Credit_Card_Fraud_Detection.ipynb

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  "metadata": {
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      "display_name": "Python 3",
      "language": "python",
      "name": "python3"
    },
    "language_info": {
      "codemirror_mode": {
        "name": "ipython",
        "version": 3
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      "file_extension": ".py",
      "mimetype": "text/x-python",
      "name": "python",
      "nbconvert_exporter": "python",
      "pygments_lexer": "ipython3",
      "version": "3.7.3"
    },
    "colab": {
      "name": "Credit_Card_Fraud_Detection.ipynb",
      "version": "0.3.2",
      "provenance": []
    }
  },
  "cells": [
    {
      "cell_type": "code",
      "metadata": {
        "id": "_-4Iy2ys7xGc",
        "colab_type": "code",
        "colab": {},
        "outputId": "5678413e-adfa-496f-c13f-12f85713b20c"
      },
      "source": [
        "#Importing the basic libraries\n",
        "import numpy as np\n",
        "import pandas as pd\n",
        "import matplotlib.pyplot as plt\n",
        "import plotly.offline as py\n",
        "from plotly import tools\n",
        "py.init_notebook_mode(connected=True)\n",
        "import plotly.graph_objs as go\n",
        "from sklearn.model_selection import train_test_split\n",
        "import warnings\n",
        "warnings.filterwarnings('ignore')"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "display_data",
          "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-latest.min']\n",
              "            }\n",
              "        });\n",
              "        require(['plotly'], function(Plotly) {\n",
              "            window._Plotly = Plotly;\n",
              "        });\n",
              "        }\n",
              "        </script>\n",
              "        "
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "5E6UvrB87xGk",
        "colab_type": "code",
        "colab": {},
        "outputId": "85b84a2c-002e-4a3e-fdfe-3e695a2fae6e"
      },
      "source": [
        "#Reading the dataset \n",
        "df = pd.read_csv('creditcard.csv')\n",
        "df.shape"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(284807, 31)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 2
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "E9QdW2tf7xGm",
        "colab_type": "code",
        "colab": {},
        "outputId": "0d6a4756-1645-4add-fb12-020a830ccf3d"
      },
      "source": [
        "#Let's check if there are any null values \n",
        "df.isnull().sum()"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Time      0\n",
              "V1        0\n",
              "V2        0\n",
              "V3        0\n",
              "V4        0\n",
              "V5        0\n",
              "V6        0\n",
              "V7        0\n",
              "V8        0\n",
              "V9        0\n",
              "V10       0\n",
              "V11       0\n",
              "V12       0\n",
              "V13       0\n",
              "V14       0\n",
              "V15       0\n",
              "V16       0\n",
              "V17       0\n",
              "V18       0\n",
              "V19       0\n",
              "V20       0\n",
              "V21       0\n",
              "V22       0\n",
              "V23       0\n",
              "V24       0\n",
              "V25       0\n",
              "V26       0\n",
              "V27       0\n",
              "V28       0\n",
              "Amount    0\n",
              "Class     0\n",
              "dtype: int64"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 3
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "nY9wWSmN7xGp",
        "colab_type": "code",
        "colab": {},
        "outputId": "45858ba6-741d-47aa-9d0b-57e269ee3336"
      },
      "source": [
        "#Describe the dataset to get rough idea about the data \n",
        "df.describe()"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
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              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Time</th>\n",
              "      <th>V1</th>\n",
              "      <th>V2</th>\n",
              "      <th>V3</th>\n",
              "      <th>V4</th>\n",
              "      <th>V5</th>\n",
              "      <th>V6</th>\n",
              "      <th>V7</th>\n",
              "      <th>V8</th>\n",
              "      <th>V9</th>\n",
              "      <th>...</th>\n",
              "      <th>V21</th>\n",
              "      <th>V22</th>\n",
              "      <th>V23</th>\n",
              "      <th>V24</th>\n",
              "      <th>V25</th>\n",
              "      <th>V26</th>\n",
              "      <th>V27</th>\n",
              "      <th>V28</th>\n",
              "      <th>Amount</th>\n",
              "      <th>Class</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>count</th>\n",
              "      <td>284807.000000</td>\n",
              "      <td>2.848070e+05</td>\n",
              "      <td>2.848070e+05</td>\n",
              "      <td>2.848070e+05</td>\n",
              "      <td>2.848070e+05</td>\n",
              "      <td>2.848070e+05</td>\n",
              "      <td>2.848070e+05</td>\n",
              "      <td>2.848070e+05</td>\n",
              "      <td>2.848070e+05</td>\n",
              "      <td>2.848070e+05</td>\n",
              "      <td>...</td>\n",
              "      <td>2.848070e+05</td>\n",
              "      <td>2.848070e+05</td>\n",
              "      <td>2.848070e+05</td>\n",
              "      <td>2.848070e+05</td>\n",
              "      <td>2.848070e+05</td>\n",
              "      <td>2.848070e+05</td>\n",
              "      <td>2.848070e+05</td>\n",
              "      <td>2.848070e+05</td>\n",
              "      <td>284807.000000</td>\n",
              "      <td>284807.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>mean</th>\n",
              "      <td>94813.859575</td>\n",
              "      <td>3.919560e-15</td>\n",
              "      <td>5.688174e-16</td>\n",
              "      <td>-8.769071e-15</td>\n",
              "      <td>2.782312e-15</td>\n",
              "      <td>-1.552563e-15</td>\n",
              "      <td>2.010663e-15</td>\n",
              "      <td>-1.694249e-15</td>\n",
              "      <td>-1.927028e-16</td>\n",
              "      <td>-3.137024e-15</td>\n",
              "      <td>...</td>\n",
              "      <td>1.537294e-16</td>\n",
              "      <td>7.959909e-16</td>\n",
              "      <td>5.367590e-16</td>\n",
              "      <td>4.458112e-15</td>\n",
              "      <td>1.453003e-15</td>\n",
              "      <td>1.699104e-15</td>\n",
              "      <td>-3.660161e-16</td>\n",
              "      <td>-1.206049e-16</td>\n",
              "      <td>88.349619</td>\n",
              "      <td>0.001727</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>std</th>\n",
              "      <td>47488.145955</td>\n",
              "      <td>1.958696e+00</td>\n",
              "      <td>1.651309e+00</td>\n",
              "      <td>1.516255e+00</td>\n",
              "      <td>1.415869e+00</td>\n",
              "      <td>1.380247e+00</td>\n",
              "      <td>1.332271e+00</td>\n",
              "      <td>1.237094e+00</td>\n",
              "      <td>1.194353e+00</td>\n",
              "      <td>1.098632e+00</td>\n",
              "      <td>...</td>\n",
              "      <td>7.345240e-01</td>\n",
              "      <td>7.257016e-01</td>\n",
              "      <td>6.244603e-01</td>\n",
              "      <td>6.056471e-01</td>\n",
              "      <td>5.212781e-01</td>\n",
              "      <td>4.822270e-01</td>\n",
              "      <td>4.036325e-01</td>\n",
              "      <td>3.300833e-01</td>\n",
              "      <td>250.120109</td>\n",
              "      <td>0.041527</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>min</th>\n",
              "      <td>0.000000</td>\n",
