Lending_Club__colab.ipynb

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  "metadata": {
    "colab": {
      "name": "Lending Club _ Final_Copy.ipynb",
      "provenance": [],
      "collapsed_sections": []
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
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    {
      "cell_type": "code",
      "metadata": {
        "id": "dyA3zArhKNw2"
      },
      "source": [
        "\n",
        "# import libraries\n",
        "import pandas as pd\n",
        "import numpy as np\n",
        "import matplotlib.pyplot as plt\n",
        "import seaborn as sns\n",
        "from sklearn.model_selection import train_test_split\n",
        "from sklearn.preprocessing import MinMaxScaler\n",
        "from tensorflow.keras.models import Sequential\n",
        "from tensorflow.keras.layers import Dense,Dropout\n",
        "from tensorflow.keras.callbacks import EarlyStopping\n",
        "from tensorflow.keras.models import load_model\n",
        "from sklearn.metrics import confusion_matrix, classification_report\n",
        "from pickle import dump, load\n",
        "\n",
        "\n",
        "%matplotlib inline"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "bLpUlMkdKbQQ"
      },
      "source": [
        "\n",
        "df = pd.read_csv('loan_data.csv')\n"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Z9xwZrTNMU_o",
        "outputId": "6fad0b51-9523-4217-a5c3-f24e847810bb",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 561
        }
      },
      "source": [
        "df.info()\n",
        "df.head()\n"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "<class 'pandas.core.frame.DataFrame'>\n",
            "RangeIndex: 9578 entries, 0 to 9577\n",
            "Data columns (total 14 columns):\n",
            " #   Column             Non-Null Count  Dtype  \n",
            "---  ------             --------------  -----  \n",
            " 0   credit.policy      9578 non-null   int64  \n",
            " 1   purpose            9578 non-null   object \n",
            " 2   int.rate           9578 non-null   float64\n",
            " 3   installment        9578 non-null   float64\n",
            " 4   log.annual.inc     9578 non-null   float64\n",
            " 5   dti                9578 non-null   float64\n",
            " 6   fico               9578 non-null   int64  \n",
            " 7   days.with.cr.line  9578 non-null   float64\n",
            " 8   revol.bal          9578 non-null   int64  \n",
            " 9   revol.util         9578 non-null   float64\n",
            " 10  inq.last.6mths     9578 non-null   int64  \n",
            " 11  delinq.2yrs        9578 non-null   int64  \n",
            " 12  pub.rec            9578 non-null   int64  \n",
            " 13  not.fully.paid     9578 non-null   int64  \n",
            "dtypes: float64(6), int64(7), object(1)\n",
            "memory usage: 1.0+ MB\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "execute_result",
          "data": {
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              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>credit.policy</th>\n",
              "      <th>purpose</th>\n",
              "      <th>int.rate</th>\n",
              "      <th>installment</th>\n",
              "      <th>log.annual.inc</th>\n",
              "      <th>dti</th>\n",
              "      <th>fico</th>\n",
              "      <th>days.with.cr.line</th>\n",
              "      <th>revol.bal</th>\n",
              "      <th>revol.util</th>\n",
              "      <th>inq.last.6mths</th>\n",
              "      <th>delinq.2yrs</th>\n",
              "      <th>pub.rec</th>\n",
              "      <th>not.fully.paid</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>1</td>\n",
              "      <td>debt_consolidation</td>\n",
              "      <td>0.1189</td>\n",
              "      <td>829.10</td>\n",
              "      <td>11.350407</td>\n",
              "      <td>19.48</td>\n",
              "      <td>737</td>\n",
              "      <td>5639.958333</td>\n",
              "      <td>28854</td>\n",
              "      <td>52.1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>1</td>\n",
              "      <td>credit_card</td>\n",
              "      <td>0.1071</td>\n",
              "      <td>228.22</td>\n",
              "      <td>11.082143</td>\n",
              "      <td>14.29</td>\n",
              "      <td>707</td>\n",
              "      <td>2760.000000</td>\n",
              "      <td>33623</td>\n",
              "      <td>76.7</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>1</td>\n",
              "      <td>debt_consolidation</td>\n",
              "      <td>0.1357</td>\n",
              "      <td>366.86</td>\n",
              "      <td>10.373491</td>\n",
              "      <td>11.63</td>\n",
              "      <td>682</td>\n",
              "      <td>4710.000000</td>\n",
              "      <td>3511</td>\n",
              "      <td>25.6</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>1</td>\n",
              "      <td>debt_consolidation</td>\n",
              "      <td>0.1008</td>\n",
              "      <td>162.34</td>\n",
              "      <td>11.350407</td>\n",
              "      <td>8.10</td>\n",
              "      <td>712</td>\n",
              "      <td>2699.958333</td>\n",
              "      <td>33667</td>\n",
              "      <td>73.2</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>1</td>\n",
              "      <td>credit_card</td>\n",
              "      <td>0.1426</td>\n",
              "      <td>102.92</td>\n",
              "      <td>11.299732</td>\n",
              "      <td>14.97</td>\n",
              "      <td>667</td>\n",
              "      <td>4066.000000</td>\n",
              "      <td>4740</td>\n",
              "      <td>39.5</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "   credit.policy             purpose  ...  pub.rec  not.fully.paid\n",
              "0              1  debt_consolidation  ...        0               0\n",
              "1              1         credit_card  ...        0               0\n",
              "2              1  debt_consolidation  ...        0               0\n",
              "3              1  debt_consolidation  ...        0               0\n",
              "4              1         credit_card  ...        0               0\n",
              "\n",
              "[5 rows x 14 columns]"
            ]
          },
          "metadata": {
            "tags": []
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          "execution_count": 7
        }
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    {
      "cell_type": "code",
      "metadata": {
        "id": "XacDGoKbMlw3",
        "outputId": "aaffb155-9749-4269-b753-2f89f90d766f",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 452
        }
      },
      "source": [
        "\n",
        "df.describe().transpose()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
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              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>count</th>\n",
              "      <th>mean</th>\n",
              "      <th>std</th>\n",
              "      <th>min</th>\n",
              "      <th>25%</th>\n",
              "      <th>50%</th>\n",
              "      <th>75%</th>\n",
              "      <th>max</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>credit.policy</th>\n",
              "      <td>9578.0</td>\n",
              "      <td>0.804970</td>\n",
              "      <td>0.396245</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000e+00</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>int.rate</th>\n",
              "      <td>9578.0</td>\n",
              "      <td>0.122640</td>\n",
              "      <td>0.026847</td>\n",
              "      <td>0.060000</td>\n",
              "      <td>0.103900</td>\n",
              "      <td>0.122100</td>\n",
              "      <td>0.140700</td>\n",
              "      <td>2.164000e-01</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>installment</th>\n",
              "      <td>9578.0</td>\n",
              "      <td>319.089413</td>\n",
              "      <td>207.071301</td>\n",
              "      <td>15.670000</td>\n",
              "      <td>163.770000</td>\n",
              "      <td>268.950000</td>\n",
              "      <td>432.762500</td>\n",
              "      <td>9.401400e+02</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>log.annual.inc</th>\n",
              "      <td>9578.0</td>\n",
              "      <td>10.932117</td>\n",
              "      <td>0.614813</td>\n",
              "      <td>7.547502</td>\n",
              "      <td>10.558414</td>\n",
              "      <td>10.928884</td>\n",
              "      <td>11.291293</td>\n",
              "      <td>1.452835e+01</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>dti</th>\n",
              "      <td>9578.0</td>\n",
              "      <td>12.606679</td>\n",
              "      <td>6.883970</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>7.212500</td>\n",
              "      <td>12.665000</td>\n",
              "      <td>17.950000</td>\n",
              "      <td>2.996000e+01</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>fico</th>\n",
              "      <td>9578.0</td>\n",
              "      <td>710.846314</td>\n",
              "      <td>37.970537</td>\n",
              "      <td>612.000000</td>\n",
              "      <td>682.000000</td>\n",
              "      <td>707.000000</td>\n",
              "      <td>737.000000</td>\n",
              "      <td>8.270000e+02</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>days.with.cr.line</th>\n",
              "      <td>9578.0</td>\n",
              "      <td>4560.767197</td>\n",
              "      <td>2496.930377</td>\n",
              "      <td>178.958333</td>\n",
              "      <td>2820.000000</td>\n",
              "      <td>4139.958333</td>\n",
              "      <td>5730.000000</td>\n",
              "      <td>1.763996e+04</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>revol.bal</th>\n",
              "      <td>9578.0</td>\n",
              "      <td>16913.963876</td>\n",
              "      <td>33756.189557</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>3187.000000</td>\n",
              "      <td>8596.000000</td>\n",
              "      <td>18249.500000</td>\n",
              "      <td>1.207359e+06</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>revol.util</th>\n",
              "      <td>9578.0</td>\n",
              "      <td>46.799236</td>\n",
              "      <td>29.014417</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>22.600000</td>\n",
              "      <td>46.300000</td>\n",
              "      <td>70.900000</td>\n",
              "      <td>1.190000e+02</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>inq.last.6mths</th>\n",
              "      <td>9578.0</td>\n",
              "      <td>1.577469</td>\n",
              "      <td>2.200245</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>2.000000</td>\n",
              "      <td>3.300000e+01</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>delinq.2yrs</th>\n",
              "      <td>9578.0</td>\n",
              "      <td>0.163708</td>\n",
              "      <td>0.546215</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>1.300000e+01</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>pub.rec</th>\n",
              "      <td>9578.0</td>\n",
              "      <td>0.062122</td>\n",
              "      <td>0.262126</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>5.000000e+00</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>not.fully.paid</th>\n",
              "      <td>9578.0</td>\n",
              "      <td>0.160054</td>\n",
              "      <td>0.366676</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>1.000000e+00</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "                    count          mean  ...           75%           max\n",
              "credit.policy      9578.0      0.804970  ...      1.000000  1.000000e+00\n",
              "int.rate           9578.0      0.122640  ...      0.140700  2.164000e-01\n",
              "installment        9578.0    319.089413  ...    432.762500  9.401400e+02\n",
              "log.annual.inc     9578.0     10.932117  ...     11.291293  1.452835e+01\n",
              "dti                9578.0     12.606679  ...     17.950000  2.996000e+01\n",
              "fico               9578.0    710.846314  ...    737.000000  8.270000e+02\n",
              "days.with.cr.line  9578.0   4560.767197  ...   5730.000000  1.763996e+04\n",
              "revol.bal          9578.0  16913.963876  ...  18249.500000  1.207359e+06\n",
              "revol.util         9578.0     46.799236  ...     70.900000  1.190000e+02\n",
              "inq.last.6mths     9578.0      1.577469  ...      2.000000  3.300000e+01\n",
              "delinq.2yrs        9578.0      0.163708  ...      0.000000  1.300000e+01\n",
              "pub.rec            9578.0      0.062122  ...      0.000000  5.000000e+00\n",
              "not.fully.paid     9578.0      0.160054  ...      0.000000  1.000000e+00\n",
              "\n",
              "[13 rows x 8 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 8
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "YrSD_sp6B5ss",
        "outputId": "defb70c7-e420-4d2b-d32c-5d838397d178",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "source": [
        "df['not.fully.paid'].isnull().mean()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.0"
            ]
          },
          "metadata": {
            "tags": []
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          "execution_count": 9
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "MqVpB62gC7cx"
      },
      "source": [
        "df1=pd.get_dummies(df, columns=['purpose'])"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "B_UF64O7DvcK"
      },
      "source": [
        "df1['log.annual.inc'] = np.exp(df1['log.annual.inc'])"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "YWGZELt6D85J",
        "outputId": "b30a6314-7fd2-41e4-c1a5-f88ca409679c",
        "colab": {
          "base_uri": "https://localhost:8080/",
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      },
      "source": [
        "df1.head()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
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              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>credit.policy</th>\n",
              "      <th>int.rate</th>\n",
              "      <th>installment</th>\n",
              "      <th>log.annual.inc</th>\n",
              "      <th>dti</th>\n",
              "      <th>fico</th>\n",
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              "      <th>revol.bal</th>\n",
              "      <th>revol.util</th>\n",
              "      <th>inq.last.6mths</th>\n",
              "      <th>delinq.2yrs</th>\n",
              "      <th>pub.rec</th>\n",
              "      <th>not.fully.paid</th>\n",
              "      <th>purpose_all_other</th>\n",
              "      <th>purpose_credit_card</th>\n",
              "      <th>purpose_debt_consolidation</th>\n",
              "      <th>purpose_educational</th>\n",
              "      <th>purpose_home_improvement</th>\n",
              "      <th>purpose_major_purchase</th>\n",
              "      <th>purpose_small_business</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>1</td>\n",
              "      <td>0.1189</td>\n",
              "      <td>829.10</td>\n",
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              "      <td>19.48</td>\n",
              "      <td>737</td>\n",
              "      <td>5639.958333</td>\n",
              "      <td>28854</td>\n",
              "      <td>52.1</td>\n",
              "      <td>0</td>\n",
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              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>1</td>\n",
              "      <td>0.1071</td>\n",
              "      <td>228.22</td>\n",
              "      <td>65000.000073</td>\n",
              "      <td>14.29</td>\n",
              "      <td>707</td>\n",
              "      <td>2760.000000</td>\n",
              "      <td>33623</td>\n",
              "      <td>76.7</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>1</td>\n",
              "      <td>0.1357</td>\n",
              "      <td>366.86</td>\n",
              "      <td>31999.999943</td>\n",
              "      <td>11.63</td>\n",
              "      <td>682</td>\n",
              "      <td>4710.000000</td>\n",
              "      <td>3511</td>\n",
              "      <td>25.6</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>1</td>\n",
              "      <td>0.1008</td>\n",
              "      <td>162.34</td>\n",
              "      <td>85000.000385</td>\n",
