            }\n",
       "        });\n",
       "        require(['plotly'], function(Plotly) {\n",
       "            window._Plotly = Plotly;\n",
       "        });\n",
       "        }\n",
       "        </script>\n",
       "        "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#to view on note\n",
    "%matplotlib inline\n",
    "cf.go_offline()\n",
    "#plotly.offline.iplot()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "7bf7cbab",
   "metadata": {},
   "outputs": [],
   "source": [
    "#data\n",
    "\n",
    "df = pd.read_csv('PS_20174392719_1491204439457_log.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "d76fd3b1",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "    .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>step</th>\n",
       "      <th>type</th>\n",
       "      <th>amount</th>\n",
       "      <th>nameOrig</th>\n",
       "      <th>oldbalanceOrg</th>\n",
       "      <th>newbalanceOrig</th>\n",
       "      <th>nameDest</th>\n",
       "      <th>oldbalanceDest</th>\n",
       "      <th>newbalanceDest</th>\n",
       "      <th>isFraud</th>\n",
       "      <th>isFlaggedFraud</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>PAYMENT</td>\n",
       "      <td>9839.64</td>\n",
       "      <td>C1231006815</td>\n",
       "      <td>170136.00</td>\n",
       "      <td>160296.36</td>\n",
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       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>PAYMENT</td>\n",
       "      <td>1864.28</td>\n",
       "      <td>C1666544295</td>\n",
       "      <td>21249.00</td>\n",
       "      <td>19384.72</td>\n",
       "      <td>M2044282225</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1</td>\n",
       "      <td>TRANSFER</td>\n",
       "      <td>181.00</td>\n",
       "      <td>C1305486145</td>\n",
       "      <td>181.00</td>\n",
       "      <td>0.00</td>\n",
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       "      <td>0.00</td>\n",
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       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1</td>\n",
       "      <td>CASH_OUT</td>\n",
       "      <td>181.00</td>\n",
       "      <td>C840083671</td>\n",
       "      <td>181.00</td>\n",
       "      <td>0.00</td>\n",
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       "      <td>21182.0</td>\n",
       "      <td>0.00</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1</td>\n",
       "      <td>PAYMENT</td>\n",
       "      <td>11668.14</td>\n",
       "      <td>C2048537720</td>\n",
       "      <td>41554.00</td>\n",
       "      <td>29885.86</td>\n",
       "      <td>M1230701703</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>1</td>\n",
       "      <td>PAYMENT</td>\n",
       "      <td>7817.71</td>\n",
       "      <td>C90045638</td>\n",
       "      <td>53860.00</td>\n",
       "      <td>46042.29</td>\n",
       "      <td>M573487274</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>1</td>\n",
       "      <td>PAYMENT</td>\n",
       "      <td>7107.77</td>\n",
       "      <td>C154988899</td>\n",
       "      <td>183195.00</td>\n",
       "      <td>176087.23</td>\n",
       "      <td>M408069119</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>1</td>\n",
       "      <td>PAYMENT</td>\n",
       "      <td>7861.64</td>\n",
       "      <td>C1912850431</td>\n",
       "      <td>176087.23</td>\n",
       "      <td>168225.59</td>\n",
       "      <td>M633326333</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>1</td>\n",
       "      <td>PAYMENT</td>\n",
       "      <td>4024.36</td>\n",
       "      <td>C1265012928</td>\n",
       "      <td>2671.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>M1176932104</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>1</td>\n",
       "      <td>DEBIT</td>\n",
       "      <td>5337.77</td>\n",
       "      <td>C712410124</td>\n",
       "      <td>41720.00</td>\n",
       "      <td>36382.23</td>\n",
       "      <td>C195600860</td>\n",
       "      <td>41898.0</td>\n",
       "      <td>40348.79</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   step      type    amount     nameOrig  oldbalanceOrg  newbalanceOrig  \\\n",
       "0     1   PAYMENT   9839.64  C1231006815      170136.00       160296.36   \n",
       "1     1   PAYMENT   1864.28  C1666544295       21249.00        19384.72   \n",
       "2     1  TRANSFER    181.00  C1305486145         181.00            0.00   \n",
       "3     1  CASH_OUT    181.00   C840083671         181.00            0.00   \n",
       "4     1   PAYMENT  11668.14  C2048537720       41554.00        29885.86   \n",
       "5     1   PAYMENT   7817.71    C90045638       53860.00        46042.29   \n",
       "6     1   PAYMENT   7107.77   C154988899      183195.00       176087.23   \n",
       "7     1   PAYMENT   7861.64  C1912850431      176087.23       168225.59   \n",
       "8     1   PAYMENT   4024.36  C1265012928        2671.00            0.00   \n",
       "9     1     DEBIT   5337.77   C712410124       41720.00        36382.23   \n",
       "\n",
       "      nameDest  oldbalanceDest  newbalanceDest  isFraud  isFlaggedFraud  \n",
       "0  M1979787155             0.0            0.00        0               0  \n",
       "1  M2044282225             0.0            0.00        0               0  \n",
       "2   C553264065             0.0            0.00        1               0  \n",
       "3    C38997010         21182.0            0.00        1               0  \n",
       "4  M1230701703             0.0            0.00        0               0  \n",
       "5   M573487274             0.0            0.00        0               0  \n",
       "6   M408069119             0.0            0.00        0               0  \n",
       "7   M633326333             0.0            0.00        0               0  \n",
       "8  M1176932104             0.0            0.00        0               0  \n",
       "9   C195600860         41898.0        40348.79        0               0  "
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "15026d94",
   "metadata": {},
   "outputs": [
    {
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       "      <th>6362615</th>\n",
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      "text/plain": [
       "          step   type  amount  nameOrig  oldbalanceOrg  newbalanceOrig  \\\n",
       "0        False  False   False     False          False           False   \n",
       "1        False  False   False     False          False           False   \n",
       "2        False  False   False     False          False           False   \n",
       "3        False  False   False     False          False           False   \n",
       "4        False  False   False     False          False           False   \n",