              "      <td>-5.640751e+01</td>\n",
              "      <td>-7.271573e+01</td>\n",
              "      <td>-4.832559e+01</td>\n",
              "      <td>-5.683171e+00</td>\n",
              "      <td>-1.137433e+02</td>\n",
              "      <td>-2.616051e+01</td>\n",
              "      <td>-4.355724e+01</td>\n",
              "      <td>-7.321672e+01</td>\n",
              "      <td>-1.343407e+01</td>\n",
              "      <td>...</td>\n",
              "      <td>-3.483038e+01</td>\n",
              "      <td>-1.093314e+01</td>\n",
              "      <td>-4.480774e+01</td>\n",
              "      <td>-2.836627e+00</td>\n",
              "      <td>-1.029540e+01</td>\n",
              "      <td>-2.604551e+00</td>\n",
              "      <td>-2.256568e+01</td>\n",
              "      <td>-1.543008e+01</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>25%</th>\n",
              "      <td>54201.500000</td>\n",
              "      <td>-9.203734e-01</td>\n",
              "      <td>-5.985499e-01</td>\n",
              "      <td>-8.903648e-01</td>\n",
              "      <td>-8.486401e-01</td>\n",
              "      <td>-6.915971e-01</td>\n",
              "      <td>-7.682956e-01</td>\n",
              "      <td>-5.540759e-01</td>\n",
              "      <td>-2.086297e-01</td>\n",
              "      <td>-6.430976e-01</td>\n",
              "      <td>...</td>\n",
              "      <td>-2.283949e-01</td>\n",
              "      <td>-5.423504e-01</td>\n",
              "      <td>-1.618463e-01</td>\n",
              "      <td>-3.545861e-01</td>\n",
              "      <td>-3.171451e-01</td>\n",
              "      <td>-3.269839e-01</td>\n",
              "      <td>-7.083953e-02</td>\n",
              "      <td>-5.295979e-02</td>\n",
              "      <td>5.600000</td>\n",
              "      <td>0.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>50%</th>\n",
              "      <td>84692.000000</td>\n",
              "      <td>1.810880e-02</td>\n",
              "      <td>6.548556e-02</td>\n",
              "      <td>1.798463e-01</td>\n",
              "      <td>-1.984653e-02</td>\n",
              "      <td>-5.433583e-02</td>\n",
              "      <td>-2.741871e-01</td>\n",
              "      <td>4.010308e-02</td>\n",
              "      <td>2.235804e-02</td>\n",
              "      <td>-5.142873e-02</td>\n",
              "      <td>...</td>\n",
              "      <td>-2.945017e-02</td>\n",
              "      <td>6.781943e-03</td>\n",
              "      <td>-1.119293e-02</td>\n",
              "      <td>4.097606e-02</td>\n",
              "      <td>1.659350e-02</td>\n",
              "      <td>-5.213911e-02</td>\n",
              "      <td>1.342146e-03</td>\n",
              "      <td>1.124383e-02</td>\n",
              "      <td>22.000000</td>\n",
              "      <td>0.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>75%</th>\n",
              "      <td>139320.500000</td>\n",
              "      <td>1.315642e+00</td>\n",
              "      <td>8.037239e-01</td>\n",
              "      <td>1.027196e+00</td>\n",
              "      <td>7.433413e-01</td>\n",
              "      <td>6.119264e-01</td>\n",
              "      <td>3.985649e-01</td>\n",
              "      <td>5.704361e-01</td>\n",
              "      <td>3.273459e-01</td>\n",
              "      <td>5.971390e-01</td>\n",
              "      <td>...</td>\n",
              "      <td>1.863772e-01</td>\n",
              "      <td>5.285536e-01</td>\n",
              "      <td>1.476421e-01</td>\n",
              "      <td>4.395266e-01</td>\n",
              "      <td>3.507156e-01</td>\n",
              "      <td>2.409522e-01</td>\n",
              "      <td>9.104512e-02</td>\n",
              "      <td>7.827995e-02</td>\n",
              "      <td>77.165000</td>\n",
              "      <td>0.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>max</th>\n",
              "      <td>172792.000000</td>\n",
              "      <td>2.454930e+00</td>\n",
              "      <td>2.205773e+01</td>\n",
              "      <td>9.382558e+00</td>\n",
              "      <td>1.687534e+01</td>\n",
              "      <td>3.480167e+01</td>\n",
              "      <td>7.330163e+01</td>\n",
              "      <td>1.205895e+02</td>\n",
              "      <td>2.000721e+01</td>\n",
              "      <td>1.559499e+01</td>\n",
              "      <td>...</td>\n",
              "      <td>2.720284e+01</td>\n",
              "      <td>1.050309e+01</td>\n",
              "      <td>2.252841e+01</td>\n",
              "      <td>4.584549e+00</td>\n",
              "      <td>7.519589e+00</td>\n",
              "      <td>3.517346e+00</td>\n",
              "      <td>3.161220e+01</td>\n",
              "      <td>3.384781e+01</td>\n",
              "      <td>25691.160000</td>\n",
              "      <td>1.000000</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>8 rows × 31 columns</p>\n",
              "</div>"
            ],
            "text/plain": [
              "                Time            V1            V2            V3            V4  \\\n",
              "count  284807.000000  2.848070e+05  2.848070e+05  2.848070e+05  2.848070e+05   \n",
              "mean    94813.859575  3.919560e-15  5.688174e-16 -8.769071e-15  2.782312e-15   \n",
              "std     47488.145955  1.958696e+00  1.651309e+00  1.516255e+00  1.415869e+00   \n",
              "min         0.000000 -5.640751e+01 -7.271573e+01 -4.832559e+01 -5.683171e+00   \n",
              "25%     54201.500000 -9.203734e-01 -5.985499e-01 -8.903648e-01 -8.486401e-01   \n",
              "50%     84692.000000  1.810880e-02  6.548556e-02  1.798463e-01 -1.984653e-02   \n",
              "75%    139320.500000  1.315642e+00  8.037239e-01  1.027196e+00  7.433413e-01   \n",
              "max    172792.000000  2.454930e+00  2.205773e+01  9.382558e+00  1.687534e+01   \n",
              "\n",
              "                 V5            V6            V7            V8            V9  \\\n",
              "count  2.848070e+05  2.848070e+05  2.848070e+05  2.848070e+05  2.848070e+05   \n",
              "mean  -1.552563e-15  2.010663e-15 -1.694249e-15 -1.927028e-16 -3.137024e-15   \n",
              "std    1.380247e+00  1.332271e+00  1.237094e+00  1.194353e+00  1.098632e+00   \n",
              "min   -1.137433e+02 -2.616051e+01 -4.355724e+01 -7.321672e+01 -1.343407e+01   \n",
              "25%   -6.915971e-01 -7.682956e-01 -5.540759e-01 -2.086297e-01 -6.430976e-01   \n",
              "50%   -5.433583e-02 -2.741871e-01  4.010308e-02  2.235804e-02 -5.142873e-02   \n",
              "75%    6.119264e-01  3.985649e-01  5.704361e-01  3.273459e-01  5.971390e-01   \n",
              "max    3.480167e+01  7.330163e+01  1.205895e+02  2.000721e+01  1.559499e+01   \n",
              "\n",
              "       ...           V21           V22           V23           V24  \\\n",
              "count  ...  2.848070e+05  2.848070e+05  2.848070e+05  2.848070e+05   \n",
              "mean   ...  1.537294e-16  7.959909e-16  5.367590e-16  4.458112e-15   \n",
              "std    ...  7.345240e-01  7.257016e-01  6.244603e-01  6.056471e-01   \n",
              "min    ... -3.483038e+01 -1.093314e+01 -4.480774e+01 -2.836627e+00   \n",
              "25%    ... -2.283949e-01 -5.423504e-01 -1.618463e-01 -3.545861e-01   \n",
              "50%    ... -2.945017e-02  6.781943e-03 -1.119293e-02  4.097606e-02   \n",
              "75%    ...  1.863772e-01  5.285536e-01  1.476421e-01  4.395266e-01   \n",
              "max    ...  2.720284e+01  1.050309e+01  2.252841e+01  4.584549e+00   \n",
              "\n",
              "                V25           V26           V27           V28         Amount  \\\n",
              "count  2.848070e+05  2.848070e+05  2.848070e+05  2.848070e+05  284807.000000   \n",
              "mean   1.453003e-15  1.699104e-15 -3.660161e-16 -1.206049e-16      88.349619   \n",