              "      <td>8.10</td>\n",
              "      <td>712</td>\n",
              "      <td>2699.958333</td>\n",
              "      <td>33667</td>\n",
              "      <td>73.2</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>1</td>\n",
              "      <td>0.1426</td>\n",
              "      <td>102.92</td>\n",
              "      <td>80799.999636</td>\n",
              "      <td>14.97</td>\n",
              "      <td>667</td>\n",
              "      <td>4066.000000</td>\n",
              "      <td>4740</td>\n",
              "      <td>39.5</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "   credit.policy  int.rate  ...  purpose_major_purchase  purpose_small_business\n",
              "0              1    0.1189  ...                       0                       0\n",
              "1              1    0.1071  ...                       0                       0\n",
              "2              1    0.1357  ...                       0                       0\n",
              "3              1    0.1008  ...                       0                       0\n",
              "4              1    0.1426  ...                       0                       0\n",
              "\n",
              "[5 rows x 20 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 16
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "KryPmqY8CDYC",
        "outputId": "89cd88b2-2252-499f-cfdf-4134768c57ea",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "source": [
        "df.groupby('not.fully.paid')['not.fully.paid'].count()/len(df)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "not.fully.paid\n",
              "0    0.839946\n",
              "1    0.160054\n",
              "Name: not.fully.paid, dtype: float64"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 10
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "y8lVe87DCRZg",
        "outputId": "8bc70977-580d-452c-9ec4-456175c48f55",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 296
        }
      },
      "source": [
        "sns.set_style('darkgrid')\n",
        "sns.countplot(x='not.fully.paid', data=df)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7fe3fcbda6d8>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 11
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": "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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "coCYDHacLY1H"
      },
      "source": [
        "count_class_0, count_class_1 = df['not.fully.paid'].value_counts()"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "MOyJSAy4Mjgk"
      },
      "source": [
        "df_0 = df[df['not.fully.paid'] == 0]\n",
        "df_1 = df[df['not.fully.paid'] == 1]"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "hjhir6agM2IX"
      },
      "source": [
        "df_1_over = df_1.sample(count_class_0, replace=True)\n",
        "df_test_over = pd.concat([df_0, df_1_over], axis=0)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "SlXPkN6ONDqK",
        "outputId": "19873f66-ba67-4218-8f50-fe892198dc75",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "source": [
        "print('Random over-sampling:')\n",
        "print(df_test_over['not.fully.paid'].value_counts())"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Random over-sampling:\n",
            "1    8045\n",
            "0    8045\n",
            "Name: not.fully.paid, dtype: int64\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "EiTk1yd-NPOy",
        "outputId": "8f675a00-fa25-4db0-cac9-8d58092832db",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 296
        }
      },
      "source": [
        "#df_test_over['not.fully.paid'].value_counts().plot(kind='bar', title='Count (not.fully.paid)')\n",
        "\n",
        "sns.set_style('darkgrid')\n",
        "sns.countplot(x='not.fully.paid', data=df_test_over)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7fe3c3d0ab38>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 31
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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xYcMG4uPjKSkpAWDNmjXEx8ezYcMGpk2bxtKlSwHYt28fFRUVVFRUsGLFCubPn3/KJUdERMR6rdqzeOqpp77R4D6fjz//+c/cd999vPrqq4TDYbZt28Zzzz0HQHZ2NkVFRdxyyy1s2rSJBx54AICxY8dSWFgYOT+SkZFBXFwcffv2pV+/fuzevZurr776G2USEZGvr1Vl4fP5WLBgQeTTT9/73vd48skncbvdZ11v8eLFzJo1iy+++AI4+eW++Ph4YmNPbtbtdkfui2EYBr179z4ZKjYWu93O4cOHMQyD4cOHR8Z0uVym99KIibHhcFzcml9N5GvT3JLOysq52aqymD17NhMnTuSFF14A4I033mD27Nm88sorLa7z9ttvk5iYyFVXXcV7773XPmlbKRQKEwgcbdMYTqe9ndJIV9PWudVWmpvSEivf91pVFocOHWLKlCmRx5MnT2blypVnXWfHjh1s2rSJmpoajh8/zueff86iRYtoaGigubmZ2NhYfD4fLpcLOLnH8Nlnn+F2u2lububIkSP07NkTl8uFz+eLjGsYRmQdERHpGK06we1wOCgvLycUChEKhSgvL8fhcJx1nUceeYSamho2bdrEL3/5S0aNGsVzzz3HddddR2VlJQClpaWkpaUBkJaWRmlpKQCVlZWMGjUKm81GWloaFRUVNDY2UldXR21tLcOGDWvL7ywiIl9Tq8pi8eLFvPXWW6SkpJCamkplZSVPP/30N9rgrFmzeOWVV/B4PAQCAXJzcwHIyckhEAjg8Xh45ZVXePTRR4GTV7wdP348EyZM4O6772bu3LmRe2qIiEjHsIW/urH2WfzsZz/jiSeeICEhAYBAIMAzzzzzjT8lZbWmplC7HLsbOWtVOyWSrmL7kts5ePBIVDM4nXY+KRwa1QzS+Vwx94M2z82znbNo1Z7Fxx9/HCkKOHlYau/evW0KJSIi545WlcWJEycIBoORx4FAQF+MExE5j7Tq01B33nknU6dOZdy4cQCsX7+e++67z9JgIiLSebSqLLKysrjqqqvYtm0bAEVFRQwcONDSYCIi0nm0qiwABg4cqIIQETlPfe1LlIuIyPlHZSEiIqZUFiIiYkplISIiplQWIiJiSmUhIiKmVBYiImJKZSEiIqZUFiIiYkplISIiplQWIiJiyrKyOH78ODk5Odx8881kZGTwq1/9CoC6ujpyc3PxeDwUFBTQ2NgIQGNjIwUFBXg8HnJzczlw4EBkrOXLl+PxeBg7dixbtmyxKrKIiLTAsrKIi4tj5cqVvPHGG5SVlbFlyxZ27drF0qVLmTZtGhs2bCA+Pp6SkhIA1qxZQ3x8PBs2bGDatGksXboUgH379lFRUUFFRQUrVqxg/vz5upeGiEgHs6wsbDYbPXr0AKC5uZnm5mZsNhvbtm1j7NixAGRnZ1NdXQ3Apk2byM7OBmDs2LFs3bqVcDhMdXU1GRkZxMXF0bdvX/r168fu3butii0iImdg6TmLUCjEpEmTuP7667n++uvp27cv8fHxxMaevDK62+3GMAwADMOgd+/eAMTGxmK32zl8+DCGYeB2uyNjulyuyDoiItIxWn0/i28iJiaG8vJyGhoayM/PZ//+/VZu7r+2a8PhuLhDtiXnH80t6aysnJuWlsVX4uPjue6669i1axcNDQ00NzcTGxuLz+fD5XIBJ/cYPvvsM9xuN83NzRw5coSePXvicrnw+XyRsQzDiKzTklAoTCBwtE2ZnU57m9aXrqutc6utNDelJVa+71l2GOrQoUM0NDQAcOzYMd59910GDBjAddddR2VlJQClpaWkpaUBkJaWRmlpKQCVlZWMGjUKm81GWloaFRUVNDY2UldXR21tLcOGDbMqtoiInIFlexZ+v5/HH3+cUChEOBxm3Lhx3HTTTQwcOJCHH36YZcuWMXjwYHJzcwHIyclh1qxZeDweEhISeP755wEYNGgQ48ePZ8KECcTExDB37lxiYmKsii0iImdgC4fD4WiHaG9NTaF22R0bOWtVOyWSrmL7kts5ePBIVDM4nXY+KRwa1QzS+Vwx94M2z82oHIYSEZGuQ2UhIiKmVBYiImJKZSEiIqZUFiIiYkplISIiplQWIiJiSmUhIiKmVBYiImJKZSEiIqZUFiIiYkplISIiplQWIiJiSmUhIiKmVBYiImJKZSEiIqZUFiIiYsqysvjss8+47bbbmDBhAhkZGaxcuRKAQCBAXl4e6enp5OXlEQwGAQiHwyxcuBCPx0NmZiZ79uyJjFVaWkp6ejrp6emR+3SLiEjHsawsYmJiePzxx3nzzTf54x//yGuvvca+ffsoLi4mOTmZqqoqkpOTKS4uBqCmpoba2lqqqqpYsGAB8+bNA06WS1FREatXr2bNmjUUFRVFCkZERDqGZWWRlJTEkCFDALjkkkvo378/hmFQXV1NVlYWAFlZWWzcuBEgstxmszFixAgaGhrw+/14vV5SUlJwOBwkJCSQkpLCli1brIotIiJnENsRGzlw4AB79+5l+PDh1NfXk5SUBIDT6aS+vh4AwzBwu92RddxuN4ZhnLbc5XJhGMZZtxcTY8PhuNiC30QEzS3ptKycm5aXxRdffMGMGTN44oknuOSSS055zmazYbPZ2n2boVCYQOBom8ZwOu3tlEa6mrbOrbbS3JSWWPm+Z+mnoZqampgxYwaZmZmkp6cD0KtXL/x+PwB+v5/ExETg5B6Dz+eLrOvz+XC5XKctNwwDl8tlZWwREfkflpVFOBzmySefpH///uTl5UWWp6WlUVZWBkBZWRljxow5ZXk4HGbXrl3Y7XaSkpJITU3F6/USDAYJBoN4vV5SU1Otii0iImdg2WGo7du3U15ezpVXXsmkSZMAmDlzJvfccw8FBQWUlJTQp08fli1bBsDo0aPZvHkzHo+H7t27s3jxYgAcDgfTp08nJycHgPz8fBwOh1WxRUTkDGzhcDgc7RDtrakp1C7H7kbOWtVOiaSr2L7kdg4ePBLVDE6nnU8Kh0Y1g3Q+V8z9oM1zM2rnLEREpGtQWYiIiCmVhYiImFJZiIiIKZWFiIiYUlmIiIgplYWIiJhSWYiIiCmVhYiImFJZiIiIKZWFiIiYUlmIiIgplYWIiJhSWYiIiCmVhYiImFJZiIiIKZWFiIiYsqwsZs+eTXJyMhMnTowsCwQC5OXlkZ6eTl5eHsFgEDh5v+6FCxfi8XjIzMxkz549kXVKS0tJT08nPT2d0tJSq+KKiMhZWFYWkydPZsWKFacsKy4uJjk5maqqKpKTkykuLgagpqaG2tpaqqqqWLBgAfPmzQNOlktRURGrV69mzZo1FBUVRQpGREQ6jmVlcc0115CQkHDKsurqarKysgDIyspi48aNpyy32WyMGDGChoYG/H4/Xq+XlJQUHA4HCQkJpKSksGXLFqsii4hIC2I7cmP19fUkJSUB4HQ6qa+vB8AwDNxud+R1brcbwzBOW+5yuTAMw3Q7MTE2HI6L2zm9yEmaW9JZWTk3O7Qs/pvNZsNms1kydigUJhA42qYxnE57O6WRrqatc6utNDelJVa+73Xop6F69eqF3+8HwO/3k5iYCJzcY/D5fJHX+Xw+XC7XacsNw8DlcnVkZBERoYPLIi0tjbKyMgDKysoYM2bMKcvD4TC7du3CbreTlJREamoqXq+XYDBIMBjE6/WSmprakZFFRAQLD0PNnDmT999/n8OHD3PjjTfy4IMPcs8991BQUEBJSQl9+vRh2bJlAIwePZrNmzfj8Xjo3r07ixcvBsDhcDB9+nRycnIAyM/Px+FwWBVZRERaYAuHw+Foh2hvTU2hdjl2N3LWqnZKJF3F9iW3c/DgkahmcDrtfFI4NKoZpPO5Yu4HbZ6bneachYiInJtUFiIiYkplISIiplQWIiJiSmUhIiKmVBYiImJKZSEiIqZUFiIiYkplISIiplQWIiJiSmUhIiKmVBYiImJKZSEiIqZUFiIiYkplISIiplQWIiJiSmUhIiKmzpmyqKmpYezYsXg8HoqLi6MdR0TkvHJOlEUoFKKwsJAVK1ZQUVHBunXr2LdvX7RjiYicN86Jsti9ezf9+vWjb9++xMXFkZGRQXV1dbRjiYicN2KjHaA1DMPA7XZHHrtcLnbv3t3i67t1iznrjcdba/uS29s8hnQ97TG32uqKuR9EO4J0QlbOzXNiz0JERKLrnCgLl8uFz+eLPDYMA5fLFcVEIiLnl3OiLIYOHUptbS11dXU0NjZSUVFBWlpatGOJiJw3zolzFrGxscydO5e7776bUCjElClTGDRoULRjiYicN2zhcDgc7RAiItK5nROHoUREJLpUFiIiYkplIWely6xIZzR79mySk5OZOHFitKOcN1QW0iJdZkU6q8mTJ7NixYpoxzivqCykRbrMinRW11xzDQkJCdGOcV5RWUiLznSZFcMwophIRKJFZSEiIqZUFtIiXWZFRL6ispAW6TIrIvIVfYNbzmrz5s0sXrw4cpmV+++/P9qRRJg5cybvv/8+hw8fplevXjz44IPk5uZGO1aXprIQERFTOgwlIiKmVBYiImJKZSEiIqZUFiIiYkplISIiplQWIibWrl3b4mVODh06RG5uLllZWfz1r39tcYwXX3yR3/zmNwA8/vjjrF+/3pKsL7zwAu++++5py9977z3uvfdeS7Yp54dz4raqItFUWlrKoEGDzvjt9a1bt3LllVeyaNGiKCQ73UMPPRTtCNJFac9CzjsHDhxg/Pjx/PznPycjI4M777yTY8eOsXfvXn74wx+SmZlJfn4+wWCQ9evX8+GHH/Loo48yadIkjh07Fhln7969LFmyhOrq6shzV199deT59evX8/jjj7eYY+vWrUyfPj3y+J133iE/P/+UnOPGjeORRx5h/PjxzJgxgy+//BKAoqIipkyZwsSJE5kzZw5ffV3qv/daampqGDduHNnZ2WzYsKF9/vLkvKWykPPSv/71L37yk59QUVGB3W6nsrKSxx57jEcffZQ//elPXHnllRQVFTFu3Diuuuoqli5dSnl5ORdddFFkjMGDBzNjxgwmTJhw2nOtMWrUKPbv38+hQ4eAk4e7pkyZcspr/vnPf3LLLbfw1ltv0aNHD1577TUAbr31Vl5//XXWrVvHsWPHePvtt09Z7/jx48yZM4eXX36ZtWvXcvDgwW/y1yQSobKQ89Lll1/O4MGDARgyZAh1dXUcOXKEa6+9FoDs7OyznoNoDzabjUmTJvHGG2/Q0NDAzp07ufHGG095Te/evRk5ciQAN998M9u3bwdOnoPIzc0lMzOTbdu2nXZTqv3793P55ZfzrW99C5vNxs0332zp7yJdn85ZyHkpLi4u8nNMTAwNDQ3tvo3jx4+bvmby5Mncf//9xMXFMW7cOGJjT/0nabPZTnt8/Phx5s+fz+uvv07v3r158cUXW7UtkbbQnoUIYLfbiY+Pj+xNlJeXc8011wDQo0cPvvjii1aNc+mll/KPf/yDEydOsHHjRtPXu1wukpKSeOmll047BAXw73//m507dwKwbt06Ro4cGSmGnj178sUXX1BZWXnaev379+fTTz/lk08+AaCioqJV+UVaorIQ+Y9nnnmGZ599lszMTPbu3Rs52Zydnc0vfvGLyEnsF154ocXbyz7yyCPce++9/OhHP8LpdLZqu5mZmfTu3ZsBAwZgGAY//elPI899+9vf5ne/+x3jx4+noaGBH//4x8THx5Obm8vEiRO56667GDp06GljXnjhhRQWFnLPPfeQnZ1NYmLiN/gbEfl/uuqsSJQVFhYyePDg0y6xfeDAAe677z7WrVsXpWQi/097FiJRNHnyZD7++GMmTZoU7SgiZ6U9CxERMaU9CxERMaWyEBERUyoLERExpbIQERFTKgsRETH1fzOLO+Pg77lMAAAAAElFTkSuQmCC\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "biSAJf9NCoxR",
        "outputId": "cc39354e-4f4e-428a-b5a7-b1997e4330e1",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 404
        }
      },
      "source": [
        "df['revol.bal'].hist(figsize=[12,6], bins=50)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7fe3c3bcbf98>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 33
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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X8uXLtXjxYl1//fVqampSa2ur5syZox07dkiSlixZov379ysYDCo3N1dbt26VJHm9Xq1atUr19fWSpNWrV8vr9aZrXQAAAMCEsBzHcTI9xKWcPRvP6B7muetCKZ8f3latEyeG0zgR3LLHCWPIw33IxH3IxH3IxH3cksll72EGAAAAshWFGQAAADCgMAMAAAAGFGYAAADAgMIMAAAAGFCYAQAAAAMKMwAAAGBAYQYAAAAMKMwAAACAAYUZAAAAMKAwAwAAAAYUZgAAAMCAwgwAAAAYUJgBAAAAAwozAAAAYEBhBgAAAAwozAAAAIABhRkAAAAwoDADAAAABhRmAAAAwIDCDAAAABhQmAEAAAADCjMAAABgQGEGAAAADCjMAAAAgAGFGQAAADCgMAMAAAAGFGYAAADAgMIMAAAAGFCYAQAAAIOUC3M8Hldtba3+6Z/+SZJ09OhRNTQ0KBgMqqmpSSMjI5KkkZERNTU1KRgMqqGhQceOHUvc4/HHH1cwGFRlZaV6enomeCkAAADAxEu5MD/99NO65pprEr/evn27VqxYoX379ik/P1+tra2SpN27dys/P1/79u3TihUrtH37dknSkSNHFAqFFAqFtHPnTm3cuFHxeHyClwMAAABMrJQKcyQS0UsvvaT6+npJkuM4euWVV1RZWSlJqqurU2dnpySpq6tLdXV1kqTKykr19fXJcRx1dnaqurpaM2bMUElJia666iodPHgwHWsCAAAAJkxOKidt3bpVa9eu1RtvvCFJOnnypPLz85WTM3a53++XbduSJNu2NXv27LGb5+QoLy9PJ0+elG3bWrBgQeKePp8vcc2leDyWvN6Z419VhkylWacij2car7GLkIf7kIn7kIn7kIn7TIVMkhbm//zP/1RhYaE+/OEP62c/+9lkzJQQjzuKxU5P6nP+RVFR3rivydSs2cLrnclr7CLk4T5k4j5k4j5k4j5uycTU/ZIW5l/+8pfq6upSd3e3zpw5o1OnTmnLli0aGhrS6OiocnJyFIlE5PP5JI29c3z8+HH5/X6Njo5qeHhYV155pXw+nyKRSOK+tm0nrgEAAADcKuke5gcffFDd3d3q6urSN77xDS1atEhf//rXddNNN2nv3r2SpLa2NgUCAUlSIBBQW1ubJGnv3r1atGiRLMtSIBBQKBTSyMiIjh49qnA4rBtuuCGNSwMAAAAu3zv+HOa1a9fqiSeeUDAYVCwWU0NDgySpvr5esVhMwWBQTzzxhL74xS9KkubNm6dly5apqqpKn/vc57R+/Xp5PJ6JWQUAAACQJpbjOE6mh7iUs2fjGd3DPHddKOXzw9uqdeLEcBonglv2OGEMebgPmbgPmbgPmbiPWzIx7WHmm/4AAAAAAwozAAAAYEBhBgAAAAwozAAAAIABhRkAAAAwoDADAAAABhRmAAAAwIDCDAAAABhQmAEAAAADCjMAAABgQGEGAAAADCjMAAAAgAGFGQAAADCgMAMAAAAGFGYAAADAgMIMAAAAGFCYAQAAAAMKMwAAAGBAYQYAAAAMKMwAAACAAYUZAAAAMKAwAwAAAAYUZgAAAMCAwgwAAAAYUJgBAAAAAwozAAAAYEBhBgAAAAwozAAAAIABhRkAAAAwoDADAAAABkkL85kzZ1RfX69PfvKTqq6u1r//+79Lko4ePaqGhgYFg0E1NTVpZGREkjQyMqKmpiYFg0E1NDTo2LFjiXs9/vjjCgaDqqysVE9PT5qWBAAAAEycpIV5xowZeuqpp/SjH/1I7e3t6unpUX9/v7Zv364VK1Zo3759ys/PV2trqyRp9+7dys/P1759+7RixQpt375dknTkyBGFQiGFQiHt3LlTGzduVDweT+/qAAAAgMuUtDBblqX3ve99kqTR0VGNjo7Ksiy98sorqqyslCTV1dWps7NTktTV1aW6ujpJUmVlpfr6+uQ4jjo7O1VdXa0ZM2aopKREV111lQ4ePJiudQEAAAATIqU9zPF4XLfeeqs++tGP6qMf/ahKSkqUn5+vnJwcSZLf75dt25Ik27Y1e/ZsSVJOTo7y8vJ08uRJ2bYtv9+fuKfP50tcAwAAALhVTioneTwe7dmzR0NDQ1q9erVee+21dM/1/5/Xktc7c1KeayJMpVmnIo9nGq+xi5CH+5CJ+5CJ+5CJ+0yFTFIqzH+Rn5+vm266Sf39/RoaGtLo6KhycnIUiUTk8/kkjb1zfPz4cfn9fo2Ojmp4eFhXXnmlfD6fIpFI4l62bSeuuZR43FEsdvodLOvyFRXljfuaTM2aLbzembzGLkIe7kMm7kMm7kMm7uOWTEzdL+mWjNdff11DQ0OSpLfeeksvv/yyrrnmGt10003au3evJKmtrU2BQECSFAgE1NbWJknau3evFi1aJMuyFAgEFAqFNDIyoqNHjyocDuuGG2647MUBAAAA6ZT0HeZoNKp169YpHo/LcRzdcsstuvnmm3XttdfqgQce0I4dO3TdddepoaFBklRfX6+1a9cqGAyqoKBAjz76qCRp3rx5WrZsmaqqquTxeLR+/Xp5PJ70rg4AAAC4TJbjOE6mh7iUs2fjGd2SMXddKOXzw9uqdeLEcBonglt+ywZjyMN9yMR9yMR9yMR93JLJZW3JAAAAALIZhRkAAAAwoDADAAAABhRmAAAAwIDCDAAAABhQmAEAAAADCjMAAABgQGEGAAAADCjMAAAAgAGFGQAAADCgMAMAAAAGFGYAAADAgMIMAAAAGFCYAQAAAAMKMwAAAGBAYQYAAAAMKMwAAACAAYUZAAAAMKAwAwAAAAYUZgAAAMCAwgwAAAAYUJgBAAAAAwozAAAAYEBhBgAAAAwozAAAAIABhRkAAAAwoDADAAAABhRmAAAAwIDCDAAAABhQmAEAAAADCjMAAABgkLQwHz9+XHfddZeqqqpUXV2tp556SpIUi8XU2NioiooKNTY2anBwUJLkOI42b96sYDCompoaHTp0KHGvtrY2VVRUqKKiQm1tbWlaEgAAADBxkhZmj8ejdevW6YUXXtAPf/hDff/739eRI0fU0tKi0tJSdXR0qLS0VC0tLZKk7u5uhcNhdXR0aNOmTdqwYYOksYLd3NysXbt2affu3Wpubk6UbAAAAMCtkhbm4uJizZ8/X5J0xRVX6Oqrr5Zt2+rs7FRtba0kqba2Vi+++KIkJY5blqWFCxdqaGhI0WhUvb29Kisrk9frVUFBgcrKytTT05PGpQEAAACXL2c8Jx87dkz//d//rQULFmhgYEDFxcWSpKKiIg0MDEiSbNuW3+9PXOP3+2Xb9gXHfT6fbNs2Pp/HY8nrnTmeETNqKs06FXk803iNXYQ83IdM3IdM3IdM3GcqZJJyYX7jjTd033336V//9V91xRVXnPeYZVmyLGvCh4vHHcVipyf8vqkoKsob9zWZmjVbeL0zeY1dhDzch0zch0zch0zcxy2ZmLpfSp+ScfbsWd13332qqalRRUWFJGnWrFmKRqOSpGg0qsLCQklj7xxHIpHEtZFIRD6f74Ljtm3L5/ONfzUAAADAJEpamB3H0Ze//GVdffXVamxsTBwPBAJqb2+XJLW3t2vp0qXnHXccR/39/crLy1NxcbHKy8vV29urwcFBDQ4Oqre3V+Xl5WlaFgAAADAxkm7J+MUvfqE9e/boQx/6kG699VZJ0po1a7Ry5Uo1NTWptbVVc+bM0Y4dOyRJS5Ys0f79+xUMBpWbm6utW7dKkrxer1atWqX6+npJ0urVq+X1etO1LgAAAGBCWI7jOJke4lLOno1ndA/z3HWhlM8Pb6vWiRPDaZwIbtnjhDHk4T5k4j5k4j5k4j5uyeSy9zADAAAA2YrCDAAAABhQmAEAAAADCjMAAABgQGEGAAAADCjMAAAAgAGFGQAAADCgMAMAAAAGFGYAAADAgMIMAAAAGFCYAQAAAAMKMwAAAGBAYQYAAAAMKMwAAACAAYUZAAAAMKAwAwAAAAYUZgAAAMCAwgwAAAAYUJgBAAAAAwozAAAAYEBhBgAAAAwozAAAAIABhRkAAAAwoDADAAAABhRmAAAAwIDCDAAAABhQmAEAAAADCjMAAABgQGEGAAAADCjMAAAAgEHSwvzQQw+ptLRUy5cvTxyLxWJqbGxURUWFGhsbNTg4KElyHEebN29WMBhUTU2NDh06lLimra1NFRUVqqioUFtbWxqWAgAAAEy8pIX5tttu086dO8871tLSotLSUnV0dKi0tFQtLS2SpO7uboXDYXV0dGjTpk3asGGDpLGC3dzcrF27dmn37t1qbm5OlOx3i7fOxlVUlJfyX1fk52Z6ZAAAAKQgJ9kJN954o44dO3besc7OTj3zzDOSpNraWt11111au3atOjs7VVtbK8uytHDhQg0NDSkajerAgQMqKyuT1+uVJJWVlamnp+e8d62nuvdO92juulDK54e3VetUGucBAADAxEhamC9mYGBAxcXFkqSioiINDAxIkmzblt/vT5zn9/tl2/YFx30+n2zbTvo8Ho8lr3fmOxlxSng3ry0dPJ5pvGYuQh7uQybuQybuQybuMxUyeUeF+e0sy5JlWRMxywXicUex2Om03DuZoqK8tD9HptY2VXm9M3nNXIQ83IdM3IdM3IdM3MctmZi63zv6lIxZs2YpGo1KkqLRqAoLCyWNvXMciUQS50UiEfl8vguO27Ytn8/3Tp4aAAAAmFTvqDAHAgG1t7dLktrb27V06dLzjjuOo/7+fuXl5am4uFjl5eXq7e3V4OCgBgcH1dvbq/Ly8olbBQAAAJAmSbdkrFmzRgcOHNDJkye1ePFi3XvvvVq5cqWamprU2tqqOXPmaMeOHZKkJUuWaP/+/QoGg8rNzdXWrVslSV6vV6tWrVJ9fb0kafXq1Yk/AAgAAAC4WdLC/I1vfOOix5966qkLjlmWpX/7t3+76Pn19fWJwgwAAABMFXzTHwAAAGBAYQYAAAAMKMwAAACAAYUZAAAAMKAwAwAAAAYUZgAAAMCAwgwAAAAYUJgBAAAAAwozAAAAYEBhBgAAAAwozAAAAIABhRkAAAAwoDADAAAABhRmAAAAwIDCDAAAABhQmAEAAAADCjMAAABgQGEGAAAADCjMAAAAgAGFGQAAADCgMAMAAAAGFGYAAADAICfTA2Srt87GVVSUl/L5b54Z1amhN9M4EQAAAC6Gwpwh753u0dx1oZTPD2+r1qk0zgMAAICLY0sGAAAAYEBhBgAAAAwozAAAAIABhRkAAAAwoDADAAAABnxKxhTBx9ABAABkxqQX5u7ubm3ZskXnzp1TQ0ODVq5cOdkjTEl8DB0AAEBmTOqWjHg8rq985SvauXOnQqGQnn/+eR05cmQyRwAAAADGZVLfYT548KCuuuoqlZSUSJKqq6vV2dmpa6+9djLHyArj3cLxl2veO92T8vls+wAAANlgUguzbdvy+/2JX/t8Ph08eHAyR8ga493CIY1t4xjPNYc33TKuUj7eQn6x803PN977v5Nr+J8EAACyj+U4jjNZT/aTn/xEPT092rJliySpvb1dBw8e1Pr16ydrBAAAAGBcJnUPs8/nUyQSSfzatm35fL7JHAEAAAAYl0ktzNdff73C4bCOHj2qkZERhUIhBQKByRwBAAAAGJdJ3cOck5Oj9evX63Of+5zi8bg+9alPad68eZM5AgAAADAuk7qHGQAAAJhq+GpsAAAAwIDCDAAAABhkfWHu7u5WZWWlgsGgWlpaLnh8ZGRETU1NCgaDamho0LFjxzIwZfZIlscTTzyhqqoq1dTU6B//8R/1pz/9KQNTZpdkmfzF3r179dd//df6r//6r0mcLjulkskLL7ygqqoqVVdX68EHH5zkCbNPskz+/Oc/66677lJtba1qamq0f//+DEyZPR566CGVlpZq+fLlF33ccRxt3rxZwWBQNTU1OnTo0CRPmH2SZfKjH/1INTU1qqmp0R133KHDhw9P8oRJOFlsdHTUWbp0qfPHP/7ROXPmjFNTU+P89re/Pe+c7373u84jjzziOI7jPP/8887999+fiVGzQip59PX1OadPn3Ycx3G+973vkUeapZKJ4zjO8PCw8+lPf9ppaGhwDh48mIFJs0cqmfz+9793br31VicWizmO4zj/+7//m4lRs0YqmUMM/6EAAAYPSURBVDz88MPO9773PcdxHOe3v/2tc/PNN2di1Kxx4MAB59e//rVTXV190cdfeukl5+6773bOnTvnvPrqq059ff0kT5h9kmXyi1/8IvHfrJdeesl1mWT1O8xv/6ruGTNmJL6q++26urpUV1cnSaqsrFRfX58c/pxkWqSSx6JFi5SbmytJWrhw4Xmf642Jl0omkvTNb35Tn//85/We97wnA1Nml1Qy2bVrlz7zmc+ooKBAkjRr1qxMjJo1UsnEsiydOnVKkjQ8PKzi4uJMjJo1brzxxsQ//xfT2dmp2tpaWZalhQsXamhoSNFodBInzD7JMvnIRz6SeNyNP9+zujBf7Ku6bdu+4JzZs2dLGvtYvLy8PJ08eXJS58wWqeTxdq2trVq8ePFkjJa1Usnk0KFDikQi+vjHPz7J02WnVDIJh8P6/e9/rzvuuEN/93d/p+7u7skeM6ukksk999yj5557TosXL9bKlSv18MMPT/aYeJv/m5nf7zf+vMHkcuPP90n9HGZgouzZs0e//vWv9d3vfjfTo2S1c+fOadu2bfrqV7+a6VHwNvF4XH/4wx/0zDPPKBKJ6M4779Rzzz2n/Pz8TI+WtUKhkOrq6vTZz35Wr776qr70pS/p+eef17RpWf2+FXCBV155Ra2trfr+97+f6VHOk9X/pqbyVd0+n0/Hjx+XJI2Ojmp4eFhXXnnlpM6ZLVL96vSXX35Z3/nOd/TYY49pxowZkzli1kmWyRtvvKH/+Z//0T/8wz8oEAiov79fX/jCF/iDf2mU6n+3AoGApk+frpKSEs2dO1fhcHiSJ80eqWTS2tqqZcuWSZL+5m/+RmfOnOF3KzPo/2YWiUQu+vMGk+vw4cN6+OGH9e1vf9t1XSurC3MqX9UdCATU1tYmaexTABYtWiTLsjIx7rteKnn85je/0fr16/XYY4+xL3MSJMskLy9PP/vZz9TV1aWuri4tXLhQjz32mK6//voMTv3ulsq/J5/4xCd04MABSdLrr7+ucDiskpKSTIybFVLJZPbs2err65Mk/e53v9OZM2dUWFiYiXGhsZ/t7e3tchxH/f39ysvLY195hv35z3/Wvffeq6997Wv6q7/6q0yPc4Gs3pJxqa/q/uY3v6kPf/jDWrp0qerr67V27VoFg0EVFBTo0UcfzfTY71qp5PG1r31Np0+f1v333y9p7IfQd77znQxP/u6VSiaYXKlk8rGPfUw//elPVVVVJY/Hoy996Uuue7fm3SSVTNatW6eHH35YTz75pCzL0rZt23jzJY3WrFmjAwcO6OTJk1q8eLHuvfdejY6OSpL+/u//XkuWLNH+/fsVDAaVm5urrVu3Znjid79kmXzrW99SLBbTxo0bJUkej0fPPvtsJkc+D1+NDQAAABhk9ZYMAAAAIBkKMwAAAGBAYQYAAAAMKMwAAACAAYUZAAAAU9ZDDz2k0tJSLV++PKXzX3jhBVVVVam6uloPPvhgStdk9cfKAQAAYGq77bbbdOedd+pf/uVfkp4bDofV0tKiH/zgByooKNDAwEBKz8E7zAAAAJiybrzxRhUUFJx37I9//KPuvvtu3Xbbbfr0pz+t3/3ud5KkXbt26TOf+Uzi/FS/BI13mAEAAPCu8sgjj2jjxo2aO3eufvWrX2njxo16+umnFQ6HJUl33HGHzp07p3vuuUeLFy9Oej8KMwAAAN413njjDb366quJbwWWpJGREUlSPB7XH/7wBz3zzDOKRCK688479dxzzyk/P994TwozAAAA3jUcx1F+fr727NlzwWM+n08LFizQ9OnTVVJSorlz5yocDuuGG24w3pM9zAAAAHjXuOKKK/TBD35QP/7xjyWNFejDhw9Lkj7xiU/owIEDkqTXX39d4XBYJSUlSe9pOY7jpG9kAAAAIH3WrFmjAwcO6OTJk5o1a5buvfdeLVq0SBs2bNCJEyc0Ojqqqqoq3XPPPXIcR9u2bVNPT488Ho/++Z//WdXV1Umfg8IMAAAAGLAlAwAAADCgMAMAAAAGFGYAAADAgMIMAAAAGFCYAQAAAAMKMwAAAGBAYQYAAAAMKMwAAACAwf8D/MHD12CtHGUAAAAASUVORK5CYII=\n",
