logistic_regression_for_credit_card_fraud_detection.ipynb

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  "metadata": {
    "colab": {
      "name": "Logistic_2",
      "provenance": [],
      "collapsed_sections": [],
      "toc_visible": true
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    }
  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "mjasNrhs1dc4"
      },
      "source": [
        "# Logistic Regression"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "yHnzQvtiwMsE"
      },
      "source": [
        "import pandas as pd \r\n",
        "import numpy as np\r\n",
        "from sklearn import preprocessing\r\n",
        "from sklearn.metrics import confusion_matrix\r\n",
        "from sklearn import svm\r\n",
        "import matplotlib.pyplot as plt\r\n",
        "import matplotlib.mlab as mlab\r\n",
        "import seaborn\r\n",
        "%matplotlib inline"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "5F8JGjt-wPgY",
        "outputId": "d31d9dc9-9e96-4aee-f156-f652ff45c575"
      },
      "source": [
        "from google.colab import drive\r\n",
        "drive.mount('/content/drive')"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Drive already mounted at /content/drive; to attempt to forcibly remount, call drive.mount(\"/content/drive\", force_remount=True).\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "f6OF7G7fwR6x"
      },
      "source": [
        "data = pd.read_csv('drive/MyDrive/creditcard.csv')\r\n",
        "df = pd.DataFrame(data)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "9FUP47MY1TxP"
      },
      "source": [
        "## Data Visualization"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 222
        },
        "id": "sPA3dHQvSCdv",
        "outputId": "e04579ad-3196-432b-f50c-b1783405e776"
      },
      "source": [
        "df.head()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
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              "\n",
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              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>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>V10</th>\n",
              "      <th>V11</th>\n",
              "      <th>V12</th>\n",
              "      <th>V13</th>\n",
              "      <th>V14</th>\n",
              "      <th>V15</th>\n",
              "      <th>V16</th>\n",
              "      <th>V17</th>\n",
              "      <th>V18</th>\n",
              "      <th>V19</th>\n",
              "      <th>V20</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>0.090794</td>\n",
              "      <td>-0.551600</td>\n",
              "      <td>-0.617801</td>\n",
              "      <td>-0.991390</td>\n",
              "      <td>-0.311169</td>\n",
              "      <td>1.468177</td>\n",
              "      <td>-0.470401</td>\n",
              "      <td>0.207971</td>\n",
              "      <td>0.025791</td>\n",
              "      <td>0.403993</td>\n",
              "      <td>0.251412</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>-0.166974</td>\n",
              "      <td>1.612727</td>\n",
              "      <td>1.065235</td>\n",
              "      <td>0.489095</td>\n",
              "      <td>-0.143772</td>\n",
              "      <td>0.635558</td>\n",
              "      <td>0.463917</td>\n",
              "      <td>-0.114805</td>\n",
              "      <td>-0.183361</td>\n",
              "      <td>-0.145783</td>\n",
              "      <td>-0.069083</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>0.207643</td>\n",
              "      <td>0.624501</td>\n",
              "      <td>0.066084</td>\n",
              "      <td>0.717293</td>\n",
              "      <td>-0.165946</td>\n",
              "      <td>2.345865</td>\n",
              "      <td>-2.890083</td>\n",
              "      <td>1.109969</td>\n",
              "      <td>-0.121359</td>\n",
              "      <td>-2.261857</td>\n",
              "      <td>0.524980</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>-0.054952</td>\n",
              "      <td>-0.226487</td>\n",
              "      <td>0.178228</td>\n",
              "      <td>0.507757</td>\n",
              "      <td>-0.287924</td>\n",
              "      <td>-0.631418</td>\n",
              "      <td>-1.059647</td>\n",
              "      <td>-0.684093</td>\n",
              "      <td>1.965775</td>\n",
              "      <td>-1.232622</td>\n",
              "      <td>-0.208038</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>0.753074</td>\n",
              "      <td>-0.822843</td>\n",
              "      <td>0.538196</td>\n",
              "      <td>1.345852</td>\n",
              "      <td>-1.119670</td>\n",
              "      <td>0.175121</td>\n",
              "      <td>-0.451449</td>\n",
              "      <td>-0.237033</td>\n",
              "      <td>-0.038195</td>\n",
              "      <td>0.803487</td>\n",
              "      <td>0.408542</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",
              "</div>"
            ],
            "text/plain": [
              "   Time        V1        V2        V3  ...       V27       V28  Amount  Class\n",
              "0   0.0 -1.359807 -0.072781  2.536347  ...  0.133558 -0.021053  149.62      0\n",
              "1   0.0  1.191857  0.266151  0.166480  ... -0.008983  0.014724    2.69      0\n",
              "2   1.0 -1.358354 -1.340163  1.773209  ... -0.055353 -0.059752  378.66      0\n",
              "3   1.0 -0.966272 -0.185226  1.792993  ...  0.062723  0.061458  123.50      0\n",
              "4   2.0 -1.158233  0.877737  1.548718  ...  0.219422  0.215153   69.99      0\n",
              "\n",
              "[5 rows x 31 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 4
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 314
        },
        "id": "njQC6i0V1CX5",
        "outputId": "9c91e56d-6998-4787-8a49-8b291dfd4aaa"
      },
      "source": [
        "df.describe()"
      ],
      "execution_count": null,
      "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>V10</th>\n",
              "      <th>V11</th>\n",
              "      <th>V12</th>\n",
              "      <th>V13</th>\n",
              "      <th>V14</th>\n",
              "      <th>V15</th>\n",
              "      <th>V16</th>\n",
              "      <th>V17</th>\n",
              "      <th>V18</th>\n",
              "      <th>V19</th>\n",
              "      <th>V20</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>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>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>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>1.768627e-15</td>\n",
              "      <td>9.170318e-16</td>\n",
              "      <td>-1.810658e-15</td>\n",
              "      <td>1.693438e-15</td>\n",
              "      <td>1.479045e-15</td>\n",
              "      <td>3.482336e-15</td>\n",
              "      <td>1.392007e-15</td>\n",
              "      <td>-7.528491e-16</td>\n",
              "      <td>4.328772e-16</td>\n",
              "      <td>9.049732e-16</td>\n",
              "      <td>5.085503e-16</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>1.088850e+00</td>\n",
              "      <td>1.020713e+00</td>\n",
              "      <td>9.992014e-01</td>\n",
              "      <td>9.952742e-01</td>\n",
              "      <td>9.585956e-01</td>\n",
              "      <td>9.153160e-01</td>\n",
              "      <td>8.762529e-01</td>\n",
              "      <td>8.493371e-01</td>\n",
              "      <td>8.381762e-01</td>\n",
              "      <td>8.140405e-01</td>\n",
              "      <td>7.709250e-01</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>-2.458826e+01</td>\n",
              "      <td>-4.797473e+00</td>\n",
              "      <td>-1.868371e+01</td>\n",
              "      <td>-5.791881e+00</td>\n",
              "      <td>-1.921433e+01</td>\n",
              "      <td>-4.498945e+00</td>\n",
              "      <td>-1.412985e+01</td>\n",
              "      <td>-2.516280e+01</td>\n",
              "      <td>-9.498746e+00</td>\n",