              "std    5.212781e-01  4.822270e-01  4.036325e-01  3.300833e-01     250.120109   \n",
              "min   -1.029540e+01 -2.604551e+00 -2.256568e+01 -1.543008e+01       0.000000   \n",
              "25%   -3.171451e-01 -3.269839e-01 -7.083953e-02 -5.295979e-02       5.600000   \n",
              "50%    1.659350e-02 -5.213911e-02  1.342146e-03  1.124383e-02      22.000000   \n",
              "75%    3.507156e-01  2.409522e-01  9.104512e-02  7.827995e-02      77.165000   \n",
              "max    7.519589e+00  3.517346e+00  3.161220e+01  3.384781e+01   25691.160000   \n",
              "\n",
              "               Class  \n",
              "count  284807.000000  \n",
              "mean        0.001727  \n",
              "std         0.041527  \n",
              "min         0.000000  \n",
              "25%         0.000000  \n",
              "50%         0.000000  \n",
              "75%         0.000000  \n",
              "max         1.000000  \n",
              "\n",
              "[8 rows x 31 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 4
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Rp6ZzVtK7xGr",
        "colab_type": "code",
        "colab": {},
        "outputId": "6c160c0c-6760-43fc-8d0f-fd3654ded339"
      },
      "source": [
        "#Well let's see all the columns in the dataset \n",
        "df.columns"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Index(['Time', 'V1', 'V2', 'V3', 'V4', 'V5', 'V6', 'V7', 'V8', 'V9', 'V10',\n",
              "       'V11', 'V12', 'V13', 'V14', 'V15', 'V16', 'V17', 'V18', 'V19', 'V20',\n",
              "       'V21', 'V22', 'V23', 'V24', 'V25', 'V26', 'V27', 'V28', 'Amount',\n",
              "       'Class'],\n",
              "      dtype='object')"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 5
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "X4obuP2B7xGt",
        "colab_type": "code",
        "colab": {},
        "outputId": "e614e02f-d661-43e0-efc3-e6170d209c6a"
      },
      "source": [
        "#A brief look at the initial rows of the dataset \n",
        "df.head()"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
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              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Time</th>\n",
              "      <th>V1</th>\n",
              "      <th>V2</th>\n",
              "      <th>V3</th>\n",
              "      <th>V4</th>\n",
              "      <th>V5</th>\n",
              "      <th>V6</th>\n",
              "      <th>V7</th>\n",
              "      <th>V8</th>\n",
              "      <th>V9</th>\n",
              "      <th>...</th>\n",
              "      <th>V21</th>\n",
              "      <th>V22</th>\n",
              "      <th>V23</th>\n",
              "      <th>V24</th>\n",
              "      <th>V25</th>\n",
              "      <th>V26</th>\n",
              "      <th>V27</th>\n",
              "      <th>V28</th>\n",
              "      <th>Amount</th>\n",
              "      <th>Class</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>0.0</td>\n",
              "      <td>-1.359807</td>\n",
              "      <td>-0.072781</td>\n",
              "      <td>2.536347</td>\n",
              "      <td>1.378155</td>\n",
              "      <td>-0.338321</td>\n",
              "      <td>0.462388</td>\n",
              "      <td>0.239599</td>\n",
              "      <td>0.098698</td>\n",
              "      <td>0.363787</td>\n",
              "      <td>...</td>\n",
              "      <td>-0.018307</td>\n",
              "      <td>0.277838</td>\n",
              "      <td>-0.110474</td>\n",
              "      <td>0.066928</td>\n",
              "      <td>0.128539</td>\n",
              "      <td>-0.189115</td>\n",
              "      <td>0.133558</td>\n",
              "      <td>-0.021053</td>\n",
              "      <td>149.62</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>0.0</td>\n",
              "      <td>1.191857</td>\n",
              "      <td>0.266151</td>\n",
              "      <td>0.166480</td>\n",
              "      <td>0.448154</td>\n",
              "      <td>0.060018</td>\n",
              "      <td>-0.082361</td>\n",
              "      <td>-0.078803</td>\n",
              "      <td>0.085102</td>\n",
              "      <td>-0.255425</td>\n",
              "      <td>...</td>\n",
              "      <td>-0.225775</td>\n",
              "      <td>-0.638672</td>\n",
              "      <td>0.101288</td>\n",
              "      <td>-0.339846</td>\n",
              "      <td>0.167170</td>\n",
              "      <td>0.125895</td>\n",
              "      <td>-0.008983</td>\n",
              "      <td>0.014724</td>\n",
              "      <td>2.69</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>1.0</td>\n",
              "      <td>-1.358354</td>\n",
              "      <td>-1.340163</td>\n",
              "      <td>1.773209</td>\n",
              "      <td>0.379780</td>\n",
              "      <td>-0.503198</td>\n",
              "      <td>1.800499</td>\n",
              "      <td>0.791461</td>\n",
              "      <td>0.247676</td>\n",
              "      <td>-1.514654</td>\n",
              "      <td>...</td>\n",
              "      <td>0.247998</td>\n",
              "      <td>0.771679</td>\n",
              "      <td>0.909412</td>\n",
              "      <td>-0.689281</td>\n",
              "      <td>-0.327642</td>\n",
              "      <td>-0.139097</td>\n",
              "      <td>-0.055353</td>\n",
              "      <td>-0.059752</td>\n",
              "      <td>378.66</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>1.0</td>\n",
              "      <td>-0.966272</td>\n",
              "      <td>-0.185226</td>\n",
              "      <td>1.792993</td>\n",
              "      <td>-0.863291</td>\n",
              "      <td>-0.010309</td>\n",
              "      <td>1.247203</td>\n",
              "      <td>0.237609</td>\n",
              "      <td>0.377436</td>\n",
              "      <td>-1.387024</td>\n",
              "      <td>...</td>\n",
              "      <td>-0.108300</td>\n",
              "      <td>0.005274</td>\n",
              "      <td>-0.190321</td>\n",
              "      <td>-1.175575</td>\n",
              "      <td>0.647376</td>\n",
              "      <td>-0.221929</td>\n",
              "      <td>0.062723</td>\n",
              "      <td>0.061458</td>\n",
              "      <td>123.50</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>2.0</td>\n",
              "      <td>-1.158233</td>\n",
              "      <td>0.877737</td>\n",
              "      <td>1.548718</td>\n",
              "      <td>0.403034</td>\n",
              "      <td>-0.407193</td>\n",
              "      <td>0.095921</td>\n",
              "      <td>0.592941</td>\n",
              "      <td>-0.270533</td>\n",
              "      <td>0.817739</td>\n",
              "      <td>...</td>\n",
              "      <td>-0.009431</td>\n",
              "      <td>0.798278</td>\n",
              "      <td>-0.137458</td>\n",
              "      <td>0.141267</td>\n",
              "      <td>-0.206010</td>\n",
              "      <td>0.502292</td>\n",
              "      <td>0.219422</td>\n",
              "      <td>0.215153</td>\n",
              "      <td>69.99</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>5 rows × 31 columns</p>\n",
              "</div>"
            ],
            "text/plain": [
              "   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       V25  \\\n",
              "0  0.098698  0.363787  ... -0.018307  0.277838 -0.110474  0.066928  0.128539   \n",
              "1  0.085102 -0.255425  ... -0.225775 -0.638672  0.101288 -0.339846  0.167170   \n",
              "2  0.247676 -1.514654  ...  0.247998  0.771679  0.909412 -0.689281 -0.327642   \n",
              "3  0.377436 -1.387024  ... -0.108300  0.005274 -0.190321 -1.175575  0.647376   \n",
              "4 -0.270533  0.817739  ... -0.009431  0.798278 -0.137458  0.141267 -0.206010   \n",