            "text/plain": [
              "<Figure size 864x432 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "1dcn6dnMUGsk"
      },
      "source": [
        "df1=pd.get_dummies(df, columns=['purpose'])"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "xtwq20KCMypy",
        "outputId": "df106e83-5e0e-4d46-de70-162da0389328",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 405
        }
      },
      "source": [
        "plt.figure(figsize=(10,6))\n",
        "df[df['credit.policy']==1]['fico'].hist(alpha=0.5,color='blue',bins=30,label='Credit.Policy=1')\n",
        "df[df['credit.policy']==0]['fico'].hist(alpha=0.5,color='red',bins=30,label='Credit.Policy=0')\n",
        "plt.legend()\n",
        "plt.xlabel('FICO')"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Text(0.5, 0, 'FICO')"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 19
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 720x432 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "9AVf5OdbQvSU",
        "outputId": "d9eb0f9b-1823-4b70-ce0f-309b8e97d31e",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 405
        }
      },
      "source": [
        "plt.figure(figsize=(10,6))\n",
        "df[df['not.fully.paid']==1]['fico'].hist(alpha=0.5,color='blue',\n",
        "                                              bins=30,label='not.fully.paid=1')\n",
        "df[df['not.fully.paid']==0]['fico'].hist(alpha=0.5,color='red',\n",
        "                                              bins=30,label='not.fully.paid=0')\n",
        "plt.legend()\n",
        "plt.xlabel('FICO')"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Text(0.5, 0, 'FICO')"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 20
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 720x432 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "zcEjnMAFRAGI",
        "outputId": "497270c3-acf6-4b1a-b8f7-a81b1db6a9bb",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 462
        }
      },
      "source": [
        "plt.figure(figsize=(11,7))\n",
        "sns.countplot(x='purpose',hue='not.fully.paid',data=df,palette='Set1')"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7fb013a2ce80>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 21
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 792x504 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "aQquyZ44RFio",
        "outputId": "2915f44e-ad23-4a58-8170-a95790a9dde1",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 458
        }
      },
      "source": [
        "sns.jointplot(x='fico',y='int.rate',data=df,color='purple')"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<seaborn.axisgrid.JointGrid at 0x7fb01263fd68>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 23
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x432 with 3 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "4CKrqyaBRLl1",
        "outputId": "17b223dd-fd62-41fa-a63e-90ce729aa2f3",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 417
        }
      },
      "source": [
        "plt.figure(figsize=(11,7))\n",
        "sns.lmplot(y='int.rate',x='fico',data=df,hue='credit.policy',\n",
        "           col='not.fully.paid',palette='Set1')"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<seaborn.axisgrid.FacetGrid at 0x7fb00dcdf668>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 28
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 792x504 with 0 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 789.25x360 with 2 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "1FwSDb_URUhc"
      },
      "source": [
        "cat_feats = ['purpose']\n",
        "#cat_feats =df_test_over"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "a2iTUQ1PRYEz"
      },
      "source": [
        "#final_data = pd.get_dummies(df,columns=cat_feats,drop_first=True)\n",
        "final_data = pd.get_dummies(df_test_over,columns=cat_feats,drop_first=True)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "f89bHKqcRhDh",
        "outputId": "c06c6599-2f85-4654-ab0c-616a27fff28e",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 666
        }
      },
      "source": [
        "final_data.info()\n",
        "final_data.head()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "<class 'pandas.core.frame.DataFrame'>\n",
            "Int64Index: 16090 entries, 0 to 182\n",
            "Data columns (total 19 columns):\n",
            " #   Column                      Non-Null Count  Dtype  \n",
            "---  ------                      --------------  -----  \n",
            " 0   credit.policy               16090 non-null  int64  \n",
            " 1   int.rate                    16090 non-null  float64\n",
            " 2   installment                 16090 non-null  float64\n",
            " 3   log.annual.inc              16090 non-null  float64\n",
            " 4   dti                         16090 non-null  float64\n",
            " 5   fico                        16090 non-null  int64  \n",
            " 6   days.with.cr.line           16090 non-null  float64\n",
            " 7   revol.bal                   16090 non-null  int64  \n",
            " 8   revol.util                  16090 non-null  float64\n",
            " 9   inq.last.6mths              16090 non-null  int64  \n",
            " 10  delinq.2yrs                 16090 non-null  int64  \n",
            " 11  pub.rec                     16090 non-null  int64  \n",
            " 12  not.fully.paid              16090 non-null  int64  \n",
            " 13  purpose_credit_card         16090 non-null  uint8  \n",
            " 14  purpose_debt_consolidation  16090 non-null  uint8  \n",
            " 15  purpose_educational         16090 non-null  uint8  \n",
            " 16  purpose_home_improvement    16090 non-null  uint8  \n",
            " 17  purpose_major_purchase      16090 non-null  uint8  \n",
            " 18  purpose_small_business      16090 non-null  uint8  \n",
            "dtypes: float64(6), int64(7), uint8(6)\n",
            "memory usage: 2.1 MB\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "execute_result",
          "data": {
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              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>credit.policy</th>\n",
              "      <th>int.rate</th>\n",
              "      <th>installment</th>\n",
              "      <th>log.annual.inc</th>\n",
              "      <th>dti</th>\n",
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              "      <th>purpose_major_purchase</th>\n",
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              "      <td>14.29</td>\n",
              "      <td>707</td>\n",
              "      <td>2760.000000</td>\n",
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              "      <td>76.7</td>\n",
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              "      <th>3</th>\n",
              "      <td>1</td>\n",
              "      <td>0.1008</td>\n",
              "      <td>162.34</td>\n",
              "      <td>11.350407</td>\n",
              "      <td>8.10</td>\n",
              "      <td>712</td>\n",
              "      <td>2699.958333</td>\n",
              "      <td>33667</td>\n",
              "      <td>73.2</td>\n",
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              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>1</td>\n",
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              "      <td>102.92</td>\n",
              "      <td>11.299732</td>\n",
              "      <td>14.97</td>\n",
              "      <td>667</td>\n",
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              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "   credit.policy  int.rate  ...  purpose_major_purchase  purpose_small_business\n",
              "0              1    0.1189  ...                       0                       0\n",
              "1              1    0.1071  ...                       0                       0\n",
              "2              1    0.1357  ...                       0                       0\n",
              "3              1    0.1008  ...                       0                       0\n",
              "4              1    0.1426  ...                       0                       0\n",
              "\n",
              "[5 rows x 19 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 41
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "sD5n5MrtRCOT",
        "outputId": "6215fc2b-a33d-405c-f0e0-2ddfec426fda",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 658
        }
      },
      "source": [
        "final_data.corr()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
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              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>credit.policy</th>\n",
              "      <th>int.rate</th>\n",
              "      <th>installment</th>\n",
              "      <th>log.annual.inc</th>\n",
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              "      <th>purpose_major_purchase</th>\n",
              "      <th>purpose_small_business</th>\n",
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              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>credit.policy</th>\n",
              "      <td>1.000000</td>\n",
              "      <td>-0.297823</td>\n",
              "      <td>0.051720</td>\n",
              "      <td>0.017306</td>\n",
              "      <td>-0.115743</td>\n",
              "      <td>0.381174</td>\n",
              "      <td>0.094415</td>\n",
              "      <td>-0.196850</td>\n",
              "      <td>-0.111472</td>\n",
              "      <td>-0.550498</td>\n",
              "      <td>-0.049842</td>\n",
              "      <td>-0.062145</td>\n",
              "      <td>-0.191957</td>\n",
              "      <td>0.011830</td>\n",
              "      <td>0.016604</td>\n",
              "      <td>-0.044384</td>\n",
              "      <td>0.007923</td>\n",
              "      <td>0.038807</td>\n",
              "      <td>-0.010269</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>int.rate</th>\n",
              "      <td>-0.297823</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.276784</td>\n",
              "      <td>0.079726</td>\n",
              "      <td>0.212696</td>\n",
              "      <td>-0.682043</td>\n",
              "      <td>-0.105674</td>\n",
              "      <td>0.084220</td>\n",
              "      <td>0.425322</td>\n",
              "      <td>0.186856</td>\n",
              "      <td>0.140727</td>\n",
              "      <td>0.109875</td>\n",
              "      <td>0.214618</td>\n",
              "      <td>-0.043927</td>\n",
              "      <td>0.087384</td>\n",
              "      <td>-0.012696</td>\n",
              "      <td>-0.040770</td>\n",
              "      <td>-0.069206</td>\n",
              "      <td>0.179082</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>installment</th>\n",
              "      <td>0.051720</td>\n",
              "      <td>0.276784</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.475839</td>\n",
              "      <td>0.025765</td>\n",
              "      <td>0.099373</td>\n",
              "      <td>0.173349</td>\n",
              "      <td>0.262401</td>\n",
              "      <td>0.056755</td>\n",
              "      <td>-0.023376</td>\n",
              "      <td>0.001067</td>\n",
              "      <td>-0.024805</td>\n",
              "      <td>0.054657</td>\n",
              "      <td>0.006334</td>\n",
              "      <td>0.108572</td>\n",
              "      <td>-0.089165</td>\n",
              "      <td>0.035369</td>\n",
              "      <td>-0.048940</td>\n",
              "      <td>0.189898</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>log.annual.inc</th>\n",
              "      <td>0.017306</td>\n",
              "      <td>0.079726</td>\n",
              "      <td>0.475839</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>-0.029029</td>\n",
              "      <td>0.109645</td>\n",
              "      <td>0.339417</td>\n",
              "      <td>0.402420</td>\n",
              "      <td>0.071466</td>\n",
              "      <td>0.038108</td>\n",
              "      <td>0.012692</td>\n",
              "      <td>0.015813</td>\n",
              "      <td>-0.049353</td>\n",
              "      <td>0.080521</td>\n",
              "      <td>-0.040538</td>\n",
              "      <td>-0.124818</td>\n",
              "      <td>0.108532</td>\n",
              "      <td>-0.029034</td>\n",
              "      <td>0.129374</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>dti</th>\n",
              "      <td>-0.115743</td>\n",
              "      <td>0.212696</td>\n",
              "      <td>0.025765</td>\n",
              "      <td>-0.029029</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>-0.230042</td>\n",
              "      <td>0.091284</td>\n",
              "      <td>0.180716</td>\n",
              "      <td>0.327835</td>\n",
              "      <td>0.038546</td>\n",
              "      <td>-0.043627</td>\n",
              "      <td>0.020912</td>\n",
              "      <td>0.047659</td>\n",
              "      <td>0.074282</td>\n",
              "      <td>0.180988</td>\n",
              "      <td>-0.038983</td>\n",
              "      <td>-0.096849</td>\n",
              "      <td>-0.081550</td>\n",
              "      <td>-0.072319</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>fico</th>\n",
              "      <td>0.381174</td>\n",
              "      <td>-0.682043</td>\n",
              "      <td>0.099373</td>\n",
              "      <td>0.109645</td>\n",
              "      <td>-0.230042</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.257190</td>\n",
              "      <td>0.008128</td>\n",
              "      <td>-0.508692</td>\n",
              "      <td>-0.193631</td>\n",
              "      <td>-0.198609</td>\n",
              "      <td>-0.161679</td>\n",
              "      <td>-0.209924</td>\n",
              "      <td>-0.005327</td>\n",
              "      <td>-0.147663</td>\n",
              "      <td>-0.018454</td>\n",
              "      <td>0.093730</td>\n",
              "      <td>0.068743</td>\n",
              "      <td>0.071630</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>days.with.cr.line</th>\n",
              "      <td>0.094415</td>\n",
              "      <td>-0.105674</td>\n",
              "      <td>0.173349</td>\n",
              "      <td>0.339417</td>\n",
              "      <td>0.091284</td>\n",
              "      <td>0.257190</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.263218</td>\n",
              "      <td>0.017606</td>\n",
              "      <td>-0.027085</td>\n",
              "      <td>0.072671</td>\n",
              "      <td>0.065443</td>\n",
              "      <td>-0.038185</td>\n",
              "      <td>0.060660</td>\n",
              "      <td>-0.012240</td>\n",
              "      <td>-0.050879</td>\n",
              "      <td>0.074358</td>\n",
              "      <td>-0.021521</td>\n",
              "      <td>0.040086</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>revol.bal</th>\n",
              "      <td>-0.196850</td>\n",
              "      <td>0.084220</td>\n",
              "      <td>0.262401</td>\n",
              "      <td>0.402420</td>\n",