              "      <td>-7.213527e+00</td>\n",
              "      <td>-5.449772e+01</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>-5.354257e-01</td>\n",
              "      <td>-7.624942e-01</td>\n",
              "      <td>-4.055715e-01</td>\n",
              "      <td>-6.485393e-01</td>\n",
              "      <td>-4.255740e-01</td>\n",
              "      <td>-5.828843e-01</td>\n",
              "      <td>-4.680368e-01</td>\n",
              "      <td>-4.837483e-01</td>\n",
              "      <td>-4.988498e-01</td>\n",
              "      <td>-4.562989e-01</td>\n",
              "      <td>-2.117214e-01</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>-9.291738e-02</td>\n",
              "      <td>-3.275735e-02</td>\n",
              "      <td>1.400326e-01</td>\n",
              "      <td>-1.356806e-02</td>\n",
              "      <td>5.060132e-02</td>\n",
              "      <td>4.807155e-02</td>\n",
              "      <td>6.641332e-02</td>\n",
              "      <td>-6.567575e-02</td>\n",
              "      <td>-3.636312e-03</td>\n",
              "      <td>3.734823e-03</td>\n",
              "      <td>-6.248109e-02</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>4.539234e-01</td>\n",
              "      <td>7.395934e-01</td>\n",
              "      <td>6.182380e-01</td>\n",
              "      <td>6.625050e-01</td>\n",
              "      <td>4.931498e-01</td>\n",
              "      <td>6.488208e-01</td>\n",
              "      <td>5.232963e-01</td>\n",
              "      <td>3.996750e-01</td>\n",
              "      <td>5.008067e-01</td>\n",
              "      <td>4.589494e-01</td>\n",
              "      <td>1.330408e-01</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>2.374514e+01</td>\n",
              "      <td>1.201891e+01</td>\n",
              "      <td>7.848392e+00</td>\n",
              "      <td>7.126883e+00</td>\n",
              "      <td>1.052677e+01</td>\n",
              "      <td>8.877742e+00</td>\n",
              "      <td>1.731511e+01</td>\n",
              "      <td>9.253526e+00</td>\n",
              "      <td>5.041069e+00</td>\n",
              "      <td>5.591971e+00</td>\n",
              "      <td>3.942090e+01</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",
              "</div>"
            ],
            "text/plain": [
              "                Time            V1  ...         Amount          Class\n",
              "count  284807.000000  2.848070e+05  ...  284807.000000  284807.000000\n",
              "mean    94813.859575  3.919560e-15  ...      88.349619       0.001727\n",
              "std     47488.145955  1.958696e+00  ...     250.120109       0.041527\n",
              "min         0.000000 -5.640751e+01  ...       0.000000       0.000000\n",
              "25%     54201.500000 -9.203734e-01  ...       5.600000       0.000000\n",
              "50%     84692.000000  1.810880e-02  ...      22.000000       0.000000\n",
              "75%    139320.500000  1.315642e+00  ...      77.165000       0.000000\n",
              "max    172792.000000  2.454930e+00  ...   25691.160000       1.000000\n",
              "\n",
              "[8 rows x 31 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 5
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 335
        },
        "id": "RDloxsdN1mZG",
        "outputId": "1257b9e4-c95b-4828-edf7-71d6337535dc"
      },
      "source": [
        "df_fraud = df[df['Class'] == 1] \r\n",
        "plt.figure(figsize=(15,5))\r\n",
        "plt.scatter(df_fraud['Time'], df_fraud['Amount'])\r\n",
        "plt.title('Scratter plot amount fraud')\r\n",
        "plt.xlabel('Time')\r\n",
        "plt.ylabel('Amount')\r\n",
        "plt.xlim([0,175000])\r\n",
        "plt.ylim([0,2500])\r\n",
        "plt.show()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 1080x360 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "tI-MCn7d1pGY",
        "outputId": "d7fb88fb-ad95-4857-dce3-9d0376bcd5ee"
      },
      "source": [
        "nb_big_fraud = df_fraud[df_fraud['Amount'] > 1000].shape[0] # Recovery of frauds over 1000\r\n",
        "print('There are only {} frauds where the amount was bigger than 1000 over {} frauds'.format(str(nb_big_fraud),str(df_fraud.shape[0]) ))"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "There are only 9 frauds where the amount was bigger than 1000 over 492 frauds\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "e2znQzCu1u8g"
      },
      "source": [
        "## Unbalanced Data"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "ZMiYVsI21s-j",
        "outputId": "e43bfd2b-fd19-45fd-dc53-2b0c97389ce9"
      },
      "source": [
        "number_fraud = len(data[data.Class == 1])\r\n",
        "number_no_fraud = len(data[data.Class == 0])\r\n",
        "print('There are only {}  frauds in the original dataset, even though there are {} no frauds in the dataset.'.format(str(number_fraud),str(number_no_fraud)))"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "There are only 492  frauds in the original dataset, even though there are 284315 no frauds in the dataset.\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "FvZ7dUvg1ywD",
        "outputId": "4a7dc253-9602-4885-a66b-91fa289c5e20"
      },
      "source": [
        "print(\"The accuracy of the classifier then would be : {} which is the number of good classification over the number of tuple to classify\".format(str((284315-492)/284315)))"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "The accuracy of the classifier then would be : 0.998269524998681 which is the number of good classification over the number of tuple to classify\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "FhSW0w4xJFe5"
      },
      "source": [
        "## Correlation of the features"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "lBfLyOp5JCM7"
      },
      "source": [
        "df_corr = df.corr() # Pearson, Standard Correlation Coefficient"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 361
        },
        "id": "S5KTMwZs_a4x",
        "outputId": "c09e5d58-d7ea-4e54-ae67-8047e809b046"
      },
      "source": [
        "plt.figure(figsize=(6,5))\r\n",
        "seaborn.heatmap(df_corr, cmap='Blues')\r\n",
        "seaborn.set(font_scale=2,style='white')\r\n",
        "\r\n",
        "plt.title('Heatmap correlation')\r\n",
        "plt.show()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x360 with 2 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "F-kZnAjzJT_Q"
      },
      "source": [
        "## Data Selection"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "KXTffmmFJWi6"
      },
      "source": [
        "### UNDERSAMPLING"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "MyvCn0GaJRev",
        "outputId": "3db8e774-1801-4927-eaba-ab7ab6eb697b"
      },
      "source": [
        "# We seperate ours data in two groups : a train dataset and a test dataset\r\n",
        "\r\n",
        "# First we build our train dataset\r\n",
        "df_train_all = df[0:150000] # We divide the original dataset in two parts\r\n",
        "df_train_1 = df_train_all[df_train_all['Class'] == 1]\r\n",
        "df_train_0 = df_train_all[df_train_all['Class'] == 0]\r\n",
        "print('In this dataset, we have {} frauds so we need to take a similar number of non-fraud'.format(len(df_train_1)))\r\n",
        "\r\n",
        "df_sample=df_train_0.sample(300)\r\n",
        "df_train = df_train_1.append(df_sample) # We gather the frauds with the no frauds. \r\n",
        "df_train = df_train.sample(frac=1) # Then we mix our dataset"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "In this dataset, we have 293 frauds so we need to take a similar number of non-fraud\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "_cZHwjooJbS3"
      },
      "source": [
        "X_train = df_train.drop(['Time', 'Class'],axis=1) # We drop the features Time (useless), and the Class (label)\r\n",
        "y_train = df_train['Class'] # We create our label\r\n",
        "X_train = np.asarray(X_train)\r\n",
        "y_train = np.asarray(y_train)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "z9V1UrCNJq1B"