       "...        ...    ...     ...       ...            ...             ...   \n",
       "6362615  False  False   False     False          False           False   \n",
       "6362616  False  False   False     False          False           False   \n",
       "6362617  False  False   False     False          False           False   \n",
       "6362618  False  False   False     False          False           False   \n",
       "6362619  False  False   False     False          False           False   \n",
       "\n",
       "         nameDest  oldbalanceDest  newbalanceDest  isFraud  isFlaggedFraud  \n",
       "0           False           False           False    False           False  \n",
       "1           False           False           False    False           False  \n",
       "2           False           False           False    False           False  \n",
       "3           False           False           False    False           False  \n",
       "4           False           False           False    False           False  \n",
       "...           ...             ...             ...      ...             ...  \n",
       "6362615     False           False           False    False           False  \n",
       "6362616     False           False           False    False           False  \n",
       "6362617     False           False           False    False           False  \n",
       "6362618     False           False           False    False           False  \n",
       "6362619     False           False           False    False           False  \n",
       "\n",
       "[6362620 rows x 11 columns]"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.isnull()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "703d6d77",
   "metadata": {},
   "outputs": [],
   "source": [
    "sns.heatmap(df.isnull(),yticklabels=False,cbar=False,cmap='YlOrBr_r')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "6f3231a1",
   "metadata": {},
   "outputs": [],
   "source": [
    "df.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "466bb699",
   "metadata": {},
   "outputs": [],
   "source": [
    "df.info"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "23a7a11b",
   "metadata": {},
   "outputs": [],
   "source": [
    "df.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "8e48aacf",
   "metadata": {},
   "outputs": [],
   "source": [
    "df.size"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "da52e5c9",
   "metadata": {},
   "outputs": [],
   "source": [
    "df.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "9389a6d3",
   "metadata": {},
   "outputs": [],
   "source": [
    "sns.set_style('whitegrid')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "2dda6ff7",
   "metadata": {},
   "outputs": [],
   "source": [
    "#sns.countplot(x='nameDest',hue='isFraud',data=df)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "898c5af3",
   "metadata": {},
   "outputs": [],
   "source": [
    "df['isFraud'].iplot.hist()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "08484d65",
   "metadata": {},
   "outputs": [],
   "source": [
    "type = df[\"type\"].value_counts()\n",
    "transactions = type.index\n",
    "quantity = type.values\n",
    "\n",
    "\n",
    "figure = px.pie(df, \n",
    "             values=quantity, \n",
    "             names=transactions,hole = 0.5, \n",
    "             title=\"Distribution of Transaction Type\")\n",
    "figure.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "7610435d",
   "metadata": {},
   "outputs": [],
   "source": [
    "df[\"type\"] = df[\"type\"].map({\"CASH_OUT\": 1, \"PAYMENT\": 2, \n",
    "                                 \"CASH_IN\": 3, \"TRANSFER\": 4,\n",
    "                                 \"DEBIT\": 5})\n",
    "df[\"isFraud\"] = df[\"isFraud\"].map({0: \"No Fraud\", 1: \"Fraud\"})\n",
    "print(df.head())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "2b876f40",
   "metadata": {},
   "outputs": [],
   "source": [
    "# splitting the data\n",
    "from sklearn.model_selection import train_test_split\n",
    "x = np.array(df[[\"type\", \"amount\", \"oldbalanceOrg\", \"newbalanceOrig\"]])\n",
    "y = np.array(df[[\"isFraud\"]])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "fb9d0dde",
   "metadata": {},
   "outputs": [],
   "source": [
    "# training a machine learning model\n",
    "from sklearn.tree import DecisionTreeClassifier\n",
    "xtrain, xtest, ytrain, ytest = train_test_split(x, y, test_size=0.10, random_state=42)\n",
    "model = DecisionTreeClassifier()\n",
    "model.fit(xtrain, ytrain)\n",
    "print(model.score(xtest, ytest))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "2a192fbc",
   "metadata": {},
   "outputs": [],
   "source": [
    "# prediction\n",
    "#features = [type, amount, oldbalanceOrg, newbalanceOrig]\n",
    "features = np.array([[4, 9000.60, 9000.60, 0.0]])\n",
    "print(model.predict(features))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "a772e530",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.model_selection import train_test_split"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "e10c6b03",
   "metadata": {},
   "outputs": [],
   "source": [
    "X_train, X_test, y_train, y_test = train_test_split(x,y, test_size=0.33,random_state=101)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "f05d53a2",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.linear_model import LogisticRegression"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "093a3bfe",
   "metadata": {},
   "outputs": [],
   "source": [
    "logmodel = LogisticRegression()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "60186f66",
   "metadata": {},
   "outputs": [],
   "source": [
    "logmodel.fit(X_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "e116850c",
   "metadata": {},
   "outputs": [],
   "source": [
    "predictions =logmodel.predict(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "57bb4660",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.metrics import classification_report"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "bf8cbb46",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.metrics import confusion_matrix"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "bbf5e4ed",
   "metadata": {},
   "outputs": [],
   "source": [
    "confusion_matrix(y_test,predictions)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "d9f2e431",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "5854c1df",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "a115c625",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "fface8ed",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "36cc4e32",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "fe70f506",
   "metadata": {},
   "outputs": [],
   "source": []
  }
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