              "      <td>0.180716</td>\n",
              "      <td>0.008128</td>\n",
              "      <td>0.263218</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.182388</td>\n",
              "      <td>0.032254</td>\n",
              "      <td>-0.036166</td>\n",
              "      <td>-0.038701</td>\n",
              "      <td>0.061865</td>\n",
              "      <td>0.062449</td>\n",
              "      <td>-0.010192</td>\n",
              "      <td>-0.033710</td>\n",
              "      <td>-0.009100</td>\n",
              "      <td>-0.058115</td>\n",
              "      <td>0.100754</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>revol.util</th>\n",
              "      <td>-0.111472</td>\n",
              "      <td>0.425322</td>\n",
              "      <td>0.056755</td>\n",
              "      <td>0.071466</td>\n",
              "      <td>0.327835</td>\n",
              "      <td>-0.508692</td>\n",
              "      <td>0.017606</td>\n",
              "      <td>0.182388</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>-0.024732</td>\n",
              "      <td>-0.052280</td>\n",
              "      <td>0.083530</td>\n",
              "      <td>0.111527</td>\n",
              "      <td>0.093349</td>\n",
              "      <td>0.193018</td>\n",
              "      <td>-0.063756</td>\n",
              "      <td>-0.112292</td>\n",
              "      <td>-0.114816</td>\n",
              "      <td>-0.058217</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>inq.last.6mths</th>\n",
              "      <td>-0.550498</td>\n",
              "      <td>0.186856</td>\n",
              "      <td>-0.023376</td>\n",
              "      <td>0.038108</td>\n",
              "      <td>0.038546</td>\n",
              "      <td>-0.193631</td>\n",
              "      <td>-0.027085</td>\n",
              "      <td>0.032254</td>\n",
              "      <td>-0.024732</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.010637</td>\n",
              "      <td>0.100161</td>\n",
              "      <td>0.176185</td>\n",
              "      <td>-0.043535</td>\n",
              "      <td>-0.053166</td>\n",
              "      <td>0.043586</td>\n",
              "      <td>0.050534</td>\n",
              "      <td>0.001085</td>\n",
              "      <td>0.037138</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>delinq.2yrs</th>\n",
              "      <td>-0.049842</td>\n",
              "      <td>0.140727</td>\n",
              "      <td>0.001067</td>\n",
              "      <td>0.012692</td>\n",
              "      <td>-0.043627</td>\n",
              "      <td>-0.198609</td>\n",
              "      <td>0.072671</td>\n",
              "      <td>-0.036166</td>\n",
              "      <td>-0.052280</td>\n",
              "      <td>0.010637</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>-0.019618</td>\n",
              "      <td>0.018838</td>\n",
              "      <td>-0.005731</td>\n",
              "      <td>-0.025622</td>\n",
              "      <td>0.000335</td>\n",
              "      <td>-0.019792</td>\n",
              "      <td>0.016299</td>\n",
              "      <td>0.020961</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>pub.rec</th>\n",
              "      <td>-0.062145</td>\n",
              "      <td>0.109875</td>\n",
              "      <td>-0.024805</td>\n",
              "      <td>0.015813</td>\n",
              "      <td>0.020912</td>\n",
              "      <td>-0.161679</td>\n",
              "      <td>0.065443</td>\n",
              "      <td>-0.038701</td>\n",
              "      <td>0.083530</td>\n",
              "      <td>0.100161</td>\n",
              "      <td>-0.019618</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.069449</td>\n",
              "      <td>0.032821</td>\n",
              "      <td>0.041865</td>\n",
              "      <td>-0.016984</td>\n",
              "      <td>0.014983</td>\n",
              "      <td>-0.026522</td>\n",
              "      <td>-0.012678</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>not.fully.paid</th>\n",
              "      <td>-0.191957</td>\n",
              "      <td>0.214618</td>\n",
              "      <td>0.054657</td>\n",
              "      <td>-0.049353</td>\n",
              "      <td>0.047659</td>\n",
              "      <td>-0.209924</td>\n",
              "      <td>-0.038185</td>\n",
              "      <td>0.061865</td>\n",
              "      <td>0.111527</td>\n",
              "      <td>0.176185</td>\n",
              "      <td>0.018838</td>\n",
              "      <td>0.069449</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>-0.060638</td>\n",
              "      <td>-0.020242</td>\n",
              "      <td>0.035096</td>\n",
              "      <td>0.003760</td>\n",
              "      <td>-0.039428</td>\n",
              "      <td>0.100320</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>purpose_credit_card</th>\n",
              "      <td>0.011830</td>\n",
              "      <td>-0.043927</td>\n",
              "      <td>0.006334</td>\n",
              "      <td>0.080521</td>\n",
              "      <td>0.074282</td>\n",
              "      <td>-0.005327</td>\n",
              "      <td>0.060660</td>\n",
              "      <td>0.062449</td>\n",
              "      <td>0.093349</td>\n",
              "      <td>-0.043535</td>\n",
              "      <td>-0.005731</td>\n",
              "      <td>0.032821</td>\n",
              "      <td>-0.060638</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>-0.304569</td>\n",
              "      <td>-0.076040</td>\n",
              "      <td>-0.097590</td>\n",
              "      <td>-0.075497</td>\n",
              "      <td>-0.110817</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>purpose_debt_consolidation</th>\n",
              "      <td>0.016604</td>\n",
              "      <td>0.087384</td>\n",
              "      <td>0.108572</td>\n",
              "      <td>-0.040538</td>\n",
              "      <td>0.180988</td>\n",
              "      <td>-0.147663</td>\n",
              "      <td>-0.012240</td>\n",
              "      <td>-0.010192</td>\n",
              "      <td>0.193018</td>\n",
              "      <td>-0.053166</td>\n",
              "      <td>-0.025622</td>\n",
              "      <td>0.041865</td>\n",
              "      <td>-0.020242</td>\n",
              "      <td>-0.304569</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>-0.171326</td>\n",
              "      <td>-0.219881</td>\n",
              "      <td>-0.170104</td>\n",
              "      <td>-0.249684</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>purpose_educational</th>\n",
              "      <td>-0.044384</td>\n",
              "      <td>-0.012696</td>\n",
              "      <td>-0.089165</td>\n",
              "      <td>-0.124818</td>\n",
              "      <td>-0.038983</td>\n",
              "      <td>-0.018454</td>\n",
              "      <td>-0.050879</td>\n",
              "      <td>-0.033710</td>\n",
              "      <td>-0.063756</td>\n",
              "      <td>0.043586</td>\n",
              "      <td>0.000335</td>\n",
              "      <td>-0.016984</td>\n",
              "      <td>0.035096</td>\n",
              "      <td>-0.076040</td>\n",
              "      <td>-0.171326</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>-0.054896</td>\n",
              "      <td>-0.042469</td>\n",
              "      <td>-0.062337</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>purpose_home_improvement</th>\n",
              "      <td>0.007923</td>\n",
              "      <td>-0.040770</td>\n",
              "      <td>0.035369</td>\n",
              "      <td>0.108532</td>\n",
              "      <td>-0.096849</td>\n",
              "      <td>0.093730</td>\n",
              "      <td>0.074358</td>\n",
              "      <td>-0.009100</td>\n",
              "      <td>-0.112292</td>\n",
              "      <td>0.050534</td>\n",
              "      <td>-0.019792</td>\n",
              "      <td>0.014983</td>\n",
              "      <td>0.003760</td>\n",
              "      <td>-0.097590</td>\n",
              "      <td>-0.219881</td>\n",
              "      <td>-0.054896</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>-0.054505</td>\n",
              "      <td>-0.080004</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>purpose_major_purchase</th>\n",
              "      <td>0.038807</td>\n",
              "      <td>-0.069206</td>\n",
              "      <td>-0.048940</td>\n",
              "      <td>-0.029034</td>\n",
              "      <td>-0.081550</td>\n",
              "      <td>0.068743</td>\n",
              "      <td>-0.021521</td>\n",
              "      <td>-0.058115</td>\n",
              "      <td>-0.114816</td>\n",
              "      <td>0.001085</td>\n",
              "      <td>0.016299</td>\n",
              "      <td>-0.026522</td>\n",
              "      <td>-0.039428</td>\n",
              "      <td>-0.075497</td>\n",
              "      <td>-0.170104</td>\n",
              "      <td>-0.042469</td>\n",
              "      <td>-0.054505</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>-0.061892</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>purpose_small_business</th>\n",
              "      <td>-0.010269</td>\n",
              "      <td>0.179082</td>\n",
              "      <td>0.189898</td>\n",
              "      <td>0.129374</td>\n",
              "      <td>-0.072319</td>\n",
              "      <td>0.071630</td>\n",
              "      <td>0.040086</td>\n",
              "      <td>0.100754</td>\n",
              "      <td>-0.058217</td>\n",
              "      <td>0.037138</td>\n",
              "      <td>0.020961</td>\n",
              "      <td>-0.012678</td>\n",
              "      <td>0.100320</td>\n",
              "      <td>-0.110817</td>\n",
              "      <td>-0.249684</td>\n",
              "      <td>-0.062337</td>\n",
              "      <td>-0.080004</td>\n",
              "      <td>-0.061892</td>\n",
              "      <td>1.000000</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "                            credit.policy  ...  purpose_small_business\n",
              "credit.policy                    1.000000  ...               -0.010269\n",
              "int.rate                        -0.297823  ...                0.179082\n",
              "installment                      0.051720  ...                0.189898\n",
              "log.annual.inc                   0.017306  ...                0.129374\n",
              "dti                             -0.115743  ...               -0.072319\n",
              "fico                             0.381174  ...                0.071630\n",
              "days.with.cr.line                0.094415  ...                0.040086\n",
              "revol.bal                       -0.196850  ...                0.100754\n",
              "revol.util                      -0.111472  ...               -0.058217\n",
              "inq.last.6mths                  -0.550498  ...                0.037138\n",
              "delinq.2yrs                     -0.049842  ...                0.020961\n",
              "pub.rec                         -0.062145  ...               -0.012678\n",
              "not.fully.paid                  -0.191957  ...                0.100320\n",
              "purpose_credit_card              0.011830  ...               -0.110817\n",
              "purpose_debt_consolidation       0.016604  ...               -0.249684\n",
              "purpose_educational             -0.044384  ...               -0.062337\n",
              "purpose_home_improvement         0.007923  ...               -0.080004\n",
              "purpose_major_purchase           0.038807  ...               -0.061892\n",
              "purpose_small_business          -0.010269  ...                1.000000\n",
              "\n",
              "[19 rows x 19 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 55
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "9M4Us5huRPJm",
        "outputId": "07b955a8-95be-4c0d-ee5f-54c91d0a6484",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 856
        }
      },
      "source": [
        "plt.figure(\n",
        "        figsize=[16,12]\n",
        ")\n",
        "\n",
        "sns.heatmap(\n",
        "        data=final_data.corr(), \n",
        "        cmap='viridis', \n",
        "        annot=False, \n",
        "        fmt='.2g'\n",
        ")"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7fe3b94b18d0>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 56
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 1152x864 with 2 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "rQ9h8I8mS79h"
      },
      "source": [
        "to_drop2 = ['revol.bal', 'days.with.cr.line', 'installment', 'revol.bal']\n",
        "\n",
        "final_data.drop(to_drop2, axis=1, inplace=True)\n",
        "#We only focus on the grids of yellow or very light green. After comparing with the feature description again,  revol.bal,day.with.cr.line,installment can repsent by annual income. revol.util can repsent by int.rate,  "
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "r1Fsv5hdUcru",
        "outputId": "2b154cea-86f9-4232-ecf2-8471ba8fff0e",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "source": [
        "final_data.isnull().mean()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "credit.policy                 0.0\n",
              "int.rate                      0.0\n",
              "log.annual.inc                0.0\n",
              "dti                           0.0\n",
              "fico                          0.0\n",
              "revol.util                    0.0\n",
              "inq.last.6mths                0.0\n",
              "delinq.2yrs                   0.0\n",
              "pub.rec                       0.0\n",
              "not.fully.paid                0.0\n",
              "purpose_credit_card           0.0\n",
              "purpose_debt_consolidation    0.0\n",
              "purpose_educational           0.0\n",
              "purpose_home_improvement      0.0\n",
              "purpose_major_purchase        0.0\n",
              "purpose_small_business        0.0\n",
              "dtype: float64"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 59
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "QwMjwOllR7bD"
      },
      "source": [
        "#to_train = df1[df1['not.fully.paid'].isin([0,1])]\n",
        "#to_pred = df1[df1['not.fully.paid'] == 2]\n",
        "\n",
        "to_train = final_data[final_data['not.fully.paid'].isin([0,1])]\n",
        "to_pred = final_data[final_data['not.fully.paid'] == 2]"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Shvcnp4QSAun"
      },
      "source": [
        "X = to_train.drop('not.fully.paid', axis=1).values\n",
        "y = to_train['not.fully.paid'].values\n",
        "\n",
        "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.3, random_state = 101)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "AVBCNBHfSME-"
      },
      "source": [
        "scaler = MinMaxScaler()\n",
        "X_train = scaler.fit_transform(X_train)\n",
        "X_test = scaler.transform(X_test)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "OaAie2jcSOrm",
        "outputId": "4a7d474d-c553-4ff1-c53a-78df777c922a",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "source": [
        "X_train.shape"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(11263, 15)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 63
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ZcG7_NsgVFfg"
      },
      "source": [
        "model = Sequential()\n",
        "\n",
        "model.add(\n",
        "        Dense(94, activation='relu')\n",
        ")\n",
        "\n",
        "model.add(\n",
        "        Dense(30, activation='relu')\n",
        ")\n",
        "\n",
        "model.add(\n",
        "        Dense(15, activation='relu')\n",
        ")\n",
        "\n",
        "\n",
        "model.add(\n",
        "        Dense(1, activation='sigmoid')\n",
        ")\n",
        "\n",
        "model.compile(\n",
        "        optimizer='adam', \n",
        "        loss='binary_crossentropy', \n",
        "        metrics=['accuracy']\n",
        ")"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "AgBZ2hUkVKyl",
        "outputId": "ed7a9ec8-0cff-47eb-cc58-8522559188cb",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "source": [
        "early_stop = EarlyStopping(\n",
        "        monitor='val_loss', \n",
        "        mode='min', \n",
        "        verbose=1, \n",
        "        patience=25\n",
        ")\n",
        "\n",
        "model.fit(\n",
        "        X_train, \n",
        "        y_train, \n",
        "        epochs=200, \n",
        "        batch_size=256, \n",
        "        validation_data=(X_test, y_test),\n",