      },
      "source": [
        "# with all the test dataset to see if the model learn correctly\r\n",
        "df_test_all = df[150000:]\r\n",
        "\r\n",
        "X_test_all = df_test_all.drop(['Time', 'Class'],axis=1)\r\n",
        "y_test_all = df_test_all['Class']\r\n",
        "X_test_all = np.asarray(X_test_all)\r\n",
        "y_test_all = np.asarray(y_test_all)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "n9GDJ_f2LnQr"
      },
      "source": [
        "## Fucntion for Confusion Matrix"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "WkMgQzYgLmJT"
      },
      "source": [
        "class_names=np.array(['0','1']) # Binary label, Class = 1 (fraud) and Class = 0 (no fraud)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "YI1TG1KTLsMy"
      },
      "source": [
        "# Function to plot the confusion Matrix\r\n",
        "def plot_confusion_matrix(cm, classes,\r\n",
        "                          title='Confusion matrix',\r\n",
        "                          cmap=plt.cm.Blues):\r\n",
        "    \r\n",
        "    plt.imshow(cm, interpolation='nearest', cmap=cmap)\r\n",
        "    plt.title(title)\r\n",
        "    plt.colorbar()\r\n",
        "    tick_marks = np.arange(len(classes))\r\n",
        "    plt.xticks(tick_marks, classes, rotation=45)\r\n",
        "    plt.yticks(tick_marks, classes)\r\n",
        "\r\n",
        "    fmt = 'd' \r\n",
        "    thresh = cm.max() / 2.\r\n",
        "    for i, j in itertools.product(range(cm.shape[0]), range(cm.shape[1])):\r\n",
        "        plt.text(j, i, format(cm[i, j], fmt),\r\n",
        "                 horizontalalignment=\"center\",\r\n",
        "                 color=\"white\" if cm[i, j] > thresh else \"black\")\r\n",
        "\r\n",
        "    plt.tight_layout()\r\n",
        "    plt.ylabel('True label')\r\n",
        "    plt.xlabel('Predicted label')"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "USKqVcfYLynU"
      },
      "source": [
        "# Applying Logistic Regression"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "oOjoQwP5JxV4"
      },
      "source": [
        "from sklearn.linear_model import LogisticRegression"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "RqPnj05zJ__s"
      },
      "source": [
        "classifier = LogisticRegression()"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "GCMUZIXoKCBH",
        "outputId": "b9564236-4240-42d4-d7ec-8f267d46fdb5"
      },
      "source": [
        "classifier.fit(X_train, y_train)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.7/dist-packages/sklearn/linear_model/_logistic.py:940: ConvergenceWarning: lbfgs failed to converge (status=1):\n",
            "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n",
            "\n",
            "Increase the number of iterations (max_iter) or scale the data as shown in:\n",
            "    https://scikit-learn.org/stable/modules/preprocessing.html\n",
            "Please also refer to the documentation for alternative solver options:\n",
            "    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n",
            "  extra_warning_msg=_LOGISTIC_SOLVER_CONVERGENCE_MSG)\n"
          ],
          "name": "stderr"
        },
        {
          "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='auto', n_jobs=None, penalty='l2',\n",
              "                   random_state=None, solver='lbfgs', tol=0.0001, verbose=0,\n",
              "                   warm_start=False)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 20
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "60eBaoFFKc4s",
        "outputId": "cd081abf-cbba-462e-ab9c-a8eafc72b78e"
      },
      "source": [
        "print(classifier.intercept_, classifier.coef_)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "[-2.86438049] [[ 0.00908324 -0.6603154  -0.16940042  0.88268394 -0.31792477 -0.1732796\n",
            "   0.84889956 -0.8684064  -0.3717664  -0.99396007  0.57272193 -0.94655916\n",
            "  -0.551261   -1.3298367  -0.05560556 -0.55778326 -0.81908262 -0.00268815\n",
            "  -0.03360937  0.03830921  0.20862332  0.54646126 -0.08992324 -0.62976972\n",
            "   0.07651314 -0.34431767  0.16293138  0.47385403 -0.00490961]]\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "N3TxL3vRMJtm"
      },
      "source": [
        "pred = classifier.predict(X_train)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "qDa_wGJKMQVo",
        "outputId": "67d12dbb-5819-4d73-a2fb-56165b61e854"
      },
      "source": [
        "print ('Accuracy from sk-learn: {}'.format(classifier.score(X_train,y_train)))"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Accuracy from sk-learn: 0.954468802698145\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "MFnroZFZOg5-"
      },
      "source": [
        "### Confusion Matrix"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 295
        },
        "id": "HPVqDF1xOWVS",
        "outputId": "67e689c9-7584-4313-934e-dfddf35d1137"
      },
      "source": [
        "cm = confusion_matrix(y_train, pred)\r\n",
        "plot_confusion_matrix(cm,class_names)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 2 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "-tjdq_yufDJk"
      },
      "source": [
        "### Precision, Recall, F1-Score, Mean Absolute Error, Mean Percentage Error and Mean Squared Error"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "flH3CxZEfY4h",
        "outputId": "8d377ef0-b1dc-488e-d27c-c8583609b9c1"
      },
      "source": [
        "from sklearn.metrics import classification_report,mean_absolute_error,mean_squared_error,r2_score\r\n",
        "report= classification_report(y_train,pred)\r\n",
        "print(report)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "              precision    recall  f1-score   support\n",
            "\n",
            "           0       0.93      0.98      0.96       300\n",
            "           1       0.98      0.92      0.95       293\n",
            "\n",
            "    accuracy                           0.95       593\n",
            "   macro avg       0.96      0.95      0.95       593\n",
            "weighted avg       0.96      0.95      0.95       593\n",
            "\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "F35NxcLXezD3",
        "outputId": "43fa8de8-1fa1-480a-eb76-f6b6cee6dcd8"
      },
      "source": [
        "mean_abs_error = mean_absolute_error(y_train,pred)\r\n",
        "mean_abs_percentage_error = np.mean(np.abs((y_train - pred) // y_train))\r\n",
        "mse= mean_squared_error(y_train,pred)\r\n",
        "r_squared_error = r2_score(y_train,pred)\r\n",
        "print(\"Mean absolute error : {} \\nMean Absolute Percentage error : {}\\nMean Squared Error : {}\\nR Squared Error: {}\".format(mean_abs_error,mean_abs_percentage_error,mse,r_squared_error))"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Mean absolute error : 0.045531197301854974 \n",
            "Mean Absolute Percentage error : 0.03709949409780776\n",
            "Mean Squared Error : 0.045531197301854974\n",
            "R Squared Error: 0.8178498293515358\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.7/dist-packages/ipykernel_launcher.py:2: RuntimeWarning: divide by zero encountered in floor_divide\n",
            "  \n"
          ],
          "name": "stderr"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "sxBLFUu0NUVv"
      },
      "source": [
        "# Undersampling using Synthetic Minority Oversampling Technique (SMOTE) approach"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "g7FzOW3cNQ2j",
        "outputId": "bbf8aa8e-d180-4ec2-cb82-61cea83a25bf"
      },
      "source": [
        "from imblearn.over_sampling import SMOTE\r\n",
        "oversample=SMOTE()\r\n",
        "X_train,y_train= oversample.fit_resample(X_train,y_train)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.7/dist-packages/sklearn/externals/six.py:31: FutureWarning: The module is deprecated in version 0.21 and will be removed in version 0.23 since we've dropped support for Python 2.7. Please rely on the official version of six (https://pypi.org/project/six/).\n",