              "\n",
              "        V26       V27       V28  Amount  Class  \n",
              "0 -0.189115  0.133558 -0.021053  149.62      0  \n",
              "1  0.125895 -0.008983  0.014724    2.69      0  \n",
              "2 -0.139097 -0.055353 -0.059752  378.66      0  \n",
              "3 -0.221929  0.062723  0.061458  123.50      0  \n",
              "4  0.502292  0.219422  0.215153   69.99      0  \n",
              "\n",
              "[5 rows x 31 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 6
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "scrolled": true,
        "id": "7OsNA-l07xGv",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "#It's good to shuffle the datset \n",
        "df = df.sample(frac=1, random_state=42).reset_index(drop=True)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "wZ6fTnia7xGy",
        "colab_type": "code",
        "colab": {},
        "outputId": "b57f0843-bd0b-44ad-e7dc-fa0b2f3e82c6"
      },
      "source": [
        "#Let's take a look again \n",
        "df.head()"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "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>Time</th>\n",
              "      <th>V1</th>\n",
              "      <th>V2</th>\n",
              "      <th>V3</th>\n",
              "      <th>V4</th>\n",
              "      <th>V5</th>\n",
              "      <th>V6</th>\n",
              "      <th>V7</th>\n",
              "      <th>V8</th>\n",
              "      <th>V9</th>\n",
              "      <th>...</th>\n",
              "      <th>V21</th>\n",
              "      <th>V22</th>\n",
              "      <th>V23</th>\n",
              "      <th>V24</th>\n",
              "      <th>V25</th>\n",
              "      <th>V26</th>\n",
              "      <th>V27</th>\n",
              "      <th>V28</th>\n",
              "      <th>Amount</th>\n",
              "      <th>Class</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>41505.0</td>\n",
              "      <td>-16.526507</td>\n",
              "      <td>8.584972</td>\n",
              "      <td>-18.649853</td>\n",
              "      <td>9.505594</td>\n",
              "      <td>-13.793819</td>\n",
              "      <td>-2.832404</td>\n",
              "      <td>-16.701694</td>\n",
              "      <td>7.517344</td>\n",
              "      <td>-8.507059</td>\n",
              "      <td>...</td>\n",
              "      <td>1.190739</td>\n",
              "      <td>-1.127670</td>\n",
              "      <td>-2.358579</td>\n",
              "      <td>0.673461</td>\n",
              "      <td>-1.413700</td>\n",
              "      <td>-0.462762</td>\n",
              "      <td>-2.018575</td>\n",
              "      <td>-1.042804</td>\n",
              "      <td>364.19</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>44261.0</td>\n",
              "      <td>0.339812</td>\n",
              "      <td>-2.743745</td>\n",
              "      <td>-0.134070</td>\n",
              "      <td>-1.385729</td>\n",
              "      <td>-1.451413</td>\n",
              "      <td>1.015887</td>\n",
              "      <td>-0.524379</td>\n",
              "      <td>0.224060</td>\n",
              "      <td>0.899746</td>\n",
              "      <td>...</td>\n",
              "      <td>-0.213436</td>\n",
              "      <td>-0.942525</td>\n",
              "      <td>-0.526819</td>\n",
              "      <td>-1.156992</td>\n",
              "      <td>0.311211</td>\n",
              "      <td>-0.746647</td>\n",
              "      <td>0.040996</td>\n",
              "      <td>0.102038</td>\n",
              "      <td>520.12</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>35484.0</td>\n",
              "      <td>1.399590</td>\n",
              "      <td>-0.590701</td>\n",
              "      <td>0.168619</td>\n",
              "      <td>-1.029950</td>\n",
              "      <td>-0.539806</td>\n",
              "      <td>0.040444</td>\n",
              "      <td>-0.712567</td>\n",
              "      <td>0.002299</td>\n",
              "      <td>-0.971747</td>\n",
              "      <td>...</td>\n",
              "      <td>0.102398</td>\n",
              "      <td>0.168269</td>\n",
              "      <td>-0.166639</td>\n",
              "      <td>-0.810250</td>\n",
              "      <td>0.505083</td>\n",
              "      <td>-0.232340</td>\n",
              "      <td>0.011409</td>\n",
              "      <td>0.004634</td>\n",
              "      <td>31.00</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>167123.0</td>\n",
              "      <td>-0.432071</td>\n",
              "      <td>1.647895</td>\n",
              "      <td>-1.669361</td>\n",
              "      <td>-0.349504</td>\n",
              "      <td>0.785785</td>\n",
              "      <td>-0.630647</td>\n",
              "      <td>0.276990</td>\n",
              "      <td>0.586025</td>\n",
              "      <td>-0.484715</td>\n",
              "      <td>...</td>\n",
              "      <td>0.358932</td>\n",
              "      <td>0.873663</td>\n",
              "      <td>-0.178642</td>\n",
              "      <td>-0.017171</td>\n",
              "      <td>-0.207392</td>\n",
              "      <td>-0.157756</td>\n",
              "      <td>-0.237386</td>\n",
              "      <td>0.001934</td>\n",
              "      <td>1.50</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>168473.0</td>\n",
              "      <td>2.014160</td>\n",
              "      <td>-0.137394</td>\n",
              "      <td>-1.015839</td>\n",
              "      <td>0.327269</td>\n",
              "      <td>-0.182179</td>\n",
              "      <td>-0.956571</td>\n",
              "      <td>0.043241</td>\n",
              "      <td>-0.160746</td>\n",
              "      <td>0.363241</td>\n",
              "      <td>...</td>\n",
              "      <td>-0.238644</td>\n",
              "      <td>-0.616400</td>\n",
              "      <td>0.347045</td>\n",
              "      <td>0.061561</td>\n",
              "      <td>-0.360196</td>\n",
              "      <td>0.174730</td>\n",
              "      <td>-0.078043</td>\n",
              "      <td>-0.070571</td>\n",
              "      <td>0.89</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>5 rows × 31 columns</p>\n",
              "</div>"
            ],
            "text/plain": [
              "       Time         V1        V2         V3        V4         V5        V6  \\\n",
              "0   41505.0 -16.526507  8.584972 -18.649853  9.505594 -13.793819 -2.832404   \n",
              "1   44261.0   0.339812 -2.743745  -0.134070 -1.385729  -1.451413  1.015887   \n",
              "2   35484.0   1.399590 -0.590701   0.168619 -1.029950  -0.539806  0.040444   \n",
              "3  167123.0  -0.432071  1.647895  -1.669361 -0.349504   0.785785 -0.630647   \n",
              "4  168473.0   2.014160 -0.137394  -1.015839  0.327269  -0.182179 -0.956571   \n",
              "\n",
              "          V7        V8        V9  ...       V21       V22       V23       V24  \\\n",
              "0 -16.701694  7.517344 -8.507059  ...  1.190739 -1.127670 -2.358579  0.673461   \n",
              "1  -0.524379  0.224060  0.899746  ... -0.213436 -0.942525 -0.526819 -1.156992   \n",
              "2  -0.712567  0.002299 -0.971747  ...  0.102398  0.168269 -0.166639 -0.810250   \n",
              "3   0.276990  0.586025 -0.484715  ...  0.358932  0.873663 -0.178642 -0.017171   \n",
              "4   0.043241 -0.160746  0.363241  ... -0.238644 -0.616400  0.347045  0.061561   \n",
              "\n",
              "        V25       V26       V27       V28  Amount  Class  \n",
              "0 -1.413700 -0.462762 -2.018575 -1.042804  364.19      1  \n",
              "1  0.311211 -0.746647  0.040996  0.102038  520.12      0  \n",
              "2  0.505083 -0.232340  0.011409  0.004634   31.00      0  \n",
              "3 -0.207392 -0.157756 -0.237386  0.001934    1.50      0  \n",