        "         callbacks=[early_stop]\n",
        ")"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Epoch 1/200\n",
            "44/44 [==============================] - 0s 5ms/step - loss: 0.6713 - accuracy: 0.6018 - val_loss: 0.6586 - val_accuracy: 0.6041\n",
            "Epoch 2/200\n",
            "44/44 [==============================] - 0s 3ms/step - loss: 0.6496 - accuracy: 0.6140 - val_loss: 0.6552 - val_accuracy: 0.6043\n",
            "Epoch 3/200\n",
            "44/44 [==============================] - 0s 3ms/step - loss: 0.6447 - accuracy: 0.6217 - val_loss: 0.6557 - val_accuracy: 0.6140\n",
            "Epoch 4/200\n",
            "44/44 [==============================] - 0s 3ms/step - loss: 0.6430 - accuracy: 0.6213 - val_loss: 0.6515 - val_accuracy: 0.6093\n",
            "Epoch 5/200\n",
            "44/44 [==============================] - 0s 3ms/step - loss: 0.6408 - accuracy: 0.6238 - val_loss: 0.6510 - val_accuracy: 0.6147\n",
            "Epoch 6/200\n",
            "44/44 [==============================] - 0s 3ms/step - loss: 0.6393 - accuracy: 0.6225 - val_loss: 0.6489 - val_accuracy: 0.6140\n",
            "Epoch 7/200\n",
            "44/44 [==============================] - 0s 3ms/step - loss: 0.6375 - accuracy: 0.6276 - val_loss: 0.6484 - val_accuracy: 0.6190\n",
            "Epoch 8/200\n",
            "44/44 [==============================] - 0s 3ms/step - loss: 0.6371 - accuracy: 0.6264 - val_loss: 0.6477 - val_accuracy: 0.6172\n",
            "Epoch 9/200\n",
            "44/44 [==============================] - 0s 3ms/step - loss: 0.6355 - accuracy: 0.6317 - val_loss: 0.6464 - val_accuracy: 0.6176\n",
            "Epoch 10/200\n",
            "44/44 [==============================] - 0s 3ms/step - loss: 0.6351 - accuracy: 0.6298 - val_loss: 0.6484 - val_accuracy: 0.6031\n",
            "Epoch 11/200\n",
            "44/44 [==============================] - 0s 3ms/step - loss: 0.6335 - accuracy: 0.6315 - val_loss: 0.6461 - val_accuracy: 0.6192\n",
            "Epoch 12/200\n",
            "44/44 [==============================] - 0s 3ms/step - loss: 0.6334 - accuracy: 0.6314 - val_loss: 0.6507 - val_accuracy: 0.6155\n",
            "Epoch 13/200\n",
            "44/44 [==============================] - 0s 3ms/step - loss: 0.6322 - accuracy: 0.6303 - val_loss: 0.6429 - val_accuracy: 0.6227\n",
            "Epoch 14/200\n",
            "44/44 [==============================] - 0s 3ms/step - loss: 0.6296 - accuracy: 0.6327 - val_loss: 0.6413 - val_accuracy: 0.6184\n",
            "Epoch 15/200\n",
            "44/44 [==============================] - 0s 3ms/step - loss: 0.6293 - accuracy: 0.6323 - val_loss: 0.6469 - val_accuracy: 0.6111\n",
            "Epoch 16/200\n",
            "44/44 [==============================] - 0s 3ms/step - loss: 0.6293 - accuracy: 0.6320 - val_loss: 0.6432 - val_accuracy: 0.6254\n",
            "Epoch 17/200\n",
            "44/44 [==============================] - 0s 3ms/step - loss: 0.6264 - accuracy: 0.6385 - val_loss: 0.6458 - val_accuracy: 0.6107\n",
            "Epoch 18/200\n",
            "44/44 [==============================] - 0s 3ms/step - loss: 0.6256 - accuracy: 0.6371 - val_loss: 0.6447 - val_accuracy: 0.6232\n",
            "Epoch 19/200\n",
            "44/44 [==============================] - 0s 3ms/step - loss: 0.6248 - accuracy: 0.6380 - val_loss: 0.6376 - val_accuracy: 0.6273\n",
            "Epoch 20/200\n",
            "44/44 [==============================] - 0s 3ms/step - loss: 0.6227 - accuracy: 0.6417 - val_loss: 0.6389 - val_accuracy: 0.6244\n",
            "Epoch 21/200\n",
            "44/44 [==============================] - 0s 3ms/step - loss: 0.6230 - accuracy: 0.6433 - val_loss: 0.6371 - val_accuracy: 0.6298\n",
            "Epoch 22/200\n",
            "44/44 [==============================] - 0s 3ms/step - loss: 0.6218 - accuracy: 0.6401 - val_loss: 0.6439 - val_accuracy: 0.6172\n",
            "Epoch 23/200\n",
            "44/44 [==============================] - 0s 2ms/step - loss: 0.6220 - accuracy: 0.6395 - val_loss: 0.6350 - val_accuracy: 0.6317\n",
            "Epoch 24/200\n",
            "44/44 [==============================] - 0s 3ms/step - loss: 0.6176 - accuracy: 0.6487 - val_loss: 0.6362 - val_accuracy: 0.6333\n",
            "Epoch 25/200\n",
            "44/44 [==============================] - 0s 3ms/step - loss: 0.6166 - accuracy: 0.6474 - val_loss: 0.6379 - val_accuracy: 0.6225\n",
            "Epoch 26/200\n",
            "44/44 [==============================] - 0s 3ms/step - loss: 0.6152 - accuracy: 0.6466 - val_loss: 0.6346 - val_accuracy: 0.6341\n",
            "Epoch 27/200\n",
            "44/44 [==============================] - 0s 2ms/step - loss: 0.6138 - accuracy: 0.6465 - val_loss: 0.6327 - val_accuracy: 0.6314\n",
            "Epoch 28/200\n",
            "44/44 [==============================] - 0s 2ms/step - loss: 0.6126 - accuracy: 0.6483 - val_loss: 0.6315 - val_accuracy: 0.6358\n",
            "Epoch 29/200\n",
            "44/44 [==============================] - 0s 3ms/step - loss: 0.6132 - accuracy: 0.6506 - val_loss: 0.6308 - val_accuracy: 0.6352\n",
            "Epoch 30/200\n",
            "44/44 [==============================] - 0s 3ms/step - loss: 0.6100 - accuracy: 0.6513 - val_loss: 0.6312 - val_accuracy: 0.6312\n",
            "Epoch 31/200\n",
            "44/44 [==============================] - 0s 2ms/step - loss: 0.6093 - accuracy: 0.6514 - val_loss: 0.6303 - val_accuracy: 0.6335\n",
            "Epoch 32/200\n",
            "44/44 [==============================] - 0s 3ms/step - loss: 0.6077 - accuracy: 0.6553 - val_loss: 0.6309 - val_accuracy: 0.6410\n",
            "Epoch 33/200\n",
            "44/44 [==============================] - 0s 3ms/step - loss: 0.6059 - accuracy: 0.6536 - val_loss: 0.6276 - val_accuracy: 0.6418\n",
            "Epoch 34/200\n",
            "44/44 [==============================] - 0s 3ms/step - loss: 0.6033 - accuracy: 0.6564 - val_loss: 0.6297 - val_accuracy: 0.6401\n",
            "Epoch 35/200\n",
            "44/44 [==============================] - 0s 3ms/step - loss: 0.6035 - accuracy: 0.6566 - val_loss: 0.6296 - val_accuracy: 0.6397\n",
            "Epoch 36/200\n",
            "44/44 [==============================] - 0s 3ms/step - loss: 0.6023 - accuracy: 0.6623 - val_loss: 0.6251 - val_accuracy: 0.6414\n",
            "Epoch 37/200\n",
            "44/44 [==============================] - 0s 3ms/step - loss: 0.6003 - accuracy: 0.6640 - val_loss: 0.6257 - val_accuracy: 0.6350\n",
            "Epoch 38/200\n",
            "44/44 [==============================] - 0s 2ms/step - loss: 0.6000 - accuracy: 0.6616 - val_loss: 0.6260 - val_accuracy: 0.6484\n",
            "Epoch 39/200\n",
            "44/44 [==============================] - 0s 3ms/step - loss: 0.5967 - accuracy: 0.6660 - val_loss: 0.6252 - val_accuracy: 0.6437\n",
            "Epoch 40/200\n",
            "44/44 [==============================] - 0s 2ms/step - loss: 0.5946 - accuracy: 0.6643 - val_loss: 0.6222 - val_accuracy: 0.6451\n",
            "Epoch 41/200\n",
            "44/44 [==============================] - 0s 2ms/step - loss: 0.5952 - accuracy: 0.6641 - val_loss: 0.6222 - val_accuracy: 0.6513\n",
            "Epoch 42/200\n",
            "44/44 [==============================] - 0s 3ms/step - loss: 0.5932 - accuracy: 0.6669 - val_loss: 0.6219 - val_accuracy: 0.6470\n",
            "Epoch 43/200\n",
            "44/44 [==============================] - 0s 2ms/step - loss: 0.5927 - accuracy: 0.6659 - val_loss: 0.6212 - val_accuracy: 0.6493\n",
            "Epoch 44/200\n",
            "44/44 [==============================] - 0s 3ms/step - loss: 0.5935 - accuracy: 0.6672 - val_loss: 0.6204 - val_accuracy: 0.6459\n",
            "Epoch 45/200\n",
            "44/44 [==============================] - 0s 2ms/step - loss: 0.5891 - accuracy: 0.6684 - val_loss: 0.6204 - val_accuracy: 0.6412\n",
            "Epoch 46/200\n",
            "44/44 [==============================] - 0s 3ms/step - loss: 0.5878 - accuracy: 0.6731 - val_loss: 0.6190 - val_accuracy: 0.6497\n",
            "Epoch 47/200\n",
            "44/44 [==============================] - 0s 2ms/step - loss: 0.5857 - accuracy: 0.6684 - val_loss: 0.6189 - val_accuracy: 0.6464\n",
            "Epoch 48/200\n",
            "44/44 [==============================] - 0s 2ms/step - loss: 0.5849 - accuracy: 0.6730 - val_loss: 0.6181 - val_accuracy: 0.6495\n",
            "Epoch 49/200\n",
            "44/44 [==============================] - 0s 3ms/step - loss: 0.5876 - accuracy: 0.6726 - val_loss: 0.6249 - val_accuracy: 0.6387\n",
            "Epoch 50/200\n",
            "44/44 [==============================] - 0s 2ms/step - loss: 0.5825 - accuracy: 0.6741 - val_loss: 0.6182 - val_accuracy: 0.6549\n",
            "Epoch 51/200\n",
            "44/44 [==============================] - 0s 3ms/step - loss: 0.5841 - accuracy: 0.6761 - val_loss: 0.6154 - val_accuracy: 0.6598\n",
            "Epoch 52/200\n",
            "44/44 [==============================] - 0s 2ms/step - loss: 0.5808 - accuracy: 0.6766 - val_loss: 0.6170 - val_accuracy: 0.6484\n",
            "Epoch 53/200\n",
            "44/44 [==============================] - 0s 2ms/step - loss: 0.5804 - accuracy: 0.6804 - val_loss: 0.6155 - val_accuracy: 0.6534\n",
            "Epoch 54/200\n",
            "44/44 [==============================] - 0s 3ms/step - loss: 0.5766 - accuracy: 0.6829 - val_loss: 0.6166 - val_accuracy: 0.6549\n",
            "Epoch 55/200\n",
            "44/44 [==============================] - 0s 2ms/step - loss: 0.5771 - accuracy: 0.6845 - val_loss: 0.6148 - val_accuracy: 0.6565\n",
            "Epoch 56/200\n",
            "44/44 [==============================] - 0s 3ms/step - loss: 0.5750 - accuracy: 0.6862 - val_loss: 0.6138 - val_accuracy: 0.6588\n",
            "Epoch 57/200\n",
            "44/44 [==============================] - 0s 3ms/step - loss: 0.5743 - accuracy: 0.6880 - val_loss: 0.6122 - val_accuracy: 0.6590\n",
            "Epoch 58/200\n",
            "44/44 [==============================] - 0s 2ms/step - loss: 0.5721 - accuracy: 0.6907 - val_loss: 0.6141 - val_accuracy: 0.6588\n",
            "Epoch 59/200\n",
            "44/44 [==============================] - 0s 2ms/step - loss: 0.5741 - accuracy: 0.6843 - val_loss: 0.6165 - val_accuracy: 0.6617\n",
            "Epoch 60/200\n",
            "44/44 [==============================] - 0s 2ms/step - loss: 0.5723 - accuracy: 0.6891 - val_loss: 0.6251 - val_accuracy: 0.6464\n",
            "Epoch 61/200\n",
            "44/44 [==============================] - 0s 3ms/step - loss: 0.5706 - accuracy: 0.6900 - val_loss: 0.6141 - val_accuracy: 0.6642\n",
            "Epoch 62/200\n",
            "44/44 [==============================] - 0s 3ms/step - loss: 0.5685 - accuracy: 0.6899 - val_loss: 0.6193 - val_accuracy: 0.6491\n",
            "Epoch 63/200\n",
            "44/44 [==============================] - 0s 3ms/step - loss: 0.5686 - accuracy: 0.6916 - val_loss: 0.6137 - val_accuracy: 0.6663\n",
            "Epoch 64/200\n",
            "44/44 [==============================] - 0s 4ms/step - loss: 0.5674 - accuracy: 0.6902 - val_loss: 0.6094 - val_accuracy: 0.6555\n",
            "Epoch 65/200\n",
            "44/44 [==============================] - 0s 3ms/step - loss: 0.5668 - accuracy: 0.6907 - val_loss: 0.6132 - val_accuracy: 0.6586\n",
            "Epoch 66/200\n",
            "44/44 [==============================] - 0s 3ms/step - loss: 0.5666 - accuracy: 0.6927 - val_loss: 0.6103 - val_accuracy: 0.6683\n",
            "Epoch 67/200\n",
            "44/44 [==============================] - 0s 3ms/step - loss: 0.5634 - accuracy: 0.6973 - val_loss: 0.6124 - val_accuracy: 0.6638\n",
            "Epoch 68/200\n",
            "44/44 [==============================] - 0s 3ms/step - loss: 0.5649 - accuracy: 0.6961 - val_loss: 0.6167 - val_accuracy: 0.6582\n",
            "Epoch 69/200\n",
            "44/44 [==============================] - 0s 3ms/step - loss: 0.5656 - accuracy: 0.6938 - val_loss: 0.6137 - val_accuracy: 0.6598\n",
            "Epoch 70/200\n",
            "44/44 [==============================] - 0s 3ms/step - loss: 0.5636 - accuracy: 0.6954 - val_loss: 0.6142 - val_accuracy: 0.6520\n",
            "Epoch 71/200\n",
            "44/44 [==============================] - 0s 3ms/step - loss: 0.5613 - accuracy: 0.6964 - val_loss: 0.6090 - val_accuracy: 0.6605\n",
            "Epoch 72/200\n",
            "44/44 [==============================] - 0s 3ms/step - loss: 0.5623 - accuracy: 0.6979 - val_loss: 0.6079 - val_accuracy: 0.6613\n",
            "Epoch 73/200\n",
            "44/44 [==============================] - 0s 2ms/step - loss: 0.5620 - accuracy: 0.7011 - val_loss: 0.6110 - val_accuracy: 0.6629\n",
            "Epoch 74/200\n",
            "44/44 [==============================] - 0s 3ms/step - loss: 0.5591 - accuracy: 0.6971 - val_loss: 0.6114 - val_accuracy: 0.6669\n",
            "Epoch 75/200\n",
            "44/44 [==============================] - 0s 2ms/step - loss: 0.5563 - accuracy: 0.6998 - val_loss: 0.6081 - val_accuracy: 0.6652\n",
            "Epoch 76/200\n",
            "44/44 [==============================] - 0s 3ms/step - loss: 0.5572 - accuracy: 0.6997 - val_loss: 0.6114 - val_accuracy: 0.6692\n",
            "Epoch 77/200\n",
            "44/44 [==============================] - 0s 3ms/step - loss: 0.5564 - accuracy: 0.7005 - val_loss: 0.6132 - val_accuracy: 0.6648\n",
            "Epoch 78/200\n",
            "44/44 [==============================] - 0s 3ms/step - loss: 0.5539 - accuracy: 0.7076 - val_loss: 0.6075 - val_accuracy: 0.6700\n",
            "Epoch 79/200\n",
            "44/44 [==============================] - 0s 3ms/step - loss: 0.5552 - accuracy: 0.7027 - val_loss: 0.6103 - val_accuracy: 0.6598\n",
            "Epoch 80/200\n",
            "44/44 [==============================] - 0s 3ms/step - loss: 0.5565 - accuracy: 0.7002 - val_loss: 0.6046 - val_accuracy: 0.6648\n",
            "Epoch 81/200\n",
            "44/44 [==============================] - 0s 3ms/step - loss: 0.5527 - accuracy: 0.7047 - val_loss: 0.6084 - val_accuracy: 0.6673\n",
            "Epoch 82/200\n",
            "44/44 [==============================] - 0s 3ms/step - loss: 0.5495 - accuracy: 0.7090 - val_loss: 0.6106 - val_accuracy: 0.6731\n",
            "Epoch 83/200\n",
            "44/44 [==============================] - 0s 3ms/step - loss: 0.5533 - accuracy: 0.7037 - val_loss: 0.6095 - val_accuracy: 0.6607\n",
            "Epoch 84/200\n",
            "44/44 [==============================] - 0s 3ms/step - loss: 0.5511 - accuracy: 0.7023 - val_loss: 0.6089 - val_accuracy: 0.6741\n",
            "Epoch 85/200\n",
            "44/44 [==============================] - 0s 3ms/step - loss: 0.5507 - accuracy: 0.7084 - val_loss: 0.6048 - val_accuracy: 0.6681\n",
            "Epoch 86/200\n",
            "44/44 [==============================] - 0s 3ms/step - loss: 0.5504 - accuracy: 0.7034 - val_loss: 0.6076 - val_accuracy: 0.6712\n",
            "Epoch 87/200\n",
            "44/44 [==============================] - 0s 3ms/step - loss: 0.5451 - accuracy: 0.7114 - val_loss: 0.6079 - val_accuracy: 0.6650\n",
            "Epoch 88/200\n",
            "44/44 [==============================] - 0s 3ms/step - loss: 0.5455 - accuracy: 0.7116 - val_loss: 0.6031 - val_accuracy: 0.6725\n",
            "Epoch 89/200\n",
            "44/44 [==============================] - 0s 3ms/step - loss: 0.5440 - accuracy: 0.7123 - val_loss: 0.6083 - val_accuracy: 0.6735\n",
            "Epoch 90/200\n",
            "44/44 [==============================] - 0s 3ms/step - loss: 0.5432 - accuracy: 0.7114 - val_loss: 0.6117 - val_accuracy: 0.6733\n",
            "Epoch 91/200\n",
            "44/44 [==============================] - 0s 2ms/step - loss: 0.5453 - accuracy: 0.7064 - val_loss: 0.6006 - val_accuracy: 0.6828\n",
            "Epoch 92/200\n",
            "44/44 [==============================] - 0s 3ms/step - loss: 0.5393 - accuracy: 0.7163 - val_loss: 0.6010 - val_accuracy: 0.6727\n",
            "Epoch 93/200\n",
            "44/44 [==============================] - 0s 3ms/step - loss: 0.5418 - accuracy: 0.7146 - val_loss: 0.6024 - val_accuracy: 0.6731\n",
            "Epoch 94/200\n",
            "44/44 [==============================] - 0s 3ms/step - loss: 0.5406 - accuracy: 0.7138 - val_loss: 0.6005 - val_accuracy: 0.6754\n",
            "Epoch 95/200\n",
            "44/44 [==============================] - 0s 3ms/step - loss: 0.5390 - accuracy: 0.7157 - val_loss: 0.6114 - val_accuracy: 0.6692\n",
            "Epoch 96/200\n",
            "44/44 [==============================] - 0s 3ms/step - loss: 0.5375 - accuracy: 0.7193 - val_loss: 0.6114 - val_accuracy: 0.6797\n",
            "Epoch 97/200\n",
            "44/44 [==============================] - 0s 3ms/step - loss: 0.5377 - accuracy: 0.7189 - val_loss: 0.5992 - val_accuracy: 0.6801\n",
            "Epoch 98/200\n",