            "  \"(https://pypi.org/project/six/).\", FutureWarning)\n",
            "/usr/local/lib/python3.7/dist-packages/sklearn/utils/deprecation.py:144: FutureWarning: The sklearn.neighbors.base module is  deprecated in version 0.22 and will be removed in version 0.24. The corresponding classes / functions should instead be imported from sklearn.neighbors. Anything that cannot be imported from sklearn.neighbors is now part of the private API.\n",
            "  warnings.warn(message, FutureWarning)\n",
            "/usr/local/lib/python3.7/dist-packages/sklearn/utils/deprecation.py:87: FutureWarning: Function safe_indexing is deprecated; safe_indexing is deprecated in version 0.22 and will be removed in version 0.24.\n",
            "  warnings.warn(msg, category=FutureWarning)\n"
          ],
          "name": "stderr"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "gBpqN-YwPskO"
      },
      "source": [
        "## Applying Logistic Regression"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "O2dzMd4INxwV",
        "outputId": "5e680c8a-d989-4fda-8a82-517ca35330a6"
      },
      "source": [
        "classifier.fit(X_train, y_train)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.7/dist-packages/sklearn/linear_model/_logistic.py:940: ConvergenceWarning: lbfgs failed to converge (status=1):\n",
            "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n",
            "\n",
            "Increase the number of iterations (max_iter) or scale the data as shown in:\n",
            "    https://scikit-learn.org/stable/modules/preprocessing.html\n",
            "Please also refer to the documentation for alternative solver options:\n",
            "    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n",
            "  extra_warning_msg=_LOGISTIC_SOLVER_CONVERGENCE_MSG)\n"
          ],
          "name": "stderr"
        },
        {
          "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='auto', n_jobs=None, penalty='l2',\n",
              "                   random_state=None, solver='lbfgs', tol=0.0001, verbose=0,\n",
              "                   warm_start=False)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 28
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "K9sYkgORN0i5",
        "outputId": "4ebfae8c-2164-473e-fda2-eccf426f283f"
      },
      "source": [
        "print(classifier.intercept_, classifier.coef_)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "[-3.04955805] [[ 0.09881579 -0.61843021 -0.08189188  0.98817601 -0.34140006 -0.14366433\n",
            "   0.79005715 -0.78762421 -0.40296671 -0.99835478  0.58143513 -1.0343642\n",
            "  -0.49650436 -1.44568553  0.01833729 -0.58885464 -0.74998263 -0.06751787\n",
            "  -0.00345061 -0.14034109  0.24413152  0.51893844 -0.16940419 -0.82928158\n",
            "   0.17574159 -0.36855749  0.23615798  0.65471445 -0.00394447]]\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "5r8iugPMN3j1"
      },
      "source": [
        "pred = classifier.predict(X_train)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "FHajD2e4N5bj",
        "outputId": "36a3a254-e3e3-4ed9-deb3-7b43058e13c4"
      },
      "source": [
        "print ('Accuracy from sk-learn after applying SMOTE: {}'.format(classifier.score(X_train,y_train)))"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Accuracy from sk-learn after applying SMOTE: 0.9583333333333334\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "3JAvj1DsPhXl"
      },
      "source": [
        "## Confusion Matrix"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 295
        },
        "id": "5LcLpjwYOGK0",
        "outputId": "d6b3a1c4-fb6c-426f-afd9-46b8b2eb25a9"
      },
      "source": [
        "cm = confusion_matrix(y_train, pred)\r\n",
        "plot_confusion_matrix(cm,class_names)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 2 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "BmM3YTyDe6pf"
      },
      "source": [
        "### Precision, Recall, F1-Score, Mean Absolute Error, Mean Percentage Error and Mean Squared Error"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "5mltXYZbfINm",
        "outputId": "b6187440-0ef4-4f2c-dac4-031943930fe6"
      },
      "source": [
        "report= classification_report(y_train,pred)\r\n",
        "print(report)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "              precision    recall  f1-score   support\n",
            "\n",
            "           0       0.94      0.98      0.96       300\n",
            "           1       0.98      0.93      0.96       300\n",
            "\n",
            "    accuracy                           0.96       600\n",
            "   macro avg       0.96      0.96      0.96       600\n",
            "weighted avg       0.96      0.96      0.96       600\n",
            "\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "FNrGpGVDcypu",
        "outputId": "636557aa-4303-4d51-b7de-bcfc3158523b"
      },
      "source": [
        "mean_abs_error = mean_absolute_error(y_train,pred)\r\n",
        "mean_abs_percentage_error = np.mean(np.abs((y_train - pred) // y_train))\r\n",
        "mse= mean_squared_error(y_train,pred)\r\n",
        "r_squared_error = r2_score(y_train,pred)\r\n",
        "print(\"Mean absolute error : {} \\nMean Absolute Percentage error : {}\\nMean Squared Error : {}\\nR Squared Error: {}\".format(mean_abs_error,mean_abs_percentage_error,mse,r_squared_error))"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Mean absolute error : 0.041666666666666664 \n",
            "Mean Absolute Percentage error : 0.03333333333333333\n",
            "Mean Squared Error : 0.041666666666666664\n",
            "R Squared Error: 0.8333333333333334\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.7/dist-packages/ipykernel_launcher.py:2: RuntimeWarning: divide by zero encountered in floor_divide\n",
            "  \n"
          ],
          "name": "stderr"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "cuMCBBpHWQ1j"
      },
      "source": [
        "## Testing Error"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "6WmbBA7lVc-K"
      },
      "source": [
        "pred = classifier.predict(X_test_all)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "2Z4XsEv0WbXE",
        "outputId": "9927b673-0080-43e0-f223-e5d19e74a6e9"
      },
      "source": [
        "print ('Accuracy from sk-learn: {}'.format(classifier.score(X_test_all,y_test_all)))"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Accuracy from sk-learn: 0.952710170836826\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "ajijv1x1Wkpg"
      },
      "source": [
        "### Confusion Matrix"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 295
        },
        "id": "O5EarmxFWfup",
        "outputId": "b8ec91f2-f1d7-4bc6-f177-cecbf9ebce3c"
      },
      "source": [
        "cm = confusion_matrix(y_test_all, pred)\r\n",
        "plot_confusion_matrix(cm,class_names)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 2 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "lFKJJKWSWy0-"
      },
      "source": [
        "### Precision, Recall, F1-Score, Mean Absolute Error, Mean Percentage Error and Mean Squared Error"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "pMty-guWWonB",
        "outputId": "b4660a98-51b8-4599-fef6-ca35a3c54f34"
      },
      "source": [
        "report= classification_report(y_test_all,pred)\r\n",
        "print(report)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "              precision    recall  f1-score   support\n",
            "\n",
            "           0       1.00      0.95      0.98    134608\n",
            "           1       0.03      0.92      0.05       199\n",
            "\n",
            "    accuracy                           0.95    134807\n",
            "   macro avg       0.51      0.94      0.52    134807\n",
            "weighted avg       1.00      0.95      0.97    134807\n",
            "\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "wiPrrqrLW2Gw",
        "outputId": "40a8c851-2e9a-4197-b3f1-00d7d6284fb2"
      },
      "source": [
        "mean_abs_error = mean_absolute_error(y_test_all,pred)\r\n",