              "4 -0.360196  0.174730 -0.078043 -0.070571    0.89      0  \n",
              "\n",
              "[5 rows x 31 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 8
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "_g9dKmvq7xGz",
        "colab_type": "code",
        "colab": {},
        "outputId": "3069958d-08da-469e-dc17-6e1e4247758a"
      },
      "source": [
        "#Non fradulant cases\n",
        "df.Class.value_counts()[0]"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "284315"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 5
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ULOtq22F7xG1",
        "colab_type": "code",
        "colab": {},
        "outputId": "6c1d4b17-e68e-450a-938e-fdc62a03c4f4"
      },
      "source": [
        "#Fradulant cases \n",
        "df.Class.value_counts()[1]"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "492"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 6
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "xEcVeodD7xG4",
        "colab_type": "code",
        "colab": {},
        "outputId": "b9ac08f4-be39-4f6d-86f5-19b8e394430c"
      },
      "source": [
        "print('Percentage of correct transactions: {}'.format((df.Class.value_counts()[0]/df.shape[0])*100))"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Percentage of correct transactions: 99.82725143693798\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "liY0KSjc7xG5",
        "colab_type": "code",
        "colab": {},
        "outputId": "2b208b8b-a7fd-4d72-cfa6-10bc80c3d307"
      },
      "source": [
        "print('Percentage of fradulent transactions: {}'.format((df.Class.value_counts()[1]/df.shape[0])*100))"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Percentage of fradulent transactions: 0.1727485630620034\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "h_-7tzND7xG7",
        "colab_type": "code",
        "colab": {},
        "outputId": "cdb5b5e5-02a0-4262-f41f-a455bbd92833"
      },
      "source": [
        "#Let's visualize the distribution of the classes (0 means safe and 1 means fraudulent)\n",
        "import seaborn as sns\n",
        "colors = ['green', 'red']\n",
        "\n",
        "sns.countplot('Class', data=df, palette=colors)\n",
        "plt.title('Normal v/s Fraudulent')"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Text(0.5, 1.0, 'Normal v/s Fraudulent')"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 9
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": "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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "rJlyy0MW7xG9",
        "colab_type": "code",
        "colab": {},
        "outputId": "66d0a359-1e5d-4ac8-bbff-bc81d403ad3e"
      },
      "source": [
        "#Now let's map how much a feature affects our class \n",
        "cor = df.corr()\n",
        "fig = plt.figure(figsize = (12, 9))\n",
        "\n",
        "#Plotting the heatmap\n",
        "sns.heatmap(cor, vmax = 0.7)\n",
        "plt.show()"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 864x648 with 2 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "aJ6gAgDl7xG-",
        "colab_type": "code",
        "colab": {},
        "outputId": "2e628b12-74e8-4729-c24e-b3622f450501"
      },
      "source": [
        "cor.shape"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(31, 31)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 15
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Nmp7y3kO7xHA",
        "colab_type": "code",
        "colab": {},
        "outputId": "300bec1a-d5b3-4c6a-dfe7-06495e8c2bca"
      },
      "source": [
        "#This is how much a each feature affects the our class \n",
        "cor.iloc[-1,:]"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Time     -0.012323\n",
              "V1       -0.101347\n",
              "V2        0.091289\n",
              "V3       -0.192961\n",
              "V4        0.133447\n",
              "V5       -0.094974\n",
              "V6       -0.043643\n",
              "V7       -0.187257\n",
              "V8        0.019875\n",
              "V9       -0.097733\n",
              "V10      -0.216883\n",
              "V11       0.154876\n",
              "V12      -0.260593\n",
              "V13      -0.004570\n",
              "V14      -0.302544\n",
              "V15      -0.004223\n",
              "V16      -0.196539\n",
              "V17      -0.326481\n",
              "V18      -0.111485\n",
              "V19       0.034783\n",
              "V20       0.020090\n",
              "V21       0.040413\n",
              "V22       0.000805\n",
              "V23      -0.002685\n",
              "V24      -0.007221\n",
              "V25       0.003308\n",
              "V26       0.004455\n",
              "V27       0.017580\n",
              "V28       0.009536\n",
              "Amount    0.005632\n",
              "Class     1.000000\n",
              "Name: Class, dtype: float64"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 16
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Uy6UUIuO7xHC",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "#We need to delet the least and greatest values \n",
        "#From above analysis I've selected the following features \n",
        "#Note that I've included the class variable also because I intend to create a new dataframe using the new features\n",
        "new_features=['V1','V3','V4','V7','V10','V11','V12','V14','V16','V17','V18','Class']"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "QuYxmZ-97xHD",
        "colab_type": "code",
        "colab": {},
        "outputId": "04f9ff78-1b65-4c72-a63b-8e3aa97158d9"
      },
      "source": [
        "#Let's plot a heatmap again and see the relationship\n",
        "cor = df[new_features].corr()\n",
        "fig = plt.figure(figsize = (12, 9))\n",
        "\n",
        "#Plotting the heatmap\n",
        "sns.heatmap(cor, vmax = 0.7)\n",
        "plt.show()"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 864x648 with 2 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "jl1MV5cW7xHF",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "#We see that the class rows and columns are darker and brighter \n",
        "#This means that all the variables in our new dataset have a significant affect "
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "OqQGQVr27xHG",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "#Now splitting the dataset into the dependent variable(y) and independent variales(x)\n",
        "x=df[new_features].iloc[:,:-1].values\n",
        "y=df[new_features].iloc[:,-1].values"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "9WsdWgLY7xHN",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "#Withoud reducing the features \n",
        "#x=df.iloc[:,:-1].values\n",
        "#y=df.iloc[:,-1].values\n",
        "#Feel free to try using all the features :)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "7D1qysuQ7xHT",
        "colab_type": "code",
        "colab": {},
        "outputId": "4aea8c55-0f13-4bca-f657-1b88faff2d10"
      },
      "source": [
        "x.shape"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(284807, 11)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 14
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "W1nSVNer7xHZ",
        "colab_type": "code",
        "colab": {},
        "outputId": "f7205c7a-ce89-4d94-b8d2-c90271df54aa"
      },
      "source": [
        "y.shape"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(284807,)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 15
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "3-J9jgJa7xHc",
        "colab_type": "code",
        "colab": {},
        "outputId": "11902b69-1d7e-4be6-fbee-cd848ea55d4e"
      },
      "source": [
        "x[:5]"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([[-1.35980713,  2.53634674,  1.37815522,  0.23959855,  0.09079417,\n",
              "        -0.55159953, -0.61780086, -0.31116935, -0.47040053,  0.20797124,\n",
              "         0.02579058],\n",