            "44/44 [==============================] - 0s 3ms/step - loss: 0.5363 - accuracy: 0.7199 - val_loss: 0.6024 - val_accuracy: 0.6710\n",
            "Epoch 99/200\n",
            "44/44 [==============================] - 0s 3ms/step - loss: 0.5354 - accuracy: 0.7179 - val_loss: 0.6030 - val_accuracy: 0.6754\n",
            "Epoch 100/200\n",
            "44/44 [==============================] - 0s 3ms/step - loss: 0.5363 - accuracy: 0.7201 - val_loss: 0.6027 - val_accuracy: 0.6841\n",
            "Epoch 101/200\n",
            "44/44 [==============================] - 0s 3ms/step - loss: 0.5375 - accuracy: 0.7185 - val_loss: 0.6026 - val_accuracy: 0.6797\n",
            "Epoch 102/200\n",
            "44/44 [==============================] - 0s 3ms/step - loss: 0.5315 - accuracy: 0.7237 - val_loss: 0.6003 - val_accuracy: 0.6837\n",
            "Epoch 103/200\n",
            "44/44 [==============================] - 0s 3ms/step - loss: 0.5301 - accuracy: 0.7209 - val_loss: 0.5997 - val_accuracy: 0.6820\n",
            "Epoch 104/200\n",
            "44/44 [==============================] - 0s 3ms/step - loss: 0.5287 - accuracy: 0.7249 - val_loss: 0.6000 - val_accuracy: 0.6791\n",
            "Epoch 105/200\n",
            "44/44 [==============================] - 0s 3ms/step - loss: 0.5305 - accuracy: 0.7222 - val_loss: 0.5987 - val_accuracy: 0.6843\n",
            "Epoch 106/200\n",
            "44/44 [==============================] - 0s 3ms/step - loss: 0.5282 - accuracy: 0.7231 - val_loss: 0.6078 - val_accuracy: 0.6731\n",
            "Epoch 107/200\n",
            "44/44 [==============================] - 0s 3ms/step - loss: 0.5318 - accuracy: 0.7263 - val_loss: 0.6005 - val_accuracy: 0.6785\n",
            "Epoch 108/200\n",
            "44/44 [==============================] - 0s 3ms/step - loss: 0.5271 - accuracy: 0.7251 - val_loss: 0.5957 - val_accuracy: 0.6743\n",
            "Epoch 109/200\n",
            "44/44 [==============================] - 0s 3ms/step - loss: 0.5255 - accuracy: 0.7238 - val_loss: 0.6067 - val_accuracy: 0.6756\n",
            "Epoch 110/200\n",
            "44/44 [==============================] - 0s 3ms/step - loss: 0.5272 - accuracy: 0.7277 - val_loss: 0.6058 - val_accuracy: 0.6741\n",
            "Epoch 111/200\n",
            "44/44 [==============================] - 0s 2ms/step - loss: 0.5237 - accuracy: 0.7290 - val_loss: 0.5993 - val_accuracy: 0.6837\n",
            "Epoch 112/200\n",
            "44/44 [==============================] - 0s 3ms/step - loss: 0.5227 - accuracy: 0.7270 - val_loss: 0.6017 - val_accuracy: 0.6689\n",
            "Epoch 113/200\n",
            "44/44 [==============================] - 0s 2ms/step - loss: 0.5224 - accuracy: 0.7313 - val_loss: 0.6030 - val_accuracy: 0.6714\n",
            "Epoch 114/200\n",
            "44/44 [==============================] - 0s 3ms/step - loss: 0.5250 - accuracy: 0.7271 - val_loss: 0.5973 - val_accuracy: 0.6853\n",
            "Epoch 115/200\n",
            "44/44 [==============================] - 0s 3ms/step - loss: 0.5195 - accuracy: 0.7280 - val_loss: 0.6010 - val_accuracy: 0.6882\n",
            "Epoch 116/200\n",
            "44/44 [==============================] - 0s 3ms/step - loss: 0.5188 - accuracy: 0.7313 - val_loss: 0.6094 - val_accuracy: 0.6797\n",
            "Epoch 117/200\n",
            "44/44 [==============================] - 0s 3ms/step - loss: 0.5167 - accuracy: 0.7324 - val_loss: 0.6020 - val_accuracy: 0.6832\n",
            "Epoch 118/200\n",
            "44/44 [==============================] - 0s 3ms/step - loss: 0.5196 - accuracy: 0.7331 - val_loss: 0.5972 - val_accuracy: 0.6779\n",
            "Epoch 119/200\n",
            "44/44 [==============================] - 0s 3ms/step - loss: 0.5163 - accuracy: 0.7328 - val_loss: 0.6037 - val_accuracy: 0.6799\n",
            "Epoch 120/200\n",
            "44/44 [==============================] - 0s 3ms/step - loss: 0.5215 - accuracy: 0.7284 - val_loss: 0.6044 - val_accuracy: 0.6721\n",
            "Epoch 121/200\n",
            "44/44 [==============================] - 0s 3ms/step - loss: 0.5204 - accuracy: 0.7275 - val_loss: 0.5994 - val_accuracy: 0.6820\n",
            "Epoch 122/200\n",
            "44/44 [==============================] - 0s 3ms/step - loss: 0.5152 - accuracy: 0.7317 - val_loss: 0.6036 - val_accuracy: 0.6822\n",
            "Epoch 123/200\n",
            "44/44 [==============================] - 0s 3ms/step - loss: 0.5187 - accuracy: 0.7284 - val_loss: 0.5977 - val_accuracy: 0.6812\n",
            "Epoch 124/200\n",
            "44/44 [==============================] - 0s 3ms/step - loss: 0.5111 - accuracy: 0.7347 - val_loss: 0.6014 - val_accuracy: 0.6816\n",
            "Epoch 125/200\n",
            "44/44 [==============================] - 0s 3ms/step - loss: 0.5110 - accuracy: 0.7336 - val_loss: 0.5963 - val_accuracy: 0.6878\n",
            "Epoch 126/200\n",
            "44/44 [==============================] - 0s 3ms/step - loss: 0.5097 - accuracy: 0.7381 - val_loss: 0.5984 - val_accuracy: 0.6897\n",
            "Epoch 127/200\n",
            "44/44 [==============================] - 0s 3ms/step - loss: 0.5105 - accuracy: 0.7367 - val_loss: 0.6044 - val_accuracy: 0.6772\n",
            "Epoch 128/200\n",
            "44/44 [==============================] - 0s 3ms/step - loss: 0.5106 - accuracy: 0.7343 - val_loss: 0.6023 - val_accuracy: 0.6808\n",
            "Epoch 129/200\n",
            "44/44 [==============================] - 0s 3ms/step - loss: 0.5094 - accuracy: 0.7387 - val_loss: 0.5952 - val_accuracy: 0.6812\n",
            "Epoch 130/200\n",
            "44/44 [==============================] - 0s 3ms/step - loss: 0.5054 - accuracy: 0.7401 - val_loss: 0.5960 - val_accuracy: 0.6895\n",
            "Epoch 131/200\n",
            "44/44 [==============================] - 0s 3ms/step - loss: 0.5162 - accuracy: 0.7312 - val_loss: 0.6262 - val_accuracy: 0.6714\n",
            "Epoch 132/200\n",
            "44/44 [==============================] - 0s 3ms/step - loss: 0.5175 - accuracy: 0.7339 - val_loss: 0.6034 - val_accuracy: 0.6818\n",
            "Epoch 133/200\n",
            "44/44 [==============================] - 0s 3ms/step - loss: 0.5060 - accuracy: 0.7384 - val_loss: 0.5990 - val_accuracy: 0.6882\n",
            "Epoch 134/200\n",
            "44/44 [==============================] - 0s 3ms/step - loss: 0.5029 - accuracy: 0.7469 - val_loss: 0.5935 - val_accuracy: 0.6971\n",
            "Epoch 135/200\n",
            "44/44 [==============================] - 0s 3ms/step - loss: 0.5010 - accuracy: 0.7417 - val_loss: 0.5926 - val_accuracy: 0.6915\n",
            "Epoch 136/200\n",
            "44/44 [==============================] - 0s 2ms/step - loss: 0.5005 - accuracy: 0.7432 - val_loss: 0.5940 - val_accuracy: 0.6866\n",
            "Epoch 137/200\n",
            "44/44 [==============================] - 0s 2ms/step - loss: 0.4998 - accuracy: 0.7496 - val_loss: 0.5958 - val_accuracy: 0.6897\n",
            "Epoch 138/200\n",
            "44/44 [==============================] - 0s 3ms/step - loss: 0.4978 - accuracy: 0.7493 - val_loss: 0.5958 - val_accuracy: 0.6913\n",
            "Epoch 139/200\n",
            "44/44 [==============================] - 0s 3ms/step - loss: 0.4988 - accuracy: 0.7497 - val_loss: 0.5996 - val_accuracy: 0.6832\n",
            "Epoch 140/200\n",
            "44/44 [==============================] - 0s 3ms/step - loss: 0.4980 - accuracy: 0.7428 - val_loss: 0.5982 - val_accuracy: 0.6903\n",
            "Epoch 141/200\n",
            "44/44 [==============================] - 0s 3ms/step - loss: 0.4997 - accuracy: 0.7452 - val_loss: 0.5976 - val_accuracy: 0.6872\n",
            "Epoch 142/200\n",
            "44/44 [==============================] - 0s 3ms/step - loss: 0.4987 - accuracy: 0.7446 - val_loss: 0.5938 - val_accuracy: 0.6843\n",
            "Epoch 143/200\n",
            "44/44 [==============================] - 0s 3ms/step - loss: 0.4985 - accuracy: 0.7470 - val_loss: 0.6029 - val_accuracy: 0.6861\n",
            "Epoch 144/200\n",
            "44/44 [==============================] - 0s 2ms/step - loss: 0.4959 - accuracy: 0.7459 - val_loss: 0.5922 - val_accuracy: 0.6950\n",
            "Epoch 145/200\n",
            "44/44 [==============================] - 0s 3ms/step - loss: 0.4942 - accuracy: 0.7490 - val_loss: 0.5908 - val_accuracy: 0.6895\n",
            "Epoch 146/200\n",
            "44/44 [==============================] - 0s 2ms/step - loss: 0.4937 - accuracy: 0.7502 - val_loss: 0.6002 - val_accuracy: 0.6822\n",
            "Epoch 147/200\n",
            "44/44 [==============================] - 0s 2ms/step - loss: 0.4961 - accuracy: 0.7502 - val_loss: 0.5967 - val_accuracy: 0.6950\n",
            "Epoch 148/200\n",
            "44/44 [==============================] - 0s 3ms/step - loss: 0.4955 - accuracy: 0.7464 - val_loss: 0.6014 - val_accuracy: 0.6868\n",
            "Epoch 149/200\n",
            "44/44 [==============================] - 0s 3ms/step - loss: 0.4942 - accuracy: 0.7478 - val_loss: 0.5906 - val_accuracy: 0.6969\n",
            "Epoch 150/200\n",
            "44/44 [==============================] - 0s 3ms/step - loss: 0.4874 - accuracy: 0.7502 - val_loss: 0.5946 - val_accuracy: 0.6932\n",
            "Epoch 151/200\n",
            "44/44 [==============================] - 0s 3ms/step - loss: 0.4907 - accuracy: 0.7486 - val_loss: 0.5923 - val_accuracy: 0.6924\n",
            "Epoch 152/200\n",
            "44/44 [==============================] - 0s 3ms/step - loss: 0.4921 - accuracy: 0.7455 - val_loss: 0.5952 - val_accuracy: 0.6857\n",
            "Epoch 153/200\n",
            "44/44 [==============================] - 0s 3ms/step - loss: 0.4897 - accuracy: 0.7506 - val_loss: 0.5912 - val_accuracy: 0.6969\n",
            "Epoch 154/200\n",
            "44/44 [==============================] - 0s 3ms/step - loss: 0.4881 - accuracy: 0.7520 - val_loss: 0.5961 - val_accuracy: 0.6899\n",
            "Epoch 155/200\n",
            "44/44 [==============================] - 0s 3ms/step - loss: 0.4889 - accuracy: 0.7510 - val_loss: 0.5924 - val_accuracy: 0.7050\n",
            "Epoch 156/200\n",
            "44/44 [==============================] - 0s 3ms/step - loss: 0.4869 - accuracy: 0.7495 - val_loss: 0.6075 - val_accuracy: 0.6909\n",
            "Epoch 157/200\n",
            "44/44 [==============================] - 0s 3ms/step - loss: 0.4845 - accuracy: 0.7571 - val_loss: 0.5887 - val_accuracy: 0.6950\n",
            "Epoch 158/200\n",
            "44/44 [==============================] - 0s 3ms/step - loss: 0.4836 - accuracy: 0.7548 - val_loss: 0.5927 - val_accuracy: 0.7017\n",
            "Epoch 159/200\n",
            "44/44 [==============================] - 0s 3ms/step - loss: 0.4839 - accuracy: 0.7554 - val_loss: 0.5974 - val_accuracy: 0.6977\n",
            "Epoch 160/200\n",
            "44/44 [==============================] - 0s 3ms/step - loss: 0.4805 - accuracy: 0.7570 - val_loss: 0.5920 - val_accuracy: 0.6961\n",
            "Epoch 161/200\n",
            "44/44 [==============================] - 0s 3ms/step - loss: 0.4808 - accuracy: 0.7596 - val_loss: 0.5908 - val_accuracy: 0.6944\n",
            "Epoch 162/200\n",
            "44/44 [==============================] - 0s 3ms/step - loss: 0.4877 - accuracy: 0.7529 - val_loss: 0.6050 - val_accuracy: 0.7004\n",
            "Epoch 163/200\n",
            "44/44 [==============================] - 0s 3ms/step - loss: 0.4835 - accuracy: 0.7542 - val_loss: 0.5906 - val_accuracy: 0.6942\n",
            "Epoch 164/200\n",
            "44/44 [==============================] - 0s 3ms/step - loss: 0.4768 - accuracy: 0.7603 - val_loss: 0.6002 - val_accuracy: 0.6969\n",
            "Epoch 165/200\n",
            "44/44 [==============================] - 0s 3ms/step - loss: 0.4778 - accuracy: 0.7571 - val_loss: 0.6002 - val_accuracy: 0.6936\n",
            "Epoch 166/200\n",
            "44/44 [==============================] - 0s 3ms/step - loss: 0.4869 - accuracy: 0.7507 - val_loss: 0.5948 - val_accuracy: 0.7035\n",
            "Epoch 167/200\n",
            "44/44 [==============================] - 0s 3ms/step - loss: 0.4763 - accuracy: 0.7594 - val_loss: 0.6040 - val_accuracy: 0.6921\n",
            "Epoch 168/200\n",
            "44/44 [==============================] - 0s 3ms/step - loss: 0.4769 - accuracy: 0.7575 - val_loss: 0.5929 - val_accuracy: 0.6977\n",
            "Epoch 169/200\n",
            "44/44 [==============================] - 0s 3ms/step - loss: 0.4736 - accuracy: 0.7624 - val_loss: 0.5900 - val_accuracy: 0.6965\n",
            "Epoch 170/200\n",
            "44/44 [==============================] - 0s 3ms/step - loss: 0.4776 - accuracy: 0.7588 - val_loss: 0.5908 - val_accuracy: 0.7017\n",
            "Epoch 171/200\n",
            "44/44 [==============================] - 0s 3ms/step - loss: 0.4787 - accuracy: 0.7550 - val_loss: 0.6279 - val_accuracy: 0.6799\n",
            "Epoch 172/200\n",
            "44/44 [==============================] - 0s 3ms/step - loss: 0.4766 - accuracy: 0.7569 - val_loss: 0.5955 - val_accuracy: 0.6961\n",
            "Epoch 173/200\n",
            "44/44 [==============================] - 0s 3ms/step - loss: 0.4765 - accuracy: 0.7574 - val_loss: 0.5968 - val_accuracy: 0.6957\n",
            "Epoch 174/200\n",
            "44/44 [==============================] - 0s 3ms/step - loss: 0.4707 - accuracy: 0.7589 - val_loss: 0.5964 - val_accuracy: 0.6979\n",
            "Epoch 175/200\n",
            "44/44 [==============================] - 0s 3ms/step - loss: 0.4775 - accuracy: 0.7567 - val_loss: 0.6031 - val_accuracy: 0.6924\n",
            "Epoch 176/200\n",
            "44/44 [==============================] - 0s 3ms/step - loss: 0.4756 - accuracy: 0.7589 - val_loss: 0.6016 - val_accuracy: 0.7027\n",
            "Epoch 177/200\n",
            "44/44 [==============================] - 0s 3ms/step - loss: 0.4691 - accuracy: 0.7633 - val_loss: 0.5882 - val_accuracy: 0.7069\n",
            "Epoch 178/200\n",
            "44/44 [==============================] - 0s 3ms/step - loss: 0.4740 - accuracy: 0.7602 - val_loss: 0.5997 - val_accuracy: 0.7021\n",
            "Epoch 179/200\n",
            "44/44 [==============================] - 0s 3ms/step - loss: 0.4704 - accuracy: 0.7624 - val_loss: 0.5885 - val_accuracy: 0.7046\n",
            "Epoch 180/200\n",
            "44/44 [==============================] - 0s 3ms/step - loss: 0.4647 - accuracy: 0.7683 - val_loss: 0.5902 - val_accuracy: 0.7040\n",
            "Epoch 181/200\n",
            "44/44 [==============================] - 0s 3ms/step - loss: 0.4650 - accuracy: 0.7668 - val_loss: 0.5887 - val_accuracy: 0.7037\n",
            "Epoch 182/200\n",
            "44/44 [==============================] - 0s 3ms/step - loss: 0.4673 - accuracy: 0.7664 - val_loss: 0.5907 - val_accuracy: 0.7025\n",
            "Epoch 183/200\n",
            "44/44 [==============================] - 0s 3ms/step - loss: 0.4674 - accuracy: 0.7629 - val_loss: 0.6008 - val_accuracy: 0.6967\n",
            "Epoch 184/200\n",
            "44/44 [==============================] - 0s 3ms/step - loss: 0.4718 - accuracy: 0.7634 - val_loss: 0.5931 - val_accuracy: 0.7029\n",
            "Epoch 185/200\n",
            "44/44 [==============================] - 0s 3ms/step - loss: 0.4640 - accuracy: 0.7674 - val_loss: 0.5910 - val_accuracy: 0.7002\n",
            "Epoch 186/200\n",
            "44/44 [==============================] - 0s 3ms/step - loss: 0.4661 - accuracy: 0.7656 - val_loss: 0.6004 - val_accuracy: 0.6982\n",
            "Epoch 187/200\n",
            "44/44 [==============================] - 0s 3ms/step - loss: 0.4688 - accuracy: 0.7624 - val_loss: 0.6118 - val_accuracy: 0.6936\n",
            "Epoch 188/200\n",
            "44/44 [==============================] - 0s 3ms/step - loss: 0.4611 - accuracy: 0.7673 - val_loss: 0.5888 - val_accuracy: 0.7040\n",
            "Epoch 189/200\n",
            "44/44 [==============================] - 0s 3ms/step - loss: 0.4619 - accuracy: 0.7658 - val_loss: 0.5985 - val_accuracy: 0.7054\n",
            "Epoch 190/200\n",
            "44/44 [==============================] - 0s 3ms/step - loss: 0.4623 - accuracy: 0.7684 - val_loss: 0.5931 - val_accuracy: 0.6994\n",
            "Epoch 191/200\n",
            "44/44 [==============================] - 0s 3ms/step - loss: 0.4623 - accuracy: 0.7682 - val_loss: 0.5865 - val_accuracy: 0.7056\n",
            "Epoch 192/200\n",
            "44/44 [==============================] - 0s 3ms/step - loss: 0.4580 - accuracy: 0.7712 - val_loss: 0.6002 - val_accuracy: 0.7025\n",
            "Epoch 193/200\n",
            "44/44 [==============================] - 0s 4ms/step - loss: 0.4555 - accuracy: 0.7704 - val_loss: 0.5876 - val_accuracy: 0.7100\n",
            "Epoch 194/200\n",
            "44/44 [==============================] - 0s 3ms/step - loss: 0.4543 - accuracy: 0.7738 - val_loss: 0.5940 - val_accuracy: 0.7002\n",
            "Epoch 195/200\n",