        "mean_abs_percentage_error = np.mean(np.abs((y_test_all - pred) // y_test_all))\r\n",
        "mse= mean_squared_error(y_test_all,pred)\r\n",
        "r_squared_error = r2_score(y_test_all,pred)\r\n",
        "print(\"Mean absolute error : {} \\nMean Absolute Percentage error : {}\\nMean Squared Error : {}\\nR Squared Error: {}\".format(mean_abs_error,mean_abs_percentage_error,mse,np.abs(r_squared_error)/100))"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Mean absolute error : 0.04728982916317402 \n",
            "Mean Absolute Percentage error : 0.0001186881986840446\n",
            "Mean Squared Error : 0.04728982916317402\n",
            "R Squared Error: 0.310825356202742\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.7/dist-packages/ipykernel_launcher.py:2: RuntimeWarning: divide by zero encountered in floor_divide\n",
            "  \n"
          ],
          "name": "stderr"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "hrDRwSbaDc2m"
      },
      "source": [
        "## Hyperparameter Tuning"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "mudvXm6oDcko"
      },
      "source": [
        "classifier_b = LogisticRegression(class_weight={0:0.6,1:0.4})"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Bx0qQ-HgFHF6",
        "outputId": "2376d84e-5b9e-4698-8488-b297ce4bfbe9"
      },
      "source": [
        "classifier_b.fit(X_train,y_train)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.7/dist-packages/sklearn/linear_model/_logistic.py:940: ConvergenceWarning: lbfgs failed to converge (status=1):\n",
            "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n",
            "\n",
            "Increase the number of iterations (max_iter) or scale the data as shown in:\n",
            "    https://scikit-learn.org/stable/modules/preprocessing.html\n",
            "Please also refer to the documentation for alternative solver options:\n",
            "    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n",
            "  extra_warning_msg=_LOGISTIC_SOLVER_CONVERGENCE_MSG)\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "LogisticRegression(C=1.0, class_weight={0: 0.6, 1: 0.4}, dual=False,\n",
              "                   fit_intercept=True, intercept_scaling=1, l1_ratio=None,\n",
              "                   max_iter=100, multi_class='auto', n_jobs=None, penalty='l2',\n",
              "                   random_state=None, solver='lbfgs', tol=0.0001, verbose=0,\n",
              "                   warm_start=False)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 41
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "OCZwxaJUFcM9"
      },
      "source": [
        "pred_b = classifier_b.predict(X_test_all)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "-JlrmHgBGrZZ",
        "outputId": "a3b40f67-e3f3-4a0c-fcce-3bf2e6c6e86f"
      },
      "source": [
        "print(classifier_b.intercept_, classifier_b.coef_)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "[-3.45739348] [[ 4.35048957e-03 -2.51664252e-01 -3.61740218e-02  8.26839368e-01\n",
            "  -2.50220585e-01 -2.74290597e-01  6.15419368e-01 -6.34898774e-01\n",
            "  -1.82517507e-01 -6.05270468e-01  4.67967748e-01 -7.59020169e-01\n",
            "  -4.98490194e-01 -1.12792340e+00 -9.47965834e-04 -3.66086059e-01\n",
            "  -4.24973567e-01 -5.02473317e-02  1.24269131e-02 -1.91698161e-01\n",
            "   2.03490667e-01  3.39239798e-01 -2.17260243e-01 -7.51611739e-01\n",
            "   2.98256733e-01 -2.80065468e-01  1.67962169e-01  6.57350969e-01\n",
            "  -1.26442642e-03]]\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "NiOoeUcUFhsA",
        "outputId": "54b12f63-7011-469b-e5ec-1c4502ba3334"
      },
      "source": [
        "print ('Accuracy from sk-learn after hyperpaarameter tuning: {}'.format(classifier_b.score(X_test_all,y_test_all)))"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Accuracy from sk-learn after hyperpaarameter tuning: 0.9706024167884457\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "N3Cmlf5GHYuP"
      },
      "source": [
        "### Confusion Matrix"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 295
        },
        "id": "USNi1vmrHW1q",
        "outputId": "cd21d898-1a3e-4fca-d7e0-bd634d77e043"
      },
      "source": [
        "cm = confusion_matrix(y_test_all, pred_b)\r\n",
        "plot_confusion_matrix(cm,class_names)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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KlWPu3LncunWL6dOnA/8uc5o7MVepUkU93E5l0aJFjBw5kqtXr/L+++9jZWWFubm5Os7JyYlvvvmmxNtfBw4cyN69ewkNDWX27NnMnTsXa2trFAoFzs7OfP7550yYMEErLisriyNHjqgnctjb2yNJksZ1+OCDD/QOW3tes2bN+OSTT5g/fz6HDx/myJEjODo6olAo1ImoT58+/5lxu56engwfPpyNGzeyZcsWtmzZon69OTk5tGvXDh8fH70dsgMHDuTixYts2rSJH374gQoVKiCTyejevbtBE43y8s033xAeHq5+H+Tm4eHBtGnTWLBgAUuWLKFt27bqjnfBdJl8ogblxIk2bdqwZ88eTp8+zfXr19U3DqhSpQo+Pj5069aNbt26aa3rUL58eXbs2MHWrVv55ZdfuH37NpmZmdSsWZOXXnqJ0aNHG7xoUFGwsLBg/fr1rFy5ksOHD/PgwQNsbGzo1q2bzgStMmLECKpXr8758+eJjIwkISGBZ8+e4e7uTtOmTRkyZAi+vr4GleWtt96iYcOGrF+/nqCgIB4/fqweP/3GG2+om5D+K2bNmkWtWrXYvn07N2/eJDs7m3r16vHqq68ydOhQVq5cqTd2wIAB5OTksHPnTm7evMn9+/fVQzuLQkhICGvXrgWUbdG6hpYOGTKEkydPcvbsWaZNm8aOHTsKNNtRMF4mvXqeIAhCWWDSbdSCIAhlgUjUgiAIRk4kakEQBCMnErUgCIKR+0+M+iiI9PR0rly5gqurq1hZTDBJ2dnZJCQk4OPjU6C7weiTlJSU70xee3v7PMeLCyWrzCTqK1euqCcGCIIp27p1q8FDLFWSkpLwbdkWM/Je0dHJyYmjR4+KZG0kykyiVi13GWfpS7a88LURIW8Xds8r7SL8Z8XHx/HO20P1Lt1aEAqFAjOyiLf2I0um++/AXEqH5CD15Cqh9JWZRK1q7siWW5Mttynl0vx3Va5SpbSL8J9XFE13WWa2ZMv1LDKWI7qujE2ZSdSCIOQikykf+vYJRkUkakEoi2RmINdTM5dEZ7uxEYlaEMoimQxkepo4RI3a6IhELQhlkVzUqE2JSNSCUBaJNmqTIhK1IJRFokZtUkSiFoSySLRRmxSRqAWhTJLrT9RiCSCjIxK1IJRFZmbKhy6i6cPoiEQtCGWR6Ew0KSJRC0JZJJfr70yUi6YPYyMStSCURbI82qj1tl0LpUUkakEoi/KaQi4TbdTGRiRqQSiLZOTRRl2iJREKQCRqQSiL8prwom+7UGpEohaEski0UZsUkagFoSwSidqkiEQtCGWRLI/heSJRGx2RqAWhLBITXkyKSNSCUBaJzkSTIhK1IJRBMpkMmZ6as77tQukRiVoQyiBlX6KeRC2aqI2OSNSCUAaJGrVpEYlaEMoguUyOpGfxJbmoUhudMpeo7W2tadeyIc0bVKdZ/eo0r18Dl3L2ADR+7VP+vhOvM87J3oYhffzwbVADnzpVqFjBgXKOtjxNe8aNO/H8cuYK3+44zRNFep7P36mlNxMGdaRFw5o42llz70ESh367wqLvjvLgUUq+5a9ToyLj3nqJLq3qUqWSM1lZOdxPSObPy7fZcuBPzv51U2+sk70N4wa9RO+OjfCs6oKVhTnxiU+4fD2GA6fD2LL/T43jLczNGN6vNb4NatDIuypuLo5UcLYjPSOTyOiHHD9/lZXbfyXu4ZN8y11QIX9d4OCBfYT8FcytyEgSHyaQnp5OhQouNG3enCH+I+jdt5/e+OA//yDw62X88fs5EhMf4lyuHM19W/DOuIl07vKyQWU5sG8vQ94coP49OS1b53FfLJzPl58tyPNc9eo34I+/Lhv0/MVJ1KhNS5lL1B1b1uOHZWMNjvP2qMSS6QPVvz/LzOJp2jPKOdri18gDv0YejH2jPX3GBxIReV/nOaaP6s78iX0AyM7OQZGagWc1VyYM7sTAHr70fGeF3liA8YNe4vMP+mFlaQFAytN0LC3MqOvpRl1PN3IkSW+ibtusFlsXjaJSBUcA0jMyyXiWhUdVFzyquuBTp4pWoi7vZMvXc95S/56Vlc2Tp+k4O9jQtF41mtarxpiB7Xjzw7WcuXCjAFcxf99v/I4N69aof7e3t0cul3P//j3uH7jHwQP76duvP+u/34aFhYVGbMDiRcyfOxtJkpDJZDg5O5P48CGHfjnAoV8OMGX6LObOX1igcigUCqZ/+L5BZbe2tsbRyUnnvgoVXAw6V7GToX9ND5GnjU6ZS9QA8YlPCImI4q/wKO49SGLl3MH5xiQmP+V/649yNuQmFyOiSHisAMDayoLeLzVk0dQBVK7ozPbFo2k6YCE5OZJGfPd29dVJetn3J/hs9UEUqRnU83Rj/WfDaVK3Gj8GvEPTAZ/xLDNL6/lHDWjLkukDyc7OYfGGo6zZ+RvRcY8BqFTBgc6t6mJhrntYVZO6Vdn79Xjsba3Y/+tlPl99kEvXYgBwtLfGr6EHrZt4asWlZ2Tx9ZaTnA25SfCVu8Q9fIIkSViYm9GppTdffdifup5ubF00Cp++80lWpOV7HfPj17IVXl51adOuPbXreGFvr/y2ExMdzepV37AiYDH79u4mYPFXTJ/1kTru4IF9zPt4FgD+I0byyfzPcK1YkZSUFFZ9s5zPP53HkkVfUL9+A15/c1C+5fhs/lxiY2PwbdGSC8F/5ns8QP/X32DV2g2FeNUlTyaXI9fT9CET61EbnTKXqA+evsTPp6+pf6/uXr5AcZFRCcz9ep/W9vSMTH46GsLDJAWHVk/Cq2YlWjf25NzFSI3j5k/sC8DPJy4xK2CPevvVW3G8/v5qLu7+CM9qrowa0JZVP5zWiK3uXp6vpvQH4L3PfmDDnt819scnprD9l2Cd5ZbLZayePxR7Wyu2/xLEyI++19j/RJHO8fNXOX7+qlZssiKN6Ut2a23PzMrm6LkIbtx9QMT+ebiUs6fXSz56y2CIwUOH69xetVo1Pv38K+Lj7rNj+1a2bt6kkag/XzgfAL+Wrflm1Vr1dgcHB6bP+oi7d26z5fuNzPt4Nv0GDMTcXP9b/9LFEFav+oYmzZozYtToAidqUyKaPkxLmfvofL6mW1T+Co9S/+zuqvn1t56nG429qwIQ8P0JrdjYB0nsPPwXAG/19NXaP3FwR+xsrAi6fFsrSeenVwcfGnlVJTXtGVMW/WRQbH5uxzzkUfJTACq76v7KX9SaNW8BQNz9e+ptcffvExZ6CYBxEyfpjJswaTIA0dFRnP3ttM5jAHJycvhg4jgkSWLp8kC9tU5Tp7wJuUz3Q+Rpo/PffBeWglaNPdQ/34lN1Nj3UgsvAJJSUgkKu6MzXlWj9fWpgZ2Npca+N/5J3qpkboi3erZQn//xk1SD4/PiVbMS5Z3sAO3XXFz+/EP5QVWj5r/XOyb63w/JOl7eOuNq1a6jTrqnThzXe/41qwK5GHKBEaPG0Ny3RVEU2SipatT6HoJx0fv9b+/evUX2JP366e+lN2VmZnLcKjjyctt66qaN4LA7XAi/q3FcXU83AK7fjkeSdNfor95SdiLK5XK8PdwIiVAmH4+qLuoOwEvXo/FrWJPpo7rTqrEnttYWRN1/xMEzVwjYdFzdbp5by0Ye6tjKrk7MHtuLnu0bUMHZjoRHCs5cuMHSTccJv3lPK1YXmUxGpQoOtGtWm3kTlG3uUfcf8cuZKwWKLwyFQsGd27fY8N0adv+0E4Ax707