              "       [ 1.19185711,  0.16648011,  0.44815408, -0.07880298, -0.16697441,\n",
              "         1.61272666,  1.06523531, -0.1437723 ,  0.46391704, -0.11480466,\n",
              "        -0.18336127],\n",
              "       [-1.35835406,  1.77320934,  0.37977959,  0.79146096,  0.20764287,\n",
              "         0.62450146,  0.06608369, -0.16594592, -2.89008319,  1.10996938,\n",
              "        -0.12135931],\n",
              "       [-0.96627171,  1.79299334, -0.86329128,  0.23760894, -0.05495192,\n",
              "        -0.22648726,  0.17822823, -0.28792375, -1.05964725, -0.68409279,\n",
              "         1.965775  ],\n",
              "       [-1.15823309,  1.54871785,  0.40303393,  0.59294075,  0.75307443,\n",
              "        -0.82284288,  0.53819555, -1.11966983, -0.45144918, -0.23703324,\n",
              "        -0.03819479]])"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 16
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "evIdLqZ27xHe",
        "colab_type": "code",
        "colab": {},
        "outputId": "d33008b0-6b8a-4ac0-f3ab-ffa6646b5020"
      },
      "source": [
        "y[:5]"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([0, 0, 0, 0, 0])"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 17
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "HoI5pk3R7xI_",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "#Spliting the data into train and test sets \n",
        "from sklearn.model_selection import train_test_split\n",
        "x_train, x_test, y_train, y_test = train_test_split(x, y, test_size = 0.25, random_state = 0)\n"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "tZOvLj607xJB",
        "colab_type": "code",
        "colab": {},
        "outputId": "b4349516-0e89-4f67-d316-188a51e4fd79"
      },
      "source": [
        "#Let's see how many safe and fraudulent cases are there in training set \n",
        "safe_train=(y_train==0).sum()\n",
        "fraud_train=(y_train==1).sum()\n",
        "print(\"Safe: {} \\nFraud: {}\".format(safe_train,fraud_train))"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Safe: 213233 \n",
            "Fraud: 372\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "RoKfd5WH7xJD",
        "colab_type": "code",
        "colab": {},
        "outputId": "210d0a37-c43e-4296-81c3-75bce4fbbcff"
      },
      "source": [
        "#Let's see how many safe and fraudulent cases are there in test set \n",
        "safe_test=(y_test==0).sum()\n",
        "fraud_test=(y_test==1).sum()\n",
        "print(\"Safe: {} \\nFraud: {}\".format(safe_test,fraud_test))"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Safe: 71082 \n",
            "Fraud: 120\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "x9kRh1id7xJF",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "from sklearn.linear_model import LogisticRegression\n",
        "from sklearn.metrics import accuracy_score, f1_score, precision_score, recall_score, classification_report, confusion_matrix"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "s3u75qae7xJI",
        "colab_type": "code",
        "colab": {},
        "outputId": "bd464976-2907-4bc9-f203-bfe2aac7fc6f"
      },
      "source": [
        "#Using Logistic Regression \n",
        "clf = LogisticRegression(random_state = 0)\n",
        "clf.fit(x_train, y_train)"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "LogisticRegression(C=1.0, class_weight=None, dual=False, fit_intercept=True,\n",
              "                   intercept_scaling=1, l1_ratio=None, max_iter=100,\n",
              "                   multi_class='warn', n_jobs=None, penalty='l2',\n",
              "                   random_state=0, solver='warn', tol=0.0001, verbose=0,\n",
              "                   warm_start=False)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 23
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "scrolled": true,
        "id": "CRvA7rir7xJK",
        "colab_type": "code",
        "colab": {},
        "outputId": "2465993c-7a75-4bca-801b-edea520cdf21"
      },
      "source": [
        "#Let's evaluate our model \n",
        "y_pred = clf.predict(x_test)\n",
        "print(\"Training Accuracy: \",clf.score(x_train, y_train))\n",
        "print(\"Testing Accuracy: \", clf.score(x_test, y_test))\n",
        "cm = confusion_matrix(y_test, y_pred)\n",
        "print(cm)\n",
        "print(classification_report(y_test,y_pred)) "
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Training Accuracy:  0.999180730788137\n",
            "Testing Accuracy:  0.9992556388865481\n",
            "[[71072    10]\n",
            " [   43    77]]\n",
            "              precision    recall  f1-score   support\n",
            "\n",
            "           0       1.00      1.00      1.00     71082\n",
            "           1       0.89      0.64      0.74       120\n",
            "\n",
            "    accuracy                           1.00     71202\n",
            "   macro avg       0.94      0.82      0.87     71202\n",
            "weighted avg       1.00      1.00      1.00     71202\n",
            "\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "QStvdJ4I7xJM",
        "colab_type": "text"
      },
      "source": [
        "# Looking at the Accuracy fallacy \n",
        "\n",
        "## What it is ? \n",
        "If we look at the accuracy on test set, it's nearly 0.99914 i.e. over 99.9% accuracy.\n",
        "Does that mean our model is phenomenal? No!\n",
        "Before that let me tell you what a confusion matrix for binary classification represents \n",
        "We made the matrix between the actual values and predicted values, so the rows represent the actual values and columns respresent the predicted values. Now that you know what rows and colmns are let's understand what each cell represnt hence:\n",
        "1. [0][0] -> True positives i.e. how many safe cases are there that our model predicted correctly. Here 71069 are the number of CORRECTLY PREDICTED safe cases. \n",
        "2. [0][1] -> False Negatives i.e. how many safe cases are there that our model predicted incorrectly. Here 4 are the number of the MISCLASSIFIED safe cases. Hence 4 safe cases were misclassified as fraud. This is potentially less dangerous as it's better to stop some safe transactions with slightest chance of fraud.\n",
        "3. [1][0] -> False Positives i.e. how many fraud cases are there that our model predicted incorrectly. Here 57 are the number of the MISCLASSIFIED fraud cases. Here 57 fraud cases were misclassified as safe. This is very dangerous because we are letting the fraud cases pass through. This can cause huge loss to the organization. \n",
        "4. [1][1] -> True negatives i.e. how many fraud cases are there that our model predicted correctly. Here 72 are the number of CORRECTLY PREDICTED fraud cases.\n",
        "\n",
        "### We can see that despite having an accuracy over 99.9% our model predicted 57 fraud cases incorrectly. This is what I call accuracy fallacy.\n",
        "This usually happens when data is UNEVENLY DISTRIBUTED. From above code we realize that the number of fraud cases in training set is just 363 whereas the number of safe ones is 213242. This is very unevenly distributed and will give great accuracy but misclassify the dangerous classes.\n",
        "\n",
        "To give you a perspective, let me misclassify all the fraudulent cases.\n",
        "Let's predict every case as safe, so our confusion matrix for the test set is as follows:\n",
        "\n",
        "[[71073         0]\n",
        "\n",
        " [129           0]]\n",
        " \n",
        "So new accuracy = 71073/(71073 +  129) = 0.9981\n",
        "which is nearly 99.8% accuracy.\n",
        "Again great accuracy, terrible performance.\n",
        "\n",
        "## So how we measure model performance?\n",
        "\n",
        "### The answer is precision, recall, f1 score and AUC-ROC curve.\n",
        "Lets look at each one of them one by one\n",