            "44/44 [==============================] - 0s 3ms/step - loss: 0.4561 - accuracy: 0.7700 - val_loss: 0.6026 - val_accuracy: 0.7035\n",
            "Epoch 196/200\n",
            "44/44 [==============================] - 0s 3ms/step - loss: 0.4565 - accuracy: 0.7686 - val_loss: 0.5904 - val_accuracy: 0.7052\n",
            "Epoch 197/200\n",
            "44/44 [==============================] - 0s 3ms/step - loss: 0.4560 - accuracy: 0.7722 - val_loss: 0.5928 - val_accuracy: 0.7008\n",
            "Epoch 198/200\n",
            "44/44 [==============================] - 0s 3ms/step - loss: 0.4575 - accuracy: 0.7674 - val_loss: 0.5891 - val_accuracy: 0.7098\n",
            "Epoch 199/200\n",
            "44/44 [==============================] - 0s 3ms/step - loss: 0.4547 - accuracy: 0.7719 - val_loss: 0.6004 - val_accuracy: 0.6988\n",
            "Epoch 200/200\n",
            "44/44 [==============================] - 0s 3ms/step - loss: 0.4520 - accuracy: 0.7708 - val_loss: 0.6054 - val_accuracy: 0.6963\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<tensorflow.python.keras.callbacks.History at 0x7fe3b93b36a0>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 65
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "82Wd8o-vVPid",
        "outputId": "fda2db81-fa57-4009-aa23-6f531765a92e",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 282
        }
      },
      "source": [
        "pd.DataFrame(model.history.history)[['loss','val_loss']].plot() #over fitting "
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7fe3b93b3f60>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 66
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "5d0ZKtYGVYal",
        "outputId": "fd2d69a9-68f5-43b5-dbd0-4179f6cf75da",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "source": [
        "predictions = model.predict_classes(X_test)\n",
        "\n",
        "print(\n",
        "        confusion_matrix(y_test,predictions), \n",
        "        '\\n', \n",
        "        classification_report(y_test,predictions)\n",
        ")"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "[[1595  842]\n",
            " [ 624 1766]] \n",
            "               precision    recall  f1-score   support\n",
            "\n",
            "           0       0.72      0.65      0.69      2437\n",
            "           1       0.68      0.74      0.71      2390\n",
            "\n",
            "    accuracy                           0.70      4827\n",
            "   macro avg       0.70      0.70      0.70      4827\n",
            "weighted avg       0.70      0.70      0.70      4827\n",
            "\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "4tDMxE_tVemO",
        "outputId": "5208ddc7-4eee-4d3a-ba6f-40ed377addf3",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "source": [
        "model_new = Sequential()\n",
        "\n",
        "model_new.add(\n",
        "        Dense(94, activation='relu')\n",
        ")\n",
        "\n",
        "model_new.add(Dropout(0.2))\n",
        "\n",
        "model_new.add(\n",
        "        Dense(30, activation='relu')\n",
        ")\n",
        "\n",
        "model_new.add(Dropout(0.2))\n",
        "\n",
        "model_new.add(\n",
        "        Dense(15, activation='relu')\n",
        ")\n",
        "\n",
        "model_new.add(Dropout(0.2))\n",
        "\n",
        "model_new.add(\n",
        "        Dense(1, activation='sigmoid')\n",
        ")\n",
        "\n",
        "model_new.compile(\n",
        "        optimizer='adam', \n",
        "        loss='binary_crossentropy', \n",
        "        metrics=['binary_accuracy']\n",
        ")\n",
        "\n",
        "\n",
        "model_new.fit(\n",
        "        X_train, \n",
        "        y_train, \n",
        "        epochs=200, \n",
        "        batch_size=256, \n",
        "        validation_data=(X_test, y_test),\n",
        "         callbacks=[early_stop]\n",
        ")"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Epoch 1/200\n",
            "44/44 [==============================] - 0s 6ms/step - loss: 0.6840 - binary_accuracy: 0.5617 - val_loss: 0.6701 - val_binary_accuracy: 0.6066\n",
            "Epoch 2/200\n",
            "44/44 [==============================] - 0s 4ms/step - loss: 0.6638 - binary_accuracy: 0.6039 - val_loss: 0.6605 - val_binary_accuracy: 0.6020\n",
            "Epoch 3/200\n",
            "44/44 [==============================] - 0s 3ms/step - loss: 0.6563 - binary_accuracy: 0.6139 - val_loss: 0.6566 - val_binary_accuracy: 0.6039\n",
            "Epoch 4/200\n",
            "44/44 [==============================] - 0s 3ms/step - loss: 0.6560 - binary_accuracy: 0.6113 - val_loss: 0.6559 - val_binary_accuracy: 0.6147\n",
            "Epoch 5/200\n",
            "44/44 [==============================] - 0s 3ms/step - loss: 0.6527 - binary_accuracy: 0.6148 - val_loss: 0.6534 - val_binary_accuracy: 0.6130\n",
            "Epoch 6/200\n",
            "44/44 [==============================] - 0s 4ms/step - loss: 0.6496 - binary_accuracy: 0.6224 - val_loss: 0.6524 - val_binary_accuracy: 0.6151\n",
            "Epoch 7/200\n",
            "44/44 [==============================] - 0s 4ms/step - loss: 0.6496 - binary_accuracy: 0.6231 - val_loss: 0.6515 - val_binary_accuracy: 0.6128\n",
            "Epoch 8/200\n",
            "44/44 [==============================] - 0s 4ms/step - loss: 0.6480 - binary_accuracy: 0.6185 - val_loss: 0.6502 - val_binary_accuracy: 0.6174\n",
            "Epoch 9/200\n",
            "44/44 [==============================] - 0s 3ms/step - loss: 0.6465 - binary_accuracy: 0.6157 - val_loss: 0.6501 - val_binary_accuracy: 0.6207\n",
            "Epoch 10/200\n",
            "44/44 [==============================] - 0s 4ms/step - loss: 0.6446 - binary_accuracy: 0.6227 - val_loss: 0.6498 - val_binary_accuracy: 0.6186\n",
            "Epoch 11/200\n",
            "44/44 [==============================] - 0s 3ms/step - loss: 0.6447 - binary_accuracy: 0.6249 - val_loss: 0.6479 - val_binary_accuracy: 0.6155\n",
            "Epoch 12/200\n",
            "44/44 [==============================] - 0s 3ms/step - loss: 0.6447 - binary_accuracy: 0.6283 - val_loss: 0.6480 - val_binary_accuracy: 0.6203\n",
            "Epoch 13/200\n",
            "44/44 [==============================] - 0s 3ms/step - loss: 0.6430 - binary_accuracy: 0.6209 - val_loss: 0.6462 - val_binary_accuracy: 0.6163\n",
            "Epoch 14/200\n",
            "44/44 [==============================] - 0s 3ms/step - loss: 0.6399 - binary_accuracy: 0.6251 - val_loss: 0.6460 - val_binary_accuracy: 0.6153\n",
            "Epoch 15/200\n",
            "44/44 [==============================] - 0s 3ms/step - loss: 0.6398 - binary_accuracy: 0.6295 - val_loss: 0.6454 - val_binary_accuracy: 0.6180\n",
            "Epoch 16/200\n",
            "44/44 [==============================] - 0s 4ms/step - loss: 0.6414 - binary_accuracy: 0.6247 - val_loss: 0.6444 - val_binary_accuracy: 0.6230\n",
            "Epoch 17/200\n",
            "44/44 [==============================] - 0s 4ms/step - loss: 0.6388 - binary_accuracy: 0.6250 - val_loss: 0.6440 - val_binary_accuracy: 0.6157\n",
            "Epoch 18/200\n",
            "44/44 [==============================] - 0s 4ms/step - loss: 0.6392 - binary_accuracy: 0.6292 - val_loss: 0.6432 - val_binary_accuracy: 0.6221\n",
            "Epoch 19/200\n",
            "44/44 [==============================] - 0s 4ms/step - loss: 0.6375 - binary_accuracy: 0.6305 - val_loss: 0.6431 - val_binary_accuracy: 0.6221\n",
            "Epoch 20/200\n",
            "44/44 [==============================] - 0s 3ms/step - loss: 0.6374 - binary_accuracy: 0.6318 - val_loss: 0.6420 - val_binary_accuracy: 0.6234\n",
            "Epoch 21/200\n",
            "44/44 [==============================] - 0s 3ms/step - loss: 0.6382 - binary_accuracy: 0.6322 - val_loss: 0.6424 - val_binary_accuracy: 0.6236\n",
            "Epoch 22/200\n",
            "44/44 [==============================] - 0s 4ms/step - loss: 0.6366 - binary_accuracy: 0.6275 - val_loss: 0.6416 - val_binary_accuracy: 0.6207\n",
            "Epoch 23/200\n",
            "44/44 [==============================] - 0s 4ms/step - loss: 0.6368 - binary_accuracy: 0.6298 - val_loss: 0.6414 - val_binary_accuracy: 0.6244\n",
            "Epoch 24/200\n",
            "44/44 [==============================] - 0s 3ms/step - loss: 0.6340 - binary_accuracy: 0.6322 - val_loss: 0.6406 - val_binary_accuracy: 0.6261\n",
            "Epoch 25/200\n",
            "44/44 [==============================] - 0s 4ms/step - loss: 0.6363 - binary_accuracy: 0.6271 - val_loss: 0.6398 - val_binary_accuracy: 0.6215\n",
            "Epoch 26/200\n",
            "44/44 [==============================] - 0s 4ms/step - loss: 0.6339 - binary_accuracy: 0.6307 - val_loss: 0.6401 - val_binary_accuracy: 0.6275\n",
            "Epoch 27/200\n",
            "44/44 [==============================] - 0s 4ms/step - loss: 0.6326 - binary_accuracy: 0.6287 - val_loss: 0.6408 - val_binary_accuracy: 0.6250\n",
            "Epoch 28/200\n",
            "44/44 [==============================] - 0s 3ms/step - loss: 0.6330 - binary_accuracy: 0.6378 - val_loss: 0.6384 - val_binary_accuracy: 0.6263\n",
            "Epoch 29/200\n",
            "44/44 [==============================] - 0s 3ms/step - loss: 0.6315 - binary_accuracy: 0.6335 - val_loss: 0.6382 - val_binary_accuracy: 0.6277\n",
            "Epoch 30/200\n",
            "44/44 [==============================] - 0s 4ms/step - loss: 0.6311 - binary_accuracy: 0.6338 - val_loss: 0.6389 - val_binary_accuracy: 0.6259\n",
            "Epoch 31/200\n",
            "44/44 [==============================] - 0s 3ms/step - loss: 0.6301 - binary_accuracy: 0.6350 - val_loss: 0.6383 - val_binary_accuracy: 0.6281\n",
            "Epoch 32/200\n",
            "44/44 [==============================] - 0s 4ms/step - loss: 0.6297 - binary_accuracy: 0.6379 - val_loss: 0.6372 - val_binary_accuracy: 0.6252\n",
            "Epoch 33/200\n",
            "44/44 [==============================] - 0s 3ms/step - loss: 0.6296 - binary_accuracy: 0.6357 - val_loss: 0.6359 - val_binary_accuracy: 0.6296\n",
            "Epoch 34/200\n",
            "44/44 [==============================] - 0s 3ms/step - loss: 0.6293 - binary_accuracy: 0.6370 - val_loss: 0.6358 - val_binary_accuracy: 0.6285\n",
            "Epoch 35/200\n",
            "44/44 [==============================] - 0s 3ms/step - loss: 0.6308 - binary_accuracy: 0.6385 - val_loss: 0.6363 - val_binary_accuracy: 0.6261\n",
            "Epoch 36/200\n",
            "44/44 [==============================] - 0s 3ms/step - loss: 0.6283 - binary_accuracy: 0.6417 - val_loss: 0.6371 - val_binary_accuracy: 0.6227\n",
            "Epoch 37/200\n",
            "44/44 [==============================] - 0s 3ms/step - loss: 0.6266 - binary_accuracy: 0.6394 - val_loss: 0.6358 - val_binary_accuracy: 0.6283\n",
            "Epoch 38/200\n",
            "44/44 [==============================] - 0s 3ms/step - loss: 0.6280 - binary_accuracy: 0.6394 - val_loss: 0.6355 - val_binary_accuracy: 0.6327\n",
            "Epoch 39/200\n",
            "44/44 [==============================] - 0s 3ms/step - loss: 0.6252 - binary_accuracy: 0.6370 - val_loss: 0.6342 - val_binary_accuracy: 0.6335\n",
            "Epoch 40/200\n",
            "44/44 [==============================] - 0s 3ms/step - loss: 0.6265 - binary_accuracy: 0.6367 - val_loss: 0.6340 - val_binary_accuracy: 0.6294\n",
            "Epoch 41/200\n",
            "44/44 [==============================] - 0s 4ms/step - loss: 0.6266 - binary_accuracy: 0.6350 - val_loss: 0.6338 - val_binary_accuracy: 0.6314\n",
            "Epoch 42/200\n",
            "44/44 [==============================] - 0s 3ms/step - loss: 0.6253 - binary_accuracy: 0.6396 - val_loss: 0.6330 - val_binary_accuracy: 0.6314\n",
            "Epoch 43/200\n",
            "44/44 [==============================] - 0s 3ms/step - loss: 0.6261 - binary_accuracy: 0.6393 - val_loss: 0.6356 - val_binary_accuracy: 0.6319\n",
            "Epoch 44/200\n",
            "44/44 [==============================] - 0s 4ms/step - loss: 0.6241 - binary_accuracy: 0.6407 - val_loss: 0.6333 - val_binary_accuracy: 0.6341\n",
            "Epoch 45/200\n",
            "44/44 [==============================] - 0s 3ms/step - loss: 0.6231 - binary_accuracy: 0.6399 - val_loss: 0.6329 - val_binary_accuracy: 0.6319\n",
            "Epoch 46/200\n",
            "44/44 [==============================] - 0s 3ms/step - loss: 0.6223 - binary_accuracy: 0.6433 - val_loss: 0.6322 - val_binary_accuracy: 0.6331\n",
            "Epoch 47/200\n",
            "44/44 [==============================] - 0s 4ms/step - loss: 0.6228 - binary_accuracy: 0.6450 - val_loss: 0.6312 - val_binary_accuracy: 0.6346\n",
            "Epoch 48/200\n",
            "44/44 [==============================] - 0s 4ms/step - loss: 0.6216 - binary_accuracy: 0.6465 - val_loss: 0.6332 - val_binary_accuracy: 0.6308\n",
            "Epoch 49/200\n",
            "44/44 [==============================] - 0s 3ms/step - loss: 0.6196 - binary_accuracy: 0.6457 - val_loss: 0.6308 - val_binary_accuracy: 0.6333\n",
            "Epoch 50/200\n",
            "44/44 [==============================] - 0s 4ms/step - loss: 0.6196 - binary_accuracy: 0.6414 - val_loss: 0.6319 - val_binary_accuracy: 0.6339\n",
            "Epoch 51/200\n",
            "44/44 [==============================] - 0s 3ms/step - loss: 0.6201 - binary_accuracy: 0.6415 - val_loss: 0.6309 - val_binary_accuracy: 0.6331\n",
            "Epoch 52/200\n",
            "44/44 [==============================] - 0s 3ms/step - loss: 0.6195 - binary_accuracy: 0.6450 - val_loss: 0.6340 - val_binary_accuracy: 0.6323\n",
            "Epoch 53/200\n",
            "44/44 [==============================] - 0s 4ms/step - loss: 0.6209 - binary_accuracy: 0.6453 - val_loss: 0.6304 - val_binary_accuracy: 0.6391\n",
            "Epoch 54/200\n",
            "44/44 [==============================] - 0s 4ms/step - loss: 0.6207 - binary_accuracy: 0.6454 - val_loss: 0.6310 - val_binary_accuracy: 0.6337\n",
            "Epoch 55/200\n",
            "44/44 [==============================] - 0s 3ms/step - loss: 0.6181 - binary_accuracy: 0.6444 - val_loss: 0.6291 - val_binary_accuracy: 0.6364\n",
            "Epoch 56/200\n",
            "44/44 [==============================] - 0s 4ms/step - loss: 0.6162 - binary_accuracy: 0.6497 - val_loss: 0.6316 - val_binary_accuracy: 0.6354\n",
            "Epoch 57/200\n",
            "44/44 [==============================] - 0s 3ms/step - loss: 0.6174 - binary_accuracy: 0.6494 - val_loss: 0.6288 - val_binary_accuracy: 0.6391\n",
            "Epoch 58/200\n",
            "44/44 [==============================] - 0s 4ms/step - loss: 0.6169 - binary_accuracy: 0.6512 - val_loss: 0.6282 - val_binary_accuracy: 0.6356\n",
            "Epoch 59/200\n",
            "44/44 [==============================] - 0s 4ms/step - loss: 0.6147 - binary_accuracy: 0.6530 - val_loss: 0.6281 - val_binary_accuracy: 0.6426\n",
            "Epoch 60/200\n",
            "44/44 [==============================] - 0s 4ms/step - loss: 0.6152 - binary_accuracy: 0.6547 - val_loss: 0.6284 - val_binary_accuracy: 0.6430\n",
            "Epoch 61/200\n",
            "44/44 [==============================] - 0s 3ms/step - loss: 0.6144 - binary_accuracy: 0.6535 - val_loss: 0.6270 - val_binary_accuracy: 0.6399\n",
            "Epoch 62/200\n",
            "44/44 [==============================] - 0s 4ms/step - loss: 0.6135 - binary_accuracy: 0.6522 - val_loss: 0.6277 - val_binary_accuracy: 0.6449\n",
            "Epoch 63/200\n",
            "44/44 [==============================] - 0s 4ms/step - loss: 0.6158 - binary_accuracy: 0.6547 - val_loss: 0.6283 - val_binary_accuracy: 0.6375\n",
            "Epoch 64/200\n",
            "44/44 [==============================] - 0s 3ms/step - loss: 0.6137 - binary_accuracy: 0.6469 - val_loss: 0.6279 - val_binary_accuracy: 0.6401\n",
            "Epoch 65/200\n",
            "44/44 [==============================] - 0s 4ms/step - loss: 0.6104 - binary_accuracy: 0.6547 - val_loss: 0.6281 - val_binary_accuracy: 0.6383\n",
            "Epoch 66/200\n",
            "44/44 [==============================] - 0s 3ms/step - loss: 0.6111 - binary_accuracy: 0.6544 - val_loss: 0.6269 - val_binary_accuracy: 0.6395\n",
            "Epoch 67/200\n",
            "44/44 [==============================] - 0s 3ms/step - loss: 0.6105 - binary_accuracy: 0.6537 - val_loss: 0.6250 - val_binary_accuracy: 0.6418\n",
            "Epoch 68/200\n",
            "44/44 [==============================] - 0s 4ms/step - loss: 0.6127 - binary_accuracy: 0.6540 - val_loss: 0.6255 - val_binary_accuracy: 0.6462\n",
            "Epoch 69/200\n",
            "44/44 [==============================] - 0s 3ms/step - loss: 0.6109 - binary_accuracy: 0.6566 - val_loss: 0.6248 - val_binary_accuracy: 0.6424\n",
            "Epoch 70/200\n",
            "44/44 [==============================] - 0s 3ms/step - loss: 0.6066 - binary_accuracy: 0.6561 - val_loss: 0.6227 - val_binary_accuracy: 0.6495\n",