QKJNKdrbukRk5OTnqa3/tarjOY+7FxrJw/se4uLoyd/5nBpfz9KmTNPXxJiY6Cmtrazxq1aZb95688+4EKlaqZPD5ipNcLtd7b8T/6rcIU6Y3Uc+cObPIPln/a4n6l28n0rllXa3tvwZdZ/isjVrb3V2UzQL3E5L1njP3PjcXR/XPtau7qn/u4FuH2WN6Ym5uxhNFGpIE3h5ueHu48VavFrzy7tdcvRWnPt7K0pyqbuUAKOdgyx87ZuJazoH0jEzSMjKp6laOwb39eL17M0Z/vJkfj+ivsa+cO5i3X2ujtf3StWiGzlhPekam3tjCiI2JoX6dGlrbra2tmTpjNmPGjlNvq1qtuvrn69ciaNK0mVbc9WtX1Yk6Li5Oaz/AtA8nkZKSwldLllGuXDnDyxwbg5mZGQ6OjjxJTib0YgihF0NYt2YVG7f8QMdOXQw+Z7EpwlEft27d4rfffiMsLIwrV65w584dJEli+fLl9OjRI8/Y/fv3s337dq5fv05OTg4eHh4MGDCAQYMG5fmBcebMGTZu3MiVK1fIyMigWrVqvPLKK4waNQpLS0u9caGhoaxZs4aQkBAUCgXu7u507dqVcePG4eDgkOdrXLlyJX/88QdJSUm4urrSoUMHJkyYQMWKFfXGxcfHs3LlSs6cOUNCQgLOzs60bt2a8ePH4+HhoTfueXoTdYsW/92vfS/qcXIqcQ+fYG1ljrODLQCn/rzOjKW7dY6Ftv2nKSMt/Znec6bm2mdva6X+WXV+gDnv9OLvu/GM/WQLwVfuIpPJeLlNPdbMH4q7qxPbF4+m+cDPyc7O+SfWRh07flBHFKkZ+M9Yz54Tl8jOzqF+LXdWzR2MXyMPVs8bwsWr0dyMeqCzfE8UacQ9fIKlhZm6uePStWimfPUjkVEJ+V4zQ5mZmalroUmPH/Ps2TPMzc35cNpMRo8dr3FsJTc3fBo24krYZb5eHsDANwdr/ZEvW7JI/bMiRfv/6NAv+zmwby+t27TT26GpT61atVn4xf/o1acvNWrUxNzcnKdPn3L44AE+mjmNe/diGfJGf07/HkztOl4Gnbu4FGVn4vbt2/n+++/zP/A58+fPZ9u2bVhZWdG6dWvMzc05f/48CxYs4Pz586xYsUJnsl67di2LFy/GzMwMPz8/HB0dCQ4OZtmyZfz6669s3LgRGxsbrbgDBw4wffp0srOzadasGZUqVSI0NJTvvvuO48ePs337dipUqKAVFxQUxJgxY0hPT6dBgwa0aNGCa9eu8cMPP3D06FG2bdumM+lGRkYyePBgkpKS8PT05OWXX+bOnTvs27ePY8eO8d1339G8efMCXSu9iXrz5s0FOkFZNHTGevXP5RxtGdCtGXPHv8L5bTOYsWQ3gdt/LbLnkuf6o5EkiTc/XMuNuw/Uvx89F8G787ay5+txeHu40a9zY3Ydu6iMzfUmNzOTM2Ppbn46GqLeFhF5n4GT13Bl3yc42FkzcUhHPvhip85yzFy6h5lLlaNVHOys6dGuAZ9O6suJDR+y7PsTGiNZioKbuzs37iibY3JycrgVeZOAJYv4/NN5bN64nh/3HqBe/Qbq46fNnMPwIW8SFnoJ/7de56N5n1K7jhf3793jm+VL2bPrRywsLMjMzNT643/69CnTJk/C3NycJcu/MThRvTFoiNY2Ozs7Bgx8E7+WrenQxpdHiYl8sXA+323aWoirUfSKcniel5cXo0aNwsfHBx8fH+bMmUNQUFCeMUeOHGHbtm24urqyZcsWatasCcDDhw8ZNmwYx44dY/PmzQwfrvmhGRYWxpIlS7CxsWHTpk00btwYUP4fjh07luDgYAICApg9e7ZGXFxcHHPmzEGSJAIDA+natSsAWVlZTJs2jYMHDzJ37lwCAwM14lJTU/nwww9JT0/n448/ZujQoep9X331FevXr2fKlCns2rVL432Tk5PD5MmTSUpKYuTIkcyYMUO9b/PmzSxcuJAPPviAo0eP6vxQeZ7RN0bt37+fwYMH07x5c5o2bUr//v3ZunUrOTk5pV00AB4/SWXdT2fpMy4QSZJYNLU/TepW1TgmNU1ZW7ax1v+VzDbXPkVqxr8/p/3789Hfr6qTdG6Hz4arZ1R29Pu3My33eZJSUtm87w+t2AePUthx6AIAnfx0d8Q9L+VpOj8e+YvOI5aSnJLGB8O60LdTowLFFoZcLqd2HS8Cv13HxEmTiY6O4p1RwzXeA/36v86sjz4B4MD+n2nVvBEujtY0rOvJ6lXf0LVbd7r36AWAk5Ozxvk/X/AJ0dFRvDv+PRr4NCzSslerXp0x/3wDOHr4oNG8b2Xk0ZloYNvHwIEDmT59Or169aJ69er5BwCrV68GYOrUqeokDeDi4sK8efMAZc35+eu1du1aJEli9OjR6iQNyg/GL774ArlczoJhYmQAACAASURBVLZt23jyRHO27KZNm0hPT6dfv37qJA1gbm7Op59+ir29PcePH+fmTc0JY7t37yYhIYGWLVtqJGlV2atXr054eDhnzpzR2Hf69GmuX79OjRo1mDp1qsY+f39//Pz8ePDgAbt3aw9/1cWoE/X8+fOZOnUqV65cwdfXlzZt2nDnzh0WLFjApEmTjOZNDxB6PYbfL91CLpcz7NXWGvvu/dP+/Pywvdxy78s9JTt32/WNu7qntwP8/U8CV7VJgzKhpjxVTmm/Ff1Q79BEVfKvWsmwdtl7CcnsOxUKoPWai8s74ycCcPnSRUIvXdTYN3POXI6dOsugIf7UrVefatWq07ZdBwK+XsmPew6QlJQEQK3atdUxkZE3WRW4AteKFXnvgykoFAqNR0ZGrg/Nf7Y9e6a/CUsX3xZ+ADx58oRHiSUzOiY/qtXzdD+K97nj4uIIDw/HwsJCZxu2n58flSpVIiEhgUuXLqm3P3v2TJ0Q+/btqxVXrVo1mjRpQmZmJqdPaw7BPH78uN44e3t7OnXqpHHc83F9+vTRijMzM6NXr155xvXq1QszM+2JaKpynDihPVxXl0JNeJEkiWPHjnHu3Dni4uJIT09n06ZN6v2pqamEh4cjk8nw9dUeF1wQhf1qVJruPVAmAo+qmtOFr/3TweftUQmZTKZz5Ec9T3dA+ZXpWq4Owau37pOdnYOZWcH+ep4/99XI+/g1KlinhYThY8zvPVB+kHhWK5kp0pUrV1H/fPtWJE2babbx+bVqjV8r7Q+N7OxsrlxRrrXRomUr9fZ7sTFkZ2eT8OAB3p5VteJyq/LPh+nMOXPVtXdTJcvjDi/FPTwvIiICgDp16mBtba3zmIYNGxIfH8/Vq1dp1kzZOXz79m3S0tJwdnbWW3Nv2LAhISEhREREqJOrQqEgKipKvV9f3P79+9VlU7l69Wq+cblf04vG6WPwZ+edO3fo27cv77//Pjt27OD06dNa7VFWVlbMmTMHf39/Lly4YOhTAIX/alSaalZRdkQ8zdXkAHD6wt+AsmPQt4HuN1iXVspRJMFX7mp0LKalZ/Ln5dsA1Kmhf4iXVw1lz/Pde480tp/88zqgTKRyPesPe9WspDO2IPS95uJy985t9c+q6eUFcfzoYZIeP8bS0pJ+r71eHEXT60Kw8u/DwcGB8jo6q0qD/tq0TL1OdVxcHDExMRqP55sUCiMmRrl8QeXKlfUe4+7urnFs7p9V+3RRnTM2NlYrztHRUe97RhWX+/kUCoX6W1iVKlUKHJf7d31xqtfw+PFjnj59qvf1qBiUqJOTk3n77be5ceMGXl5eTJo0SecLNzMzY9CgQcrOrqNHDXkKoPBfjYpTfjXatk1r4dewJoDW9PFrt+IIva78j5s8vOvzobi7OvFGD2XN8IeD2tOwtx5Q/qF3a1OPOjW0hwL1aNdAnWyPnNUcI/zDoWCys3NwdrBl2KuttGIrlnfgzX8m1Dwfm99rrlXdlT4dlW3Tz7/mwsjOztY7zlxlecBiQNm22KJlwZpbkpOT+Xi2sjPHf8RIXFz/HfLYvkNHktOy9T5Wrvnu3/P8sy13bTq/8sbGxLB29UoAunbrYTRjlAsy4WXIkCF06dJF45H7m3NhpaYqJ17l1YlmZ6ccWZQ7iRUkztbWtsjicv+sL1ZXXEGeUxWnK1YXg94169ev5/79+7Rr145du3Yxfvx4vV9dOnfuDMDFixd17s9LQb8awb9fMQxRwdlO/Sjn+O8Fc3aw0diX+yvgtkWjmDehD03qVsXc/N/L5lrOngmDOrJr+bvI5XKi7z/i+5/Paz3nJ98o1wl5rWtTPnv/VfUQvLqebvy0bCyO9jbcik5g/W7tKeKbfj5PROR9zM3N+GHJGHwbKMcXq4bnrfpEOergz8u3Ofxcsr1+O56Ne5Xl+XJyfwa83FSdgOt5urEz4B3sba14lPyUr7ee0ohdMv11lkx/nVaNPbCy/LeVzMnehqF9WnJs3QfY2ljyRJHG11s0YwsjJiaal9r6sXnTemJz1VBycnK4HHqJ0SOG8v0GZeIcO26ixljnB/HxzJ0zk4shf5GermyXz8zM5Ojhg3Tv3J7r167iWas28z794oXLmdu5s2d4rU8Pftr5A/G5xmenpqay+6eddO/cnkeJidja2hpVc4lMphz1oesh+6eReuvWrZw4cULjYUxNjWWJQW3UJ0+eRCaTMXPmzDwXtQGoUaMGFhYW6rYhQxT2q1GBz3/qK53bT3+v2Tvr3WsuUfeVzQHOjrbMGN2dGaO7k5WVTbIiHXMzOU65xirfuPuA1z9YzdM07c6mI2cjmBe4n3kT+vDhiJeZNLQzT9OeqeMTHqcwcPIanSvnZWfnMOD9bzm69n3q13Lnty3TeKJIw8xMjp2NMuFHRN5n8NR1Ol/XlEU/4lnVhU4tvdmyaBRp6c/IyMxSj9FOSkll0NR1WhNybK0t8e/bivGDOpKdnUOyIg2ZTKbx4XY/IZmh078j9p/2+RcVejGEie+OAZSTW+zs7VGkpGh06g3xH86CzzX/D9PS01i+9H8sX/o/9RKnipQUsrKU17Nho8bs3L0fR0dHipIkSZw8foyTx48BypqStY0NyUlJ6lmS5StUYN3GLXjXrVekz/0i8mqjVm13c3OjatW82+0LQ1WbTEvTv9qiqpapqlkXNE5Vky2KuNw/p6Wl6ZwQoytO9ZzJycl6n1MVpytWF4MSdUxMDFZWVtTO1WueFzs7OxQK7WnN+SnsV6PiNCtgDz3b+9DBtw41KpfHtZwDcrmM2PjHXP47ln2nQtn+SzAZz7QTrcpX644QFHaHiYM70aJhDRxsrbkVnVCgGwfciU3E943PmTysK307N6Zm5QrkSBIhEVHsOX6RldtPa7Rt55bxLItXxn3DyP5tGNqnJXU93bC2tOBm1AOOnosgYNNxYuK1E+3iDce4fjuOl1p4U6uaCxUrOGJhLifu4RMiIu9x+LdwNv18Pt+bJRSUu3tlNmzezulfT/LXhWDi4+7zKDFROR3bsxYtWrZiqP8IWrVpqxXr4uLK7I/ncebXU9y88bf6pgH1GzSk/+sD8R8+Mt/KRWE0aNCQBZ99xZ/nz3H1agSJiQ95kpyMo5MTXl51ebl7D94e9Y5Gc4tRKMX1qFXttvfu6V+2QDV7NHcbr+rn+/f1r9mu2qcr7smTJygUCp3Ntaq43B9M9vb2ODk5kZycTGxsLHXras9G1vV8qt8LEufs7Fz0iRoocOddVlYWCoWiQIUoaTZNJxocExIRRUhEFJ+tfrHnPvXndU7908FnqCeKdOavPMD8lQcMjpUkie92neO7XecKHPP3nXiWbIxnyUb9ixgVJUtLS/q//gb9X3/D4Fg7OztmzP6YGbM/LtIyDfEfwRD/EXr3l69Qgfc/nApM1XuMMZLLZXrX+kBPp3NRqV+/PgA3btwgPT1dZ/NmWFgYAPXq/fstxNPTE2tra5KSkoiKitI58uPy5ctacQ4ODlSvXp2oqCjCwsJo3Vq7b0NXnKqs58+fJywsTGfCVcWpXlPuuIiICMLCwujSRXvpANXrez5OH4PaqKtWrUpmZibR0dH5Hnv+/HmysrKoVauWIU8BFP6rkSAIBaNq+dD3KE7u7u40aNCAzMxMDh8+rLU/KCiIuLg4XF1dadq0qXq7paUlHTp0AGDfPu214aOjo7l06RIWFhZ07NhRY58qWeqKUygUnDql7GN5+eWXdcbt379fKy47O5uDBw/mGXfw4EGdC4WpypF78k1eDErUHTt2RJIkNm7cmOdxqampLFq0CJlMpvPTJD+F/WokCELByOWyPB/F7Z133gFg8eLF3L3772qTiYmJzJ+vvAnEmDFjtEbJjBkzBplMxrp169S1WVBW3GbPnk1OTg6DBw/W6osYPnw41tbW7N27V2OSSVZWFnPnzkWhUNC1a1etZt3+/fvj6urKn3/+ydatmtP/Fy9eTFRUFPXr11d/gKh07NgRb29v7t69y5IlSzT2bdmyhaCgICpWrEj//v0LdL0Mavp4++232blzJ9u2bcPe3p4RI0Zo7FcoFPz222+sWLGC27dvU6lSJQYNyv+2R88r7FcjQRAKRkZeVWfDEnV4eLg6uQLqadgBAQGsX//vujg7d/67jkyPHj0YNGgQ27dvp0+fPrRp00a9KJMqaT4/ZRugUaNGTJkyhcWLF/PWW2/RqlUrHBwcCA4OJjExkcaNGzN58mStOHd3dz777DOmT5/OhAkTaN68ORUrViQ0NJTY2Fhq1KjBggXaNyi2s7Nj6dKljBkzhgULFrBr1y5q1qzJtWvXiIyMpFy5cixZskRrkpBcLmfp0qUMGTKE7777jl9//ZW6dety584dwsPDsba2JiAgoEDrfICBibp8+fIEBgYybtw41qxZw7p169TjSP38/FAoFEiShCRJODk5ERgYqDFesKBUX43Cw8M5fPiw1jKp+r4aCYJQMHIzGTIz3QlZMpNhyDQyhUJBaGio1vY7d+7kGTdv3jyaN2/O1q1bCQoKIicnB09Pz3yXOR0zZgze3t5s2LCBsLAw9TKn/v7+eS5z2rt3b6pVq8bq1asJCQkhNDQUd3d3Ro0alecyp35+fuzZs4fAwED++OMP/v77b1xcXHjzzTeZOHGi3mVOa9euzb59+wgMDOTMmTMcPXoUZ2dn+vTpw4QJEwxa5lQm5TdiX4d79+6xdOlSjhw5Qmam5jrE5ubmdOvWjSlTprxQs8Thw4d5//33cXV1ZevWrdSooRw7nJiYyLBhw7h58yazZ88u8LjOmJgYunTpQqx1O7LlBfsUEwwXf35FaRfhP+tebCyv9urKiRMnCj1sTvV3YNZzHjI73bMkpaeJZB+a90LPIxStQo1Xqly5MosXL2bhwoVcuXKFhIQEJEmiQoUK+Pj4FEkHX2G/GgmCkD95rqniz5PkMnTfJ0coLS80sNTa2rrQiy4VRGG/GgmCkLc8740o7plodIp+BkAR69Onj84lBgVBeBH6E7VU3DNeBIMVOlGHhIRw5MgRIiIiePRIOc26fPny1K9fnx49eohOPkEwYnkOwyuB4XmCYQxO1A8fPmTGjBn8/rty8aDcfZGRkZFcuHCB77//nrZt2/Lll1/i4lIy6xQLglBwBVjqQzAiBiVqhULBkCFDiIqKQpIkmjZtip+fn3p4yoMHDwgODiYkJIRz584xdOhQfvrpJ4PWDRYEofjJ5eRRoy7Zsgj5MyhRBwYGcvfuXcqXL09AQAAtW7bUeVxwcDDvv/8+d+/eZdWqVUybNq1ICisIQtEoyruQC8XPoM/Oo0ePIpPJWLhwod4kDdCiRQsWLlyIJEkcOXLkhQspCELRksn0Tx8Xidr4GFSjTkhIwMrKSn1TgLx06tQJa2trHjzQvmu2IAilS7RRmxaDp5CnpOhfMzk35Se2HGdn50IVTBCE4pPXqA9JjPowOgY1fbRt25bU1NQC3V7r4sWLpKam0q5du0IXThCE4lGQeyYKxsOgRD1x4kScnZ2ZOXNmnmtSx8TEMGvWLCpUqMDEiYYv0i8IQvEqzfWoBcPpbfoIDta+GzbAhx9+yKJFi+jTpw89e/ZU3xEclMPzgoKCOHToEBYWFkyfPp3o6Gjc3NyKp/SCIBSK6ka2ukhiaQajozdR+/v75/kVSJIk9u7dy969e3XuS09P5+OPP0Ymk6nvKi4IgvEQNWfToTdR53UHcEEQTFtenYklcYcXwTB6E/XJkydLshyCIJQgZVu0vgkvJVwYIV9Gv3qeIAhFz0wuw0wMzzMZIlELQhkkJryYFpGoBaEMksv016hzRKY2OoVK