        "![image.png](attachment:image.png)\n",
        "\n",
        "Image source : Wikipedia \n",
        "### Precision:\n",
        "Precision refers to the percentage of your results which are relevant and is calculated as follows : \n",
        "True Positives/(True Positives + Flase Positives)\n",
        "\n",
        "### Recall:\n",
        "Recall refers to the percentage of total relevant results correctly classified by your algorithm and is calculated as follows : True Positives/(True Positives + False Negatives)\n",
        "\n",
        "### F1 Score:\n",
        "It is the harmonic mean of precision and recall and is calculated as follows: \n",
        "(2 x Precision x Recall)/(Precision + Recall)\n",
        "\n",
        "## Hence the greater the F1 score the better it is\n",
        "In the above example we see that f1 score of safe class is 1 but that of fraud is 0.70 and hence the macro average is 0.85 \n",
        "\n",
        "### AUC-ROC curve (Area Under Curve - Receiver Operating Characteristic Curve):\n",
        "The True Positive Rate (TPR) is plot against False Positive Rate (FPR) for the probabilities of the classifier predictions. Then, the area under the plot is calculated. The greater the area under the curve the better our model is.\n",
        "\n",
        "TPR is also knowns as recall and hence = True Positives/(True Positives + False Negatives)\n",
        "\n",
        "FPR is negation of specificity = 1 - Specificity = 1- True Negatives/(True Negatives + False Positives) \n",
        "\n",
        "Hence FPR = False Positives/(True Negatives + False Positives)\n",
        "\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Spfyet6L7xJN",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "from sklearn.metrics import roc_curve, auc"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "cbfGYb2B7xJO",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "#Calculating the FPR and TPR\n",
        "fpr, tpr, thresholds = roc_curve(y_test, y_pred)\n",
        "roc_auc = auc(fpr, tpr)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ASQB8Xeo7xJQ",
        "colab_type": "code",
        "colab": {},
        "outputId": "ce063bc4-4085-487c-b774-9f96b2c5be8e"
      },
      "source": [
        "#Plotting the curves \n",
        "label = 'Logistic Regressoin Classifier AUC:' + ' {0:.2f}'.format(roc_auc)\n",
        "label2 = 'Random Model' \n",
        "plt.figure(figsize = (20, 12))\n",
        "plt.plot([0,1], [0,1], 'r--', label=label2)\n",
        "plt.plot(fpr, tpr, c = 'g', label = label, linewidth = 4)\n",
        "plt.xlabel('False Positive Rate', fontsize = 16)\n",
        "plt.ylabel('True Positive Rate', fontsize = 16)\n",
        "plt.title('Receiver Operating Characteristic', fontsize = 16)\n",
        "plt.legend(loc = 'lower right', fontsize = 16)"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.legend.Legend at 0x7fbbfae97400>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 27
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 1440x864 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "NwJgFRGz7xJS",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "#We see that AUC is 0.78 which is not bad, not great but fair enough"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "1tmE0l9J7xJU",
        "colab_type": "text"
      },
      "source": [
        "## Let's try a supervised anomaly detection algorithm KNN\n",
        "\n",
        "We use anomaly detection algorithm to find unusual patterns in the data. Since the data is unbalanced greatly and there must be some unusual patterns in data, let's try K Nearest Neighbours Algorithm "
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "0VrIZ7IU7xJV",
        "colab_type": "code",
        "colab": {},
        "outputId": "6e373cb0-44d3-4c30-f6ef-2d3869331a8f"
      },
      "source": [
        "from sklearn.neighbors import KNeighborsClassifier\n",
        "clf = KNeighborsClassifier(n_neighbors = 5, metric = 'minkowski', p = 2)\n",
        "clf.fit(x_train, y_train)"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "KNeighborsClassifier(algorithm='auto', leaf_size=30, metric='minkowski',\n",
              "                     metric_params=None, n_jobs=None, n_neighbors=5, p=2,\n",
              "                     weights='uniform')"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 28
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "c3rbtIj67xJX",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "KNeighborsClassifier?"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "pRfI2uER7xJZ",
        "colab_type": "code",
        "colab": {},
        "outputId": "9b7cd640-0d4b-49dc-f334-d1f71aa75339"
      },
      "source": [
        "y_pred = clf.predict(x_test)\n",
        "print(\"Training Accuracy: \",clf.score(x_train, y_train))\n",
        "print(\"Testing Accuracy: \", clf.score(x_test, y_test))\n",
        "cm = confusion_matrix(y_test, y_pred)\n",
        "print(cm)\n",
        "print(classification_report(y_test,y_pred)) "
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Training Accuracy:  0.9995786615481848\n",
            "Testing Accuracy:  0.9995646189713772\n",
            "[[71076     6]\n",
            " [   25    95]]\n",
            "              precision    recall  f1-score   support\n",
            "\n",
            "           0       1.00      1.00      1.00     71082\n",
            "           1       0.94      0.79      0.86       120\n",
            "\n",
            "    accuracy                           1.00     71202\n",
            "   macro avg       0.97      0.90      0.93     71202\n",
            "weighted avg       1.00      1.00      1.00     71202\n",
            "\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "xrTczdu87xJb",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "fpr, tpr, thresholds = roc_curve(y_test, y_pred)\n",
        "roc_auc = auc(fpr, tpr)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "KVhhF5AX7xJf",
        "colab_type": "code",
        "colab": {},
        "outputId": "aa5dc743-d730-4e2b-fc9f-4e7e669e9f2b"
      },
      "source": [
        "label = 'KNN Classifier AUC:' + ' {0:.2f}'.format(roc_auc)\n",
        "label2 = 'Random Model' \n",
        "plt.figure(figsize = (20, 12))\n",
        "plt.plot([0,1], [0,1], 'r--', label=label2)\n",
        "plt.plot(fpr, tpr, c = 'g', label = label, linewidth = 4)\n",
        "plt.xlabel('False Positive Rate', fontsize = 16)\n",
        "plt.ylabel('True Positive Rate', fontsize = 16)\n",
        "plt.title('Receiver Operating Characteristic', fontsize = 16)\n",
        "plt.legend(loc = 'lower right', fontsize = 16)"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.legend.Legend at 0x7fbbfa272860>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 32
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 1440x864 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "l3NefJXx7xJh",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "#We see that AUC is 0.90 which is good, with different random state of data you may get different AUC.\n",
        "#I once achieved 0.94\n",
        "#Though I've kept a constant seed for random state wile shuffling the dataset, feel free to mess with that :D"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "FigritoT7xJi",
        "colab_type": "text"
      },
      "source": [
        "## Now let's try an unsupervised algorithm namely Isolation forest \n",
        "\n",
        "Since it is unsupervised, we don't need the target feature that is our class variable. Also we can use entire dataset instead of training on one and hence testing on the other\n",
        "\n",
        "Isolation forest tries to separate each point in the data. Here an anomalous point could be separated in a few steps while normal points which are closer could take significantly more steps to be segregated."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "6Bbfx7a97xJj",
        "colab_type": "code",
        "colab": {},
        "outputId": "439f8fac-a1fa-451c-ac41-e343e794b146"
      },
      "source": [