1HFxcezatYuQkBAePHhAWloa+m69KJPJOH78+AsVUhCEoiUWZTItBifqffv2MXfuXDIyMvJMzqp94j9dEIyPaPowLQYl6vDwcGbPnk1WVhYDBgygU6dOTJw4EScnJ5YtW0ZiYiK///47Bw4cwM7OjtmzZ6snwwiCYDzMZDLM9GRk0fRhfAxK1Bs2bCArK4sRI0Ywc+ZM9XYLCwtat24NQO/evRk+fDijRo1i2bJl7Nmzp2hLLAjCixM3tzUpBs0VDQkJQSaTMWLEiDyP8/b2Zu7cucTGxrJmzZoXKZ8gCMVANTxP30MwLgYl6ocPH2JlZaWxdoeZmRnp6elax3bu3BkLCwvRkSgIRkgsymRaDGr6sLW11epAtLe358mTJ6SlpWFjY/Pvic3NsbS0JC4urmhKKghCkZHL8phCLjK10TGoRl2pUiVSUlLIyMhQb/Pw8ADQWqM6KiqKp0+fYm4uhmoLgrGRyf5J1joeIk8bH4MSdd26dZEkifDwcPW29u3bI0kSS5cuJSEhAYBHjx7x0UcfIZPJaNy4cdGWWBCEFybL5yEYF4MSdceOHZEkicOHD6u3DR06lAoVKhAeHk6nTp1o37497dq1IygoCLlczrhx44q80IIgvBjRmWhaDErUXbp04dtvv6Vr167qbU5OTmzatAkfHx+ysrJISEggJycHNzc3li9fjq+vb5EXWhCEFyNuxWVaDGpAtrS0pGPHjlrba9euzY8//sj9+/eJi4vDwcGBWrVqif9wQTBSYj1q01KkPX3u7u64u7sX5SkFQSgGMkBfPhZp2viIIRmCUAapRnjo2ycYF5GoBaEMEonatOhN1PXq1SuSJxA3txUE4yPaqE2L3kStbwlTQxXVeQRBKEJ5TRUXedro6E3UJ06cKMlyCIJQgkTTh2nRm6irVKlSkuUoMaH75lOlStXSLoYgGMzCvOgSqFymv4lDtHwYH9GZKAhlkBz9s90MmgUnlAiRqAWhDJLnMVVcdCYaH5GoBaEMksv0N3GIPG18RKIWhDIor8WXxKJMxkckakEog8RdyE2LSNSCUAaZyWSY68nI+u5OLpQekagFoQySkUeNukRLIhSESNSCUAaJCS+mRSRqQSiD5HIw0zNgWi4GUhudQiVqSZI4duwY586dIy4ujvT0dDZt2qTen5qaSnh4ODKZTNzhRRCMkKhRmxaDE/WdO3d47733uHnzpnrBpefv5GJlZcWcOXOIjo5m8+bNIlkLgpExy6NGrW+7UHoM+i9JTk7m7bff5saNG3h5eTFp0iTs7e21jjMzM2PQoEFIksTRo0eLrLCCIBQNWT7/BONiUKJev3499+/fp127duzatYvx48djbW2t89jOnTsDcPHixRcvpSAIRcpMBuZy3Q8zkaeNjkFNHydPnkQmkzFz5kzMzfMOrVGjBhYWFkRFRb1QAQVBKHp53W28MDelnjlzJnv27NG738PDg8OHD2ttz8nJYfv27ezatYvbt28jl8vx9vZm8ODB9O7dO8/n3L9/P9u3b+f69evk5OTg4eHBgAEDGDRoEPI8ekTPnDnDxo0buXLlChkZGVSrVo1XXnmFUaNGYWlpqTcuNDSUNWvWEBISgkKhwN3dna5duzJu3DgcHBzyLOuLMihRx8TEYGVlRe3atQt0vJ2dHQqFolAFEwSh+BRXG3WzZs2oUaOG1nZXV1etbdnZ2UycOJGTJ09ib29P27ZtefbsGefPn2fKlClcunSJjz76SOfzzJ8/n23btmFlZUXr1q0xNzfn/PnzLFiwgPPnz7NixQqdyXrt2rUsXrwYMzMz/Pz8cHR0JDg4mGXLlvHrr7+yceNGbGxstOIOHDjA9OnTyc7OplmzZlSqVInQ0FC+++47jh8/zvbt26lQoUIhrljBGNyZmJOTU6DjsrKyUCgU2NnZGVwoQRCKl0ymf3THiwz6GDhwIP379y/QsZs2beLkyZPUrl2bTZs24eLiAigHLAwZMoTNmzfTqlUrunbtqhF35MgRtm3bhqurK1u2bKFmzZoAPHz4kGHDhnHs2DE2b97M8OHDNeLCwsJYsmQJNjY2bNq0icaNGwPw9OlTxo4dS3BwMAEBAcyePVsjLi4ujjlz5iBJEoGBgeryZGVlMW3aNA4ePMjcuXMJDAw0+HoVlEGfnVWrViUzM5Po6Oh8jz1//jxZWVnUqlWr0IUTBKF4qFbP0/cobtnZ2axbtw6AefPmqZM0QM2aNZk6dSoA3377rVbs6tWrAZg6dao6SQO4uLgwb948QFlzfr5SuXbtWiRJYvTo0eokDcpv/l988QVyuZxt27bx5MkTjbhNmzaRnp5Ov379ND40zM3N+fTTT7G3t+f48ePcvHmzEFeiYAxK1B07dkSSJDZu3JjncampqSxatAiZTEaXLl1epHyCIBQDM5ksz0dxu3jxIomJibi5udGiRQut/T169MDCwoKwsDDi4+PV2+Pi4ggPD8fCwoIePXpoxfn5+VGpUiUSEhK4dOmSevuzZ884c+YMAH379tWKq1atGk2aNCEzM5PTp09r7Dt+/LjeOHt7ezp16qRxXHEwKFG//fbbODk5sW3bNgICAnj8+LHGfoVCwaFDhxgwYAA3btygYsWKDBo0qEgLLAjCi1OtnqfvUVh//vknX3zxBR9//DHLli3jt99+09lcevXqVQAaNmyo8zw2NjbqvjDVsQAREREA1KlTR++IM9U5c8fdvn2btLQ0nJ2dqV69ep5xqucAZU5TDYjQV1ZdcUXNoDbq8uXLExgYyLhx41izZg3r1q1TT3rx8/NDoVAgSRKSJOHk5ERgYCC2trbFUnBBEArPTCbDXN961P9k6ri4OK19jo6OODo66j3v3r17tbbVrl2bpUuX4u3trd4WExMDQOXKlfWey93dnatXr6qPNSQu97G5f1bt00V1ztjYWK04R0dHnXNGcsflfr6iZnBnoq+vLz///DNLly7lyJEjZGZmAqjbdczNzenWrRtTpkz5z94gVxBMXUHWox4yZIjWvokTJ/Lee+9pba9bty4fffQRbdq0wd3dHYVCQUREBAEBAVy7do23336bPXv2UKlSJUDZPAroHGGhoqrkPX36VL2tIHGqAQyGxhX2+XTFFbVCrfVRuXJlFi9ezMKFC7ly5QoJCQlIkkSFChXw8fERIz0EwcgV5A4vW7duxc3NTWOfvtr0iBEjNH63tbWlYsWKtGnTBn9/fy5dusTq1auZO3fuixe+DHqh1fOsra3FOh6CYIJk6O+gUqVvNzc3qlat+kLPY2lpyTvvvMP48eM1OulUtdC0tDS9sarabO6KX0HiVDVbQ+MK+3y64oqaWH5FEMog1ep5+h5FydPTE0Bj9IaqWfTevXt641Rt5LmbUF807v79+3rjVPt0xT158kTv5D1V3It+qOXFoBp1XhcnL3k1/AuCUPJKcpnTpKQkQLPGWb9+fUA5CUWXtLQ0bty4oXFs7p9v3LhBenq6zpEfqnPWq1dPvc3T0xNra2uSkpKIiorSOfLj8uXLWnEODg5Ur16dqKgowsLCaN26dYHiippBibowY6JlMlmxDlsRBMFwMvTfcquoR1EfOnQIAB8fH/W2pk2bUr58eeLi4ggODtYaS3348GEyMzNp2LChugMSlKM2GjRoQHh4OIcPH6Zfv34acUFBQcTFxeHq6krTpk3V2y0tLenQoQNHjx5l3759TJw4USMuOjqaS5cuYWFhQceOHTX2denShQ0bNrBv3z6tRK1QKDh16hQAL7/8soFXpuAMavpQDb0z5FHQKedlQUpKCgf272P+Jx/zau+eVHVzwcZCho2FjOvXruUbfzEkhDEjR1DPyxNne2vKOdjQoG5txo4eSWiuwf3/ZaV1DW/euMGmDet5f+J42rZqgZOdFTYWMjq0bVWUL6/EyGQy5HLdD0MXZbp69SqnTp0iOztbY3tWVhbr169n8+bNgGaHo5mZGaNHjwaUMxMTExPV++7cucOSJUsAePfdd7We75133gFg8eLF3L17V709MTGR+fPnAzBmzBittT7GjBmDTCZj3bp16lowKNu0Z8+eTU5ODoMHD9bqMB0+fDjW1tbs3buXEydOaLy+uXPnolAo6Nq1a4HXQCoMg2rU1/L5Q1AoFISFhbFmzRr10Jw2bdq8UAH/S06dPMGbr79WqNjVq1YyZfIk9R+D6ivfrchIbkVGsnXL9yz/eiWjxrxTZOU1RqV1DWfNnMaBfT8XvuBGRo7+WpqhHVexsbFMmDABZ2dn6tevT/ny5UlKSuLvv//mwYMHyOVypk2bRvv27TXiRowYQXBwMKdOnaJbt260bt2arKwsfv/9dzIyMvD399da5wOUsxYHDRrE9u3b6dOnD23atFEvyqRKmkOHDtWKa9SoEVOmTGHx4sW89dZbtGrVCgcHB4KDg0lMTKRx48ZMnjxZK87d3Z3PPvuM6dOnM2HCBJo3b07FihUJDQ0lNjaWGjVqsGDBAgOvmmGK9J6J9vb2tG7dmtatWzN58mQmTJjAzp07qVOnTlE+jUmrWLEizZr70ty3BZUrV2HCuPwT69WICHWC6dL1Zf63ZBl1/2kPC79yhSmTJ3Hm9K9Mfn8inTp3wfM/vr5KaVxDMzMz6tarR/PmLWjW3Je/LgSzbevmYnl9JaEo26i9vb0ZNmwYYWFh3Lx5k6SkJGQyGW5ubvTv358hQ4ZoNHuomJmZsXLlSrZt28bu3bs5e/YscrmcBg0aMHjwYPr06aP3OefNm0fz5s3ZunUrQUFB5OTk4Onpme8yp2PGjMHb25sNGzYQFhamXubU398/z2VOe/fuTbVq1Vi9ejUhISGEhobi7u7OqFGjjG+ZU0NMmTKFQ4cOERgYyLJly4rraUzKK7370PfVf9vU7t65U6C4n37cQXZ2No6Ojvzw426NGVI+DRvy4+6fqV2zKikpKfxyYD/vvf9BURfdaJTWNdy6fSdmZmbq3xcumPdCr6O0KSe8FM3qedWqVWPOnDmFKodcLmfo0KE6a8D56dOnT57JXJ8OHTrQoUMHg+MaN27MypUrDY4rCsU2PK9q1arqtV4Fpdx/6IZ48M+wptq16+icxuro6Ejt2spvLampxTc7yhiU1jUs7PMaK3keCzKJm9san2JL1GlpF8PfvwAAGoxJREFUaSgUClJSUorrKcqM6v8s5Xjz5g2dYzmfPHnCzZvKoUxNmjYryaKZDHENNcnyeQjGpdgS9ZYtW8jJySnWQeBlxaDBQ7GxseHJkye8NbA/VyMi1KNqwq9c4Y0B/UhJSaHry93o3qNnaRfXKIlrqKm0lzkVDGNQG3V+zRgZGRnExcVx4sQJTp8+jUwm0xrnKBiuatWq/PDjboYPHcSJ48do1riBesRCeno6lSpVYsasOcz+SKyjoI+4hpoKsiiTYDwMStT+/v4FGmOpWvq0W7dujBw5snAlEzR0696DA4eOMWzoW9yKjCQ9PV29Lz09naSkJFJTU/O8OWdZJ67hv2T//NO3TzAuBjV9VK5cGXd3d72PatWqUb9+fV577TXWrFnDihUr8r1buVAwn87/hHatW2BlZcXunw8QfT+B6PsJ7P75AJWrVGH1qkA6v9RW62YOwr/ENfyX6Ew0LQZl0ZMnTxZXObTcunWL3377jbCwMK5cucKdO3eQJInly5frvAXPf9n2bVv5fOECKlasyLGTZzTudtyz1yv4tWxFs0b1uRoRweJFX/LZF1+VYmmNk7iGmkTTh2kxqEatUChQKBRaU0WLw/bt2/n888/Zv38/t2/fVjenlEWBXy8HYPDQYTpvSV+hQgUGDfEH4MD+/87suaIkrqEmOXmsnieaPoyOQYna19cXPz8/Hjx4UFzlUfPy8mLUqFEEBARw7Ngx/Pz8iv05jdX1a8p7v9Ws6aH3GA8P5VKSBZ0AUtaIa6iptO9CLhjGoKYPW1tbzM3N87zvWFEZOHBgsT+HqVBNh42OjtJ7TFSUcnGa4p7KaqrENdQky6Mt2tBFmYTiZ1CNumrVqqSlpZGVlVVc5RF0aNioMQA7d2zXOVlDoVDw484fAGjh17JEy2YqxDXUJMvnn2BcDErUPXv2JCsri+PHjxdXef7zHj58qH7kHl2QlJSksS/38rBjxo4DIDoqir6v9OBiSAjZ2dlkZ2dzMSSEvq/0IPqfW9qPnzipZF9QKSiNa5iRkaFxbtXtl7KzsjS2JycnF+dLLzKi6cO0GNT0MWrUKE6ePMknn3yCk5OTzrsdCHmr5u6qc3vH9prX8tqN29T4Z9rzm28NIjjoTwK/Xs7538/RpmVzrKysAGUCAeXX1U/mf0rXl7sVX+GNRGlcw50/bOed0W9rbQ8J+UujPO07vMTRE78W5mWVKGVC1rd6XgkXRsiXQYl6zZo1tGrVisjISEaOHIm3tzdNmjShfPnyepcVBLTupiAYbvHSZfTu05fv1q3hzz/OqxcZqlGzJq1bt2XsuAm0Eh+ceRLX8F8leYcX4cXlmaiHDRuGs7MzK1asAOCbb75BJpOph8pdu3aN69ev642XJAmZTCYSdS5pmYUfZtixU2c6dupchKUxTaVxDf2Hj8B/+IhCP6+xUU140bdPMC55JuqgoCBcXFzUv/fr10/0CAvCf4GoUpsUg5o+vvzyy+IqhyAIJagk70IuvDixEIcglEGiQm1aRKIWhDJIhv67jYtx1MZHJGpBKIPEokymJd9ErVAomDVrVqGfQCaT8fnnnxscFx4ezvz589W/37x5E4CAgADWr1+v3r5z585Cl00QyirR9GFa8k3UGRkZ7N27t1AnVw3PK0yiVigUhIaGam2/UwYWzBGEYifT3/QhqtTGJ99EbW5uTpMmTUqiLBpatmyZ5xhtQRAKTzR9mJZ8E7WTkxObN28uibIIglBCRKI2LaIzURDKIHHPRNMiErUglEF5rZInFmUyPiJRC0JZJIZ9mBSRqAWhDBJTyE2LSNSCUEaJdGw68kzU165dK6lyCIJQ0kSmNhmiRi0IZZBo+jAtIlELQhkk+hJNi0jUglAGKSe86BlHLTK10RGJWhDKIDEz0bSIRC0IZZBI1KZFJGpBKIPEFHLTIhK1IJRBsjymkIsatfERiVoQyiIx7MOkiEQtCGWQaPowLSJRC0IZJFbPMy0iUQtCGSQjj1EfJVoSoSBEohaEMkiWxz0T9d5LUSg1IlELQhkk+hJNi0jUglAGyfJYlEnUqI2PSNSCUBaJKrVJEYlaEMogMerDtIhELQhlkBhHbVpEohaEskg0fZgUkagFoQwSTR+mRSRqQSiDRNOHaRGJWhDKojzWoxZ52viIRC0IZZCYQm5aRKIWhDJI3IXctIhELQhlkBj0YVpEohaEskhkapMiErUglEHK4Xn6mj5KuDBCvkSiFoQySFSoTUuZSdTZ2dkAxMfFlXJJBKFwVO9d1Xv5RTx4EK932MeDB/EvfH6haJWZRJ2QkADA28OGlHJJBOHFJCQkUKNGjULF2tvb4+TklO/fgZOTE/b29oV6DqHoySRJkkq7ECUhPT2dK1eu4OrqipmZWWkXRxAMlp2dTUJCAj4+PlhbWxf6PElJSSgUijyPsbe3x9nZudDPIRStMpOoBUEQTJW8tAsgCIIg5E0kakEQBCMnErUgCIKRE4laEATByIlELQiCYOREohYEQTByIlELgiAYOZGoBUEQjJxI1IIgCEZOJGpBEAQjJxK1kfh/e/ceFtVx8HH8uyCrLHJRUULAishzAI24VG0IpjUYH01MzGMUU6qAtkHTNtKnT8SENhptq00NxqcxaNsEjY1G6w01BYLWpDE1BbmJIt4SEjUoRAmy3Fl3d94/ePfElQUlku4a5vMPcs6ZszNn5bezM+cihMBisQCoPyVJkkAGtdPQaDSYTCabZTKwJUmCXnSbU2eWk5NDUVERpaWlBAQEEBgYSFxc3De+laXUUWlpKd/73vcYOHCgo6siSd0m757nYGvWrCEjIwMANzc3hBCYTCa8vb155plniImJYfjw4Q6u5d1t5cqVbN26lWXLlvH444/j7e3t6CpJUre4rlixYoWjK9Fbbdy4kfT0dB566CH++Mc/kpyczKOPPkpTUxNlZWUUFhZy9epV/Pz88Pf3d3R170pbt24lPT0dgJMnT+Lj48OwYcPu6H7OkvS/JoPaQSorK3nllVdwd3dn5cqVjB49Gg8PD4YMGcLUqVPx8fGhsrKSgoICqqqqCAwM5N5773V0te8qhYWFvPrqq9TX1zNp0iTOnTvHsWPHGDBggAxr6a4iJxMdpLa2lgsXLjBp0iRCQ0PViUPr8/Di4+NZtGgRY8aM4eOPP2bTpk2cPXvWkVW+qzQ2NvLee+9RWVlJcnIy69evJyEhgfr6etatW0dWVhYGg8HR1ZSk2yInEx2kpqYGs9nM5cuXaWtrw83NDQBXV1csFgsuLi5MnToVV1dXNmzYwOHDhwkJCSEwMBCdToemkweTSu00Gg01NTXo9Xqio6PRaDSkpKQAsHnzZtatWwcgx6ylu4Ic+nCQPn36kJubi9ls5sknn6Rv376YzWZcXFzQaDQIIdBoNAQHB+Pq6kpBQQFlZWVMmDABf39/db1kn1arRa/XExYWxvjx4wFwcXHhgQceoLm5maNHj1JaWmp3GEQeW8nZyKB2EFdXV44cOcKJEycwGAzExMTg4uKCxWJBo9HYhPXIkSO5fPkyxcXFVFVVMWXKFLUHLnXO09OToUOHAl9fUOTq6tplWFs/LCXJmcigdhCtVktQUBA5OTmcPHmS/v37M2bMGDQajU1YW/+t1+t57733MBqNzJw5E61W6+gm3FU0Gg0uLi6YzeZOwzowMBAPDw8AXn75ZXJycpg8ebKDay5JMqgdys/PD41GQ2FhIZ988gk6nY5Ro0Z1CGuTyYSrqytZWVl88sknTJ06FV9fX/n1/BvoKqz9/f0ZNmwYGRkZvPHGG5w5c4annnpKDW9JchQZ1A6k0WgYMWIE165do7i4mLNnz9KnTx9Gjx6NRqPBbDYjhKBPnz706dOHHTt24O3tzdNPP03fvn0dXf271s1h3dTUREFBAcXFxZSVlbFjxw58fHzYvXu3OnQiSY4kg9rB3N3diYiIoLa2luLiYoqLi7l27RoTJkzAxcVFHS/dvn07u3fvJjo6mocffliOUd+hG8P6wQcfpKWlhaKiIj777DO8vb155513CAkJcXQ1JQmQp+c5BV9fX1JSUhg0aBBbt25l06ZNnDp1iqioKMLDw/nPf/5DdnY2vr6+JCcn4+7u7ugqfyfcOHSk1Wq5fv063t7ebNu2jREjRjiwZpJkS97rw4m0tbWRl5fHqlWrqKys5Ma3JiwsjFdffVUGyLcgLS2NjRs34uXlxbZt22RPWnI6MqidUE1NDeXl5Zw6dQohBIqioNfr8fX1dXTVvnNOnjxJXFwcQgjeffdd+UEoOSUZ1FKvZjQayc7OJiIiQoa05LRkUDupG6+Ok1fKfbvk8ZWcnQxqSZIkJyevlZUkSXJyMqglSZKcnAxqSZIkJyeDWpIkycnJoJYkSXJyMqjvIgkJCYSGhpKZmWmz/OjRo4SGhjJp0iQH1aznZWZmEhoaSkJCQrfKfVvHIjQ0lNDQUCorK3t0v7difVTb0aNH/6evKzmXXnevj9TUVPbu3dthuYeHB0OHDiU6Opp58+Zxzz33OKB2zuH06dMcOnSIgIAAZs6c6ejqSFKv12t71G5ubvj6+uLr68ugQYNobm7mzJkzbNq0ienTp1NUVOToKt42d3d3hg8f3mO35Dx9+jTp6el2P9AkSfrf63U9aqvIyEi2bNmi/t7S0sKBAwdYtWoV9fX1/PrXv+bQoUM2z9JzVhEREeTm5jq6GpIkfUt6bY/6Zu7u7syYMYMXX3wRgKtXr3Lo0CEH10qSJKkX96g7M23aNH7zm99gsVgoLy/n8ccfB9on8goKCnj55ZeZPHkyf/vb33j//fepqqrCzc3NZqjEaDSyc+dOcnJy+PTTT2lubmbw4MFERUWRlJTU5c1/PvroIzIyMigvL0cIQUhICHPmzGHGjBmdljl69CiJiYkEBATwwQcf2N2mqqqKv//97xw5coRLly4B4O/vj16v54knniAqKgponzSzKigosPkd4O233+b++++3WVZUVMQ777xDcXExtbW1eHh4EB4eTmxsLI899lin99H48ssvSU9P58MPP6Suro4hQ4YwefJknn322U7beicaGxs5ePAghw8fpqKigurqaoxGI35+fkRFRfH0008TFBR0y/2cO3eOv/zlLxQUFFBfX09AQADTp09nwYIFXT7LsrKykrfeeosjR45QXV2Ni4sLw4cP55FHHiE+Ph6dTteDrZW+S2RQ30Sr1TJgwAC++uorGhsbO6yvra1l5syZfPHFF2i12g5PWrly5QoLFizgzJkzQPuTRNzd3bl8+TKZmZlkZ2ezZs0apkyZ0mHfGRkZpKWlAe03tff09KSsrIwXXnhB3d83ceDAAZ5//nlaW1sB6Nu3L/369eOzzz6joqKC/Px8NeB9fX1pbW2lsbERNzc3vL29bfZ1c3vT0tLIyMhQf+/fvz8Gg4G8vDzy8vL44IMPWLNmTYcne1dUVBAfH09tbS0AOp2OmpoaNm/ezL///W9+8pOffOP2dmbfvn384Q9/ANqfAu/p6YnFYuHixYtcvHiRrKws1q9fT3R0dKf7OHbsGC+99BLNzc30798fIQSff/4569at46OPPmLTpk12n7F48OBBUlJSaGtrA9q/wV2/fp3y8nLKy8v55z//yVtvvSVvZSvZJ3qZF154QSiKIuLj4+2ub2lpEaGhoUJRFLF69Wp1eXx8vFAURej1ejFx4kRx+PBhYTabhRBCnD9/XgghhNFoFLNmzRKKooh58+aJkpISYTQahRBCfPnll2LVqlVCURQxZswYceHCBZvXLSwsVF83JSVFXLlyRQghhMFgEK+88opQFEWMHTtWKIoi9uzZY1M2Pz9fKIoiYmJiOrSnuLhYjBw5UiiKIhISEsTx48eFxWIRQgjR0NAg/vWvf4nU1FSbMnv27OnyGFlt3rxZKIoioqOjxY4dO0R9fb16DLOzs8WECROEoijir3/9q005o9Eopk2bJhRFEQ8//LAoKCgQQghhNpvF+++/L6KiotS23qoON+vqWGRlZYm1a9eK48ePi7a2NiGEEBaLRXz66adi8eLFQlEUERUVJZqamjqUVRRFfQ9mzZolzpw5I4QQoq2tTezZs0dEREQIRVHE0qVLO5Q9fvy4GDVqlBg5cqRYu3atqK6uFkIIYTKZRElJiZg5c6ZQFEX87Gc/61A2JiZGKIoi8vPzu3UcpO8WGdQ32bJli/pHmZubqy63BvWoUaPE2bNn7ZbduXOnUBRFzJkzRw3omy1btkwoiiJ+97vf2SxPTExUw9QapDf67W9/q9arO0EdGxsrFEURc+fO7bRON7udoDYYDEKv14vRo0eL06dP292mpKREhIaGivHjx6vBKIQQe/fuVY9lRUVFh3KFhYVqW3syqLtisVjE/PnzhaIoIjMzs8N6a30eeOABce3atQ7rrccsLCxMXLp0yWZdXFycUBRFbN++3e5rX7t2Tf1QO3HihM06GdSSEELIyUTa70dcWVnJxo0b1aGHgIAAYmJiOmz7wx/+EEVR7O7HejpbYmJipw+ffeKJJwD4+OOP1WV1dXXqBQ0LFiywO6b785//vBstaldRUcGJEycAWLJkSY8+EPfAgQM0NzcTHR1NWFiY3W0iIyMJDAzEYDBQXl5uUxZgypQpBAcHdyg3btw4xo8f32N1vR0ajYaJEycCUFJS0ul2cXFx+Pj4dFg+Y8YM7rnnHiwWCwcPHlSXX7x4kZKSEry8vIiNjbW7Tx8fH370ox8B8N///vdOmiF9R/XaMWp7E2VWgwcPZv369XYnhiIjI+2WMZlMaii+9NJL/P73v7e7ndlsBqC6ulpddvr0aYQQuLi4MHbsWLvlhg4dir+/P1VVVZ036ibHjx8H2oNgzJgxt13udhw7dgyA/Px8JkyY0Ol2BoMBaJ/MtB67U6dOAXQZxuPHj6ewsLCnqquqrq5my5Yt5OXlcfHiRZqamrBYLDbbXLlypdPyP/jBD+wud3FxYdy4cWRlZantg69Dv7m5Wf0gsKe5uRmgW++v1Hv02qC+caJMo9Hg7u6uXpk4e/bsDpNoVgMGDLC73GAwcP36daC9h3wr1ok9QJ1Q8/T07HLm38/Pr1t/yDU1NUD72R097erVq0D7+ectLS233N5ee4cMGdLp9n5+fndYw44KCgp45pln1FCE9mPet29ftY6NjY0267tTL2t7rO2Dr4+TyWRS34+u3HicJMmq1wb1zRe83C5XV1e7y2/sle3bt4/w8PBvXLe7gbW9iYmJ6rnnzuz69essWbJEHa559tlnGT16tBrSALt27WLp0qU9+rri/x+gFBYWxv79+3t031LvIceoe4iPj48a4pcvX+5W2YEDBwLQ0NDQZe+0q6/k9lhP9fo2vk7fyb6t7e2qPd1t662UlpZSXV2Nj48PGzZsYNy4cTYhDfDVV1/dcj+3U2dr+wAGDRoE2A51SVJ3yaDuIW5ubtx3331A+0Ur3REeHo5Go8FisVBcXGx3my+++KLbHwDWcem6ujpKS0tvu5z1nGfRxeM09Xo90D6c0N2v6yNHjgTo8n4qPT0+bQ3KoKAg3N3d7W5zOxN5ndVLCKG2x9o++Po41dXVqXMGktRdMqh70JNPPgm0n/1xqwtUrJNs0N4bt14ZmJGRYTcg33zzzW7XZ8SIEURERADtF6ZYx9BvpX///gDU19d3us0jjzyCTqfDYDCwfv36Lvd3Y1utZaH9IpDz58932L6kpKTHg9rT0xOA8+fPqxed3OjIkSO3dSvR7du32z0u+/fvV682vPFiphEjRqhhfav3oLW1FaPReMs6SL2PDOoeFBsbi16vp62tjXnz5rFz506bqxuvXr3Ku+++S3x8PG+//bZN2UWLFqHRaMjLyyM1NVWdeGpoaGDt2rXs2LFDDZvuSE1NxdXVlaKiIpKSkigrK1PXNTY2kp2dzeLFi23KhISEAO2n93XWCxwwYADPPfccAG+88QZLly7l888/V9e3trZSVFTE8uXLiYuLsyk7bdo0QkJCMBqNLFy4UO2JWiwWPvzwQ5KTk9UPi57y/e9/H3d3d+rq6nj++efVYYrW1lZ2795NcnKy3dPubtbW1kZSUhLnzp0D2se+9+7dy4oVK4D2/wP33nuvTZkXX3wRrVZLYWEh8+fPp6ioSB3jN5vNnD17lvT0dCZPntzjQz7Sd0OvnUz8Nri5ubFhwwYWLVpESUkJy5YtY/ny5Xh5eWE0Gm3OJrD2oK3GjRtHSkoKaWlp7Nu3j/379+Pl5UVjYyNms5mf/vSnlJeXU1BQ0K06jR07lrS0NFJTU8nPzyc2NpZ+/frRr18/DAYDQggCAgJsygQFBamnxz311FP4+Piol0WvXbtW7SEmJCTQ0NDAunXr2LVrF7t27UKn0+Hm5kZDQ4MaRjfv383Njddee42EhAQuXLjA3Llz0el0WCwWWltbGTZsGElJSfzpT3/qVlu74uXlxXPPPceqVavIzc0lNzcXT09PWlpaMJlMhIeHM2vWLFauXNnlfpYvX86yZcuYPn06np6etLa2qr1kvV5PampqhzIRERGkp6ezePFiioqKmDt3LlqtFp1OR1NTk00vu7P7oki9m+xR97BBgwaxdetW1qxZw8SJExk4cCBNTU0ABAcHM2PGDP785z+zcOHCDmWTkpJ48803uf/++9HpdJhMJu677z5Wr15tNwBu12OPPUZOTg7x8fHqTYdMJhPBwcHMnj2b1atXdyjz+uuvM2fOHAIDA2lqauLSpUtcunSpw7DBL3/5S/bv38+Pf/xjgoKCsFgs6k2oHnzwQZYsWcK2bds67D8kJIR9+/Yxe/ZsBg8ejMlkwtfXl/nz57N79+5OT4+8E4mJiaSnp6u9a7PZTHBwMMnJyfzjH/+we4+Om0VGRrJz504effRRtFotGo2G4cOH86tf/YotW7Z0uo+JEydy4MABfvGLXzBq1Ci0Wi0NDQ14eHgQGRnJwoULyczM7PChJkkAGtHVjJEkSZLkcLJHLUmS5ORkUEuSJDk5GdSSJElOTga1JEmSk5NBLUmS5ORkUEuSJDk5GdSSJElOTga1JEmSk5NBLUmS5ORkUEuSJDk5GdSSJElO7v8AY2iPup4HQuIAAAAASUVORK5CYII=\n",
            "text/plain": [
              "<Figure size 432x288 with 2 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "WJO60YQWHJzI"
      },
      "source": [
        "### Precision, Recall, F1-Score, Mean Absolute Error, Mean Percentage Error and Mean Squared Error"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "cyMTNjLOG3Eq",
        "outputId": "37119624-ad1f-4ef7-d897-2d5701da7d4c"
      },
      "source": [
        "report= classification_report(y_test_all,pred_b)\r\n",
        "print(report)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "              precision    recall  f1-score   support\n",
            "\n",
            "           0       1.00      0.97      0.99    134608\n",
            "           1       0.04      0.91      0.08       199\n",
            "\n",
            "    accuracy                           0.97    134807\n",
            "   macro avg       0.52      0.94      0.53    134807\n",
            "weighted avg       1.00      0.97      0.98    134807\n",
            "\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "BJxVeP4tG4Ob",
        "outputId": "948e2d89-0f4b-4edb-d325-491b3b07cbf3"
      },
      "source": [
        "mean_abs_error = mean_absolute_error(y_test_all,pred_b)\r\n",
        "mean_abs_percentage_error = np.mean(np.abs((y_test_all - pred_b) // y_test_all))\r\n",
        "mse= mean_squared_error(y_test_all,pred_b)\r\n",
        "r_squared_error = r2_score(y_test_all,pred_b)\r\n",
        "print(\"Mean absolute error : {} \\nMean Absolute Percentage error : {}\\nMean Squared Error : {}\\nR Squared Error: {}\".format(mean_abs_error,mean_abs_percentage_error,mse,np.abs(r_squared_error)/100))"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Mean absolute error : 0.029397583211554296 \n",
            "Mean Absolute Percentage error : 0.00013352422351955016\n",
            "Mean Squared Error : 0.029397583211554296\n",
            "R Squared Error: 0.18944013907944574\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.7/dist-packages/ipykernel_launcher.py:2: RuntimeWarning: divide by zero encountered in floor_divide\n",
            "  \n"
          ],
          "name": "stderr"
        }
      ]
    }
  ]
}