        "(df.Class.value_counts()[1]/df.Class.value_counts()[0])"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.0017304750013189597"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 41
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "2ZjL8L0s7xJt",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "IsolationForest?"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "azVACddS7xJx",
        "colab_type": "code",
        "colab": {},
        "outputId": "89d62c54-628a-4ecc-b464-d08ad5fbbd07"
      },
      "source": [
        "#Importing and fitting the Isolation Forest Algorithm \n",
        "from sklearn.ensemble import IsolationForest\n",
        "clf=IsolationForest(n_estimators=100,contamination=(df.Class.value_counts()[1]/df.shape[0]), random_state=42,max_features=x.shape[1])\n",
        "clf.fit(x)"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "IsolationForest(behaviour='old', bootstrap=False,\n",
              "                contamination=0.001727485630620034, max_features=11,\n",
              "                max_samples='auto', n_estimators=100, n_jobs=None,\n",
              "                random_state=42, verbose=0, warm_start=False)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 98
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "-UX2YatT7xJ0",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "#Predicting the class\n",
        "y_pred = clf.predict(x)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "KbvFPVef7xJ4",
        "colab_type": "code",
        "colab": {},
        "outputId": "971cd6cf-eb35-4024-d671-c6f448471180"
      },
      "source": [
        "#Since the algorithm classifies one class as 1 and other as -1\n",
        "#Let's see how many classes it predicted as fraudulent \n",
        "(y_pred==-1).sum()"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "492"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 100
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "XgkLULfg7xJ_",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "#Since our class variables are either 0 or 1, so we need to replace the predicted classes as 0 and 1\n",
        "y_pred[y_pred == 1] = 0\n",
        "y_pred[y_pred == -1] = 1"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "scrolled": true,
        "id": "i4T1NMxZ7xKD",
        "colab_type": "code",
        "colab": {},
        "outputId": "197ff802-e1b2-4a80-8cc4-b1eb092c4e18"
      },
      "source": [
        "#Let's see how good our model performed \n",
        "print(\"Training Accuracy: \",accuracy_score(y, y_pred))\n",
        "cm = confusion_matrix(y, y_pred)\n",
        "print(cm)\n",
        "print(classification_report(y,y_pred))"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Training Accuracy:  0.9985323394439041\n",
            "[[284106    209]\n",
            " [   209    283]]\n",
            "              precision    recall  f1-score   support\n",
            "\n",
            "           0       1.00      1.00      1.00    284315\n",
            "           1       0.58      0.58      0.58       492\n",
            "\n",
            "    accuracy                           1.00    284807\n",
            "   macro avg       0.79      0.79      0.79    284807\n",
            "weighted avg       1.00      1.00      1.00    284807\n",
            "\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "cCBfRLRB7xKL",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "fpr, tpr, thresholds = roc_curve(y, y_pred)\n",
        "roc_auc = auc(fpr, tpr)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "lx4UafXO7xKS",
        "colab_type": "code",
        "colab": {},
        "outputId": "a50385e6-a68d-4d81-f135-0dbf8e22d857"
      },
      "source": [
        "label = 'Isolation Forest Classifier AUC:' + ' {0:.2f}'.format(roc_auc)\n",
        "label2 = 'Random Model' \n",
        "plt.figure(figsize = (20, 12))\n",
        "plt.plot([0,1], [0,1], 'r--', label=label2)\n",
        "plt.plot(fpr, tpr, c = 'g', label = label, linewidth = 4)\n",
        "plt.xlabel('False Positive Rate', fontsize = 16)\n",
        "plt.ylabel('True Positive Rate', fontsize = 16)\n",
        "plt.title('Receiver Operating Characteristic', fontsize = 16)\n",
        "plt.legend(loc = 'lower right', fontsize = 16)"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.legend.Legend at 0x7fc318378f98>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 104
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 1440x864 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "-HYKfUKa7xKV",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "#We get AUC score of 0.79, that's bad. Seems like this algorithm is not working very well on our dataset "
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "0_-M2CrR7xKY",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "#Let's try another unsupervised algorithm \n",
        "from sklearn.neighbors import LocalOutlierFactor\n",
        "clf=LocalOutlierFactor(p=3,n_neighbors=5,contamination=(df.Class.value_counts()[1]/df.shape[0]))"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "kcWKLGcI7xKc",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "LocalOutlierFactor?"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "KiGPcnXP7xKf",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "y_pred = clf.fit_predict(x)\n",
        "y_pred[y_pred == 1] = 0\n",
        "y_pred[y_pred == -1] = 1"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "TNxY7WoT7xKi",
        "colab_type": "code",
        "colab": {},
        "outputId": "b5bc63c1-4022-42cc-d795-177df8c93b65"
      },
      "source": [
        "print(\"Training Accuracy: \",accuracy_score(y, y_pred))\n",
        "cm = confusion_matrix(y, y_pred)\n",
        "print(cm)\n",
        "print(classification_report(y,y_pred))"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Training Accuracy:  0.9965450287387599\n",
            "[[283823    492]\n",
            " [   492      0]]\n",
            "              precision    recall  f1-score   support\n",
            "\n",
            "           0       1.00      1.00      1.00    284315\n",
            "           1       0.00      0.00      0.00       492\n",
            "\n",
            "    accuracy                           1.00    284807\n",
            "   macro avg       0.50      0.50      0.50    284807\n",
            "weighted avg       1.00      1.00      1.00    284807\n",
            "\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Q3Ixc7LT7xKu",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "fpr, tpr, thresholds = roc_curve(y, y_pred)\n",
        "roc_auc = auc(fpr, tpr)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "GuQp37Mz7xK3",
        "colab_type": "code",
        "colab": {},
        "outputId": "3a13f328-83e2-43b7-da19-9147ad5f3f29"
      },
      "source": [
        "label = 'KNN Classifier AUC:' + ' {0:.2f}'.format(roc_auc)\n",
        "label2 = 'Random Model' \n",
        "plt.figure(figsize = (20, 12))\n",
        "plt.plot([0,1], [0,1], 'r--', label=label2)\n",
        "plt.plot(fpr, tpr, c = 'g', label = label, linewidth = 4)\n",
        "plt.xlabel('False Positive Rate', fontsize = 16)\n",
        "plt.ylabel('True Positive Rate', fontsize = 16)\n",
        "plt.title('Receiver Operating Characteristic', fontsize = 16)\n",
        "plt.legend(loc = 'lower right', fontsize = 16)"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.legend.Legend at 0x7fd8cf77f9e8>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 52
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 1440x864 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "tSkRELD47xK7",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "#WHAT!!!!? That's a garbage model with an AUC of 0.5.\n",
        "#Moreover it made 0 true negatives and that's a teriible, terrible model"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "JhfU86Tl7xK_",
        "colab_type": "text"
      },
      "source": [
        "For now it seems that *KNN* is performing quite well for the given data"
      ]
    }
  ]
}