            "Epoch 71/200\n",
            "44/44 [==============================] - 0s 3ms/step - loss: 0.6114 - binary_accuracy: 0.6513 - val_loss: 0.6238 - val_binary_accuracy: 0.6524\n",
            "Epoch 72/200\n",
            "44/44 [==============================] - 0s 4ms/step - loss: 0.6103 - binary_accuracy: 0.6536 - val_loss: 0.6237 - val_binary_accuracy: 0.6474\n",
            "Epoch 73/200\n",
            "44/44 [==============================] - 0s 4ms/step - loss: 0.6072 - binary_accuracy: 0.6556 - val_loss: 0.6244 - val_binary_accuracy: 0.6462\n",
            "Epoch 74/200\n",
            "44/44 [==============================] - 0s 3ms/step - loss: 0.6091 - binary_accuracy: 0.6551 - val_loss: 0.6213 - val_binary_accuracy: 0.6536\n",
            "Epoch 75/200\n",
            "44/44 [==============================] - 0s 3ms/step - loss: 0.6056 - binary_accuracy: 0.6596 - val_loss: 0.6230 - val_binary_accuracy: 0.6474\n",
            "Epoch 76/200\n",
            "44/44 [==============================] - 0s 4ms/step - loss: 0.6056 - binary_accuracy: 0.6577 - val_loss: 0.6223 - val_binary_accuracy: 0.6486\n",
            "Epoch 77/200\n",
            "44/44 [==============================] - 0s 4ms/step - loss: 0.6064 - binary_accuracy: 0.6582 - val_loss: 0.6215 - val_binary_accuracy: 0.6489\n",
            "Epoch 78/200\n",
            "44/44 [==============================] - 0s 4ms/step - loss: 0.6026 - binary_accuracy: 0.6611 - val_loss: 0.6225 - val_binary_accuracy: 0.6466\n",
            "Epoch 79/200\n",
            "44/44 [==============================] - 0s 4ms/step - loss: 0.6036 - binary_accuracy: 0.6603 - val_loss: 0.6233 - val_binary_accuracy: 0.6462\n",
            "Epoch 80/200\n",
            "44/44 [==============================] - 0s 4ms/step - loss: 0.6011 - binary_accuracy: 0.6651 - val_loss: 0.6209 - val_binary_accuracy: 0.6482\n",
            "Epoch 81/200\n",
            "44/44 [==============================] - 0s 4ms/step - loss: 0.6013 - binary_accuracy: 0.6621 - val_loss: 0.6205 - val_binary_accuracy: 0.6530\n",
            "Epoch 82/200\n",
            "44/44 [==============================] - 0s 3ms/step - loss: 0.6033 - binary_accuracy: 0.6623 - val_loss: 0.6203 - val_binary_accuracy: 0.6538\n",
            "Epoch 83/200\n",
            "44/44 [==============================] - 0s 3ms/step - loss: 0.6011 - binary_accuracy: 0.6662 - val_loss: 0.6211 - val_binary_accuracy: 0.6542\n",
            "Epoch 84/200\n",
            "44/44 [==============================] - 0s 4ms/step - loss: 0.6023 - binary_accuracy: 0.6627 - val_loss: 0.6186 - val_binary_accuracy: 0.6538\n",
            "Epoch 85/200\n",
            "44/44 [==============================] - 0s 3ms/step - loss: 0.6011 - binary_accuracy: 0.6618 - val_loss: 0.6193 - val_binary_accuracy: 0.6544\n",
            "Epoch 86/200\n",
            "44/44 [==============================] - 0s 3ms/step - loss: 0.6023 - binary_accuracy: 0.6646 - val_loss: 0.6189 - val_binary_accuracy: 0.6538\n",
            "Epoch 87/200\n",
            "44/44 [==============================] - 0s 4ms/step - loss: 0.5976 - binary_accuracy: 0.6640 - val_loss: 0.6178 - val_binary_accuracy: 0.6561\n",
            "Epoch 88/200\n",
            "44/44 [==============================] - 0s 3ms/step - loss: 0.6005 - binary_accuracy: 0.6682 - val_loss: 0.6173 - val_binary_accuracy: 0.6567\n",
            "Epoch 89/200\n",
            "44/44 [==============================] - 0s 3ms/step - loss: 0.5994 - binary_accuracy: 0.6671 - val_loss: 0.6171 - val_binary_accuracy: 0.6609\n",
            "Epoch 90/200\n",
            "44/44 [==============================] - 0s 4ms/step - loss: 0.5989 - binary_accuracy: 0.6640 - val_loss: 0.6170 - val_binary_accuracy: 0.6582\n",
            "Epoch 91/200\n",
            "44/44 [==============================] - 0s 3ms/step - loss: 0.5996 - binary_accuracy: 0.6648 - val_loss: 0.6193 - val_binary_accuracy: 0.6538\n",
            "Epoch 92/200\n",
            "44/44 [==============================] - 0s 3ms/step - loss: 0.5996 - binary_accuracy: 0.6580 - val_loss: 0.6178 - val_binary_accuracy: 0.6538\n",
            "Epoch 93/200\n",
            "44/44 [==============================] - 0s 4ms/step - loss: 0.5977 - binary_accuracy: 0.6712 - val_loss: 0.6184 - val_binary_accuracy: 0.6530\n",
            "Epoch 94/200\n",
            "44/44 [==============================] - 0s 3ms/step - loss: 0.5987 - binary_accuracy: 0.6693 - val_loss: 0.6163 - val_binary_accuracy: 0.6567\n",
            "Epoch 95/200\n",
            "44/44 [==============================] - 0s 3ms/step - loss: 0.5950 - binary_accuracy: 0.6642 - val_loss: 0.6178 - val_binary_accuracy: 0.6542\n",
            "Epoch 96/200\n",
            "44/44 [==============================] - 0s 3ms/step - loss: 0.5930 - binary_accuracy: 0.6703 - val_loss: 0.6148 - val_binary_accuracy: 0.6553\n",
            "Epoch 97/200\n",
            "44/44 [==============================] - 0s 3ms/step - loss: 0.5950 - binary_accuracy: 0.6702 - val_loss: 0.6152 - val_binary_accuracy: 0.6573\n",
            "Epoch 98/200\n",
            "44/44 [==============================] - 0s 3ms/step - loss: 0.5946 - binary_accuracy: 0.6736 - val_loss: 0.6148 - val_binary_accuracy: 0.6592\n",
            "Epoch 99/200\n",
            "44/44 [==============================] - 0s 4ms/step - loss: 0.5911 - binary_accuracy: 0.6710 - val_loss: 0.6154 - val_binary_accuracy: 0.6613\n",
            "Epoch 100/200\n",
            "44/44 [==============================] - 0s 4ms/step - loss: 0.5918 - binary_accuracy: 0.6788 - val_loss: 0.6131 - val_binary_accuracy: 0.6588\n",
            "Epoch 101/200\n",
            "44/44 [==============================] - 0s 4ms/step - loss: 0.5948 - binary_accuracy: 0.6733 - val_loss: 0.6126 - val_binary_accuracy: 0.6547\n",
            "Epoch 102/200\n",
            "44/44 [==============================] - 0s 3ms/step - loss: 0.5888 - binary_accuracy: 0.6807 - val_loss: 0.6107 - val_binary_accuracy: 0.6623\n",
            "Epoch 103/200\n",
            "44/44 [==============================] - 0s 3ms/step - loss: 0.5919 - binary_accuracy: 0.6740 - val_loss: 0.6107 - val_binary_accuracy: 0.6656\n",
            "Epoch 104/200\n",
            "44/44 [==============================] - 0s 3ms/step - loss: 0.5936 - binary_accuracy: 0.6709 - val_loss: 0.6103 - val_binary_accuracy: 0.6611\n",
            "Epoch 105/200\n",
            "44/44 [==============================] - 0s 3ms/step - loss: 0.5888 - binary_accuracy: 0.6752 - val_loss: 0.6111 - val_binary_accuracy: 0.6582\n",
            "Epoch 106/200\n",
            "44/44 [==============================] - 0s 4ms/step - loss: 0.5924 - binary_accuracy: 0.6731 - val_loss: 0.6101 - val_binary_accuracy: 0.6663\n",
            "Epoch 107/200\n",
            "44/44 [==============================] - 0s 3ms/step - loss: 0.5868 - binary_accuracy: 0.6801 - val_loss: 0.6116 - val_binary_accuracy: 0.6613\n",
            "Epoch 108/200\n",
            "44/44 [==============================] - 0s 4ms/step - loss: 0.5915 - binary_accuracy: 0.6725 - val_loss: 0.6112 - val_binary_accuracy: 0.6609\n",
            "Epoch 109/200\n",
            "44/44 [==============================] - 0s 4ms/step - loss: 0.5857 - binary_accuracy: 0.6814 - val_loss: 0.6102 - val_binary_accuracy: 0.6642\n",
            "Epoch 110/200\n",
            "44/44 [==============================] - 0s 4ms/step - loss: 0.5857 - binary_accuracy: 0.6796 - val_loss: 0.6090 - val_binary_accuracy: 0.6665\n",
            "Epoch 111/200\n",
            "44/44 [==============================] - 0s 4ms/step - loss: 0.5882 - binary_accuracy: 0.6796 - val_loss: 0.6095 - val_binary_accuracy: 0.6636\n",
            "Epoch 112/200\n",
            "44/44 [==============================] - 0s 4ms/step - loss: 0.5887 - binary_accuracy: 0.6789 - val_loss: 0.6088 - val_binary_accuracy: 0.6613\n",
            "Epoch 113/200\n",
            "44/44 [==============================] - 0s 4ms/step - loss: 0.5843 - binary_accuracy: 0.6788 - val_loss: 0.6089 - val_binary_accuracy: 0.6660\n",
            "Epoch 114/200\n",
            "44/44 [==============================] - 0s 4ms/step - loss: 0.5842 - binary_accuracy: 0.6811 - val_loss: 0.6084 - val_binary_accuracy: 0.6607\n",
            "Epoch 115/200\n",
            "44/44 [==============================] - 0s 3ms/step - loss: 0.5860 - binary_accuracy: 0.6797 - val_loss: 0.6091 - val_binary_accuracy: 0.6605\n",
            "Epoch 116/200\n",
            "44/44 [==============================] - 0s 4ms/step - loss: 0.5899 - binary_accuracy: 0.6759 - val_loss: 0.6095 - val_binary_accuracy: 0.6638\n",
            "Epoch 117/200\n",
            "44/44 [==============================] - 0s 4ms/step - loss: 0.5855 - binary_accuracy: 0.6814 - val_loss: 0.6106 - val_binary_accuracy: 0.6615\n",
            "Epoch 118/200\n",
            "44/44 [==============================] - 0s 4ms/step - loss: 0.5846 - binary_accuracy: 0.6797 - val_loss: 0.6059 - val_binary_accuracy: 0.6646\n",
            "Epoch 119/200\n",
            "44/44 [==============================] - 0s 3ms/step - loss: 0.5832 - binary_accuracy: 0.6863 - val_loss: 0.6088 - val_binary_accuracy: 0.6665\n",
            "Epoch 120/200\n",
            "44/44 [==============================] - 0s 4ms/step - loss: 0.5833 - binary_accuracy: 0.6829 - val_loss: 0.6064 - val_binary_accuracy: 0.6621\n",
            "Epoch 121/200\n",
            "44/44 [==============================] - 0s 4ms/step - loss: 0.5817 - binary_accuracy: 0.6853 - val_loss: 0.6081 - val_binary_accuracy: 0.6621\n",
            "Epoch 122/200\n",
            "44/44 [==============================] - 0s 3ms/step - loss: 0.5832 - binary_accuracy: 0.6809 - val_loss: 0.6065 - val_binary_accuracy: 0.6613\n",
            "Epoch 123/200\n",
            "44/44 [==============================] - 0s 4ms/step - loss: 0.5817 - binary_accuracy: 0.6860 - val_loss: 0.6034 - val_binary_accuracy: 0.6644\n",
            "Epoch 124/200\n",
            "44/44 [==============================] - 0s 4ms/step - loss: 0.5781 - binary_accuracy: 0.6831 - val_loss: 0.6055 - val_binary_accuracy: 0.6700\n",
            "Epoch 125/200\n",
            "44/44 [==============================] - 0s 3ms/step - loss: 0.5806 - binary_accuracy: 0.6837 - val_loss: 0.6041 - val_binary_accuracy: 0.6658\n",
            "Epoch 126/200\n",
            "44/44 [==============================] - 0s 4ms/step - loss: 0.5813 - binary_accuracy: 0.6831 - val_loss: 0.6034 - val_binary_accuracy: 0.6665\n",
            "Epoch 127/200\n",
            "44/44 [==============================] - 0s 3ms/step - loss: 0.5780 - binary_accuracy: 0.6898 - val_loss: 0.6047 - val_binary_accuracy: 0.6646\n",
            "Epoch 128/200\n",
            "44/44 [==============================] - 0s 4ms/step - loss: 0.5770 - binary_accuracy: 0.6874 - val_loss: 0.6086 - val_binary_accuracy: 0.6631\n",
            "Epoch 129/200\n",
            "44/44 [==============================] - 0s 3ms/step - loss: 0.5782 - binary_accuracy: 0.6895 - val_loss: 0.6030 - val_binary_accuracy: 0.6648\n",
            "Epoch 130/200\n",
            "44/44 [==============================] - 0s 4ms/step - loss: 0.5776 - binary_accuracy: 0.6882 - val_loss: 0.6033 - val_binary_accuracy: 0.6708\n",
            "Epoch 131/200\n",
            "44/44 [==============================] - 0s 4ms/step - loss: 0.5764 - binary_accuracy: 0.6881 - val_loss: 0.6004 - val_binary_accuracy: 0.6729\n",
            "Epoch 132/200\n",
            "44/44 [==============================] - 0s 4ms/step - loss: 0.5743 - binary_accuracy: 0.6908 - val_loss: 0.6017 - val_binary_accuracy: 0.6700\n",
            "Epoch 133/200\n",
            "44/44 [==============================] - 0s 4ms/step - loss: 0.5732 - binary_accuracy: 0.6888 - val_loss: 0.6045 - val_binary_accuracy: 0.6675\n",
            "Epoch 134/200\n",
            "44/44 [==============================] - 0s 3ms/step - loss: 0.5775 - binary_accuracy: 0.6888 - val_loss: 0.6006 - val_binary_accuracy: 0.6745\n",
            "Epoch 135/200\n",
            "44/44 [==============================] - 0s 4ms/step - loss: 0.5764 - binary_accuracy: 0.6842 - val_loss: 0.5993 - val_binary_accuracy: 0.6712\n",
            "Epoch 136/200\n",
            "44/44 [==============================] - 0s 4ms/step - loss: 0.5747 - binary_accuracy: 0.6914 - val_loss: 0.6008 - val_binary_accuracy: 0.6692\n",
            "Epoch 137/200\n",
            "44/44 [==============================] - 0s 4ms/step - loss: 0.5723 - binary_accuracy: 0.6924 - val_loss: 0.5991 - val_binary_accuracy: 0.6708\n",
            "Epoch 138/200\n",
            "44/44 [==============================] - 0s 3ms/step - loss: 0.5759 - binary_accuracy: 0.6871 - val_loss: 0.6006 - val_binary_accuracy: 0.6685\n",
            "Epoch 139/200\n",
            "44/44 [==============================] - 0s 3ms/step - loss: 0.5705 - binary_accuracy: 0.6900 - val_loss: 0.5978 - val_binary_accuracy: 0.6700\n",
            "Epoch 140/200\n",
            "44/44 [==============================] - 0s 3ms/step - loss: 0.5715 - binary_accuracy: 0.6949 - val_loss: 0.6011 - val_binary_accuracy: 0.6665\n",
            "Epoch 141/200\n",
            "44/44 [==============================] - 0s 3ms/step - loss: 0.5774 - binary_accuracy: 0.6842 - val_loss: 0.5995 - val_binary_accuracy: 0.6716\n",
            "Epoch 142/200\n",
            "44/44 [==============================] - 0s 3ms/step - loss: 0.5755 - binary_accuracy: 0.6915 - val_loss: 0.5980 - val_binary_accuracy: 0.6727\n",
            "Epoch 143/200\n",
            "44/44 [==============================] - 0s 3ms/step - loss: 0.5697 - binary_accuracy: 0.6938 - val_loss: 0.5970 - val_binary_accuracy: 0.6718\n",
            "Epoch 144/200\n",
            "44/44 [==============================] - 0s 3ms/step - loss: 0.5718 - binary_accuracy: 0.6970 - val_loss: 0.5978 - val_binary_accuracy: 0.6741\n",
            "Epoch 145/200\n",
            "44/44 [==============================] - 0s 3ms/step - loss: 0.5748 - binary_accuracy: 0.6887 - val_loss: 0.5961 - val_binary_accuracy: 0.6772\n",
            "Epoch 146/200\n",
            "44/44 [==============================] - 0s 3ms/step - loss: 0.5724 - binary_accuracy: 0.6910 - val_loss: 0.5973 - val_binary_accuracy: 0.6747\n",
            "Epoch 147/200\n",
            "44/44 [==============================] - 0s 3ms/step - loss: 0.5716 - binary_accuracy: 0.6910 - val_loss: 0.5989 - val_binary_accuracy: 0.6766\n",
            "Epoch 148/200\n",
            "44/44 [==============================] - 0s 4ms/step - loss: 0.5697 - binary_accuracy: 0.6930 - val_loss: 0.5947 - val_binary_accuracy: 0.6735\n",
            "Epoch 149/200\n",
            "44/44 [==============================] - 0s 3ms/step - loss: 0.5731 - binary_accuracy: 0.6924 - val_loss: 0.5941 - val_binary_accuracy: 0.6764\n",
            "Epoch 150/200\n",
            "44/44 [==============================] - 0s 3ms/step - loss: 0.5699 - binary_accuracy: 0.6934 - val_loss: 0.5959 - val_binary_accuracy: 0.6824\n",
            "Epoch 151/200\n",
            "44/44 [==============================] - 0s 3ms/step - loss: 0.5660 - binary_accuracy: 0.6945 - val_loss: 0.5937 - val_binary_accuracy: 0.6783\n",
            "Epoch 152/200\n",
            "44/44 [==============================] - 0s 3ms/step - loss: 0.5642 - binary_accuracy: 0.7001 - val_loss: 0.5950 - val_binary_accuracy: 0.6772\n",
            "Epoch 153/200\n",
            "44/44 [==============================] - 0s 3ms/step - loss: 0.5653 - binary_accuracy: 0.6967 - val_loss: 0.5921 - val_binary_accuracy: 0.6816\n",
            "Epoch 154/200\n",
            "44/44 [==============================] - 0s 3ms/step - loss: 0.5665 - binary_accuracy: 0.6933 - val_loss: 0.5955 - val_binary_accuracy: 0.6741\n",
            "Epoch 155/200\n",
            "44/44 [==============================] - 0s 3ms/step - loss: 0.5670 - binary_accuracy: 0.6991 - val_loss: 0.5940 - val_binary_accuracy: 0.6810\n",
            "Epoch 156/200\n",
            "44/44 [==============================] - 0s 4ms/step - loss: 0.5663 - binary_accuracy: 0.6961 - val_loss: 0.5929 - val_binary_accuracy: 0.6849\n",
            "Epoch 157/200\n",
            "44/44 [==============================] - 0s 4ms/step - loss: 0.5657 - binary_accuracy: 0.6982 - val_loss: 0.5903 - val_binary_accuracy: 0.6803\n",
            "Epoch 158/200\n",
            "44/44 [==============================] - 0s 4ms/step - loss: 0.5660 - binary_accuracy: 0.6955 - val_loss: 0.5959 - val_binary_accuracy: 0.6760\n",
            "Epoch 159/200\n",
            "44/44 [==============================] - 0s 4ms/step - loss: 0.5635 - binary_accuracy: 0.6990 - val_loss: 0.5891 - val_binary_accuracy: 0.6820\n",
            "Epoch 160/200\n",
            "44/44 [==============================] - 0s 4ms/step - loss: 0.5626 - binary_accuracy: 0.6982 - val_loss: 0.5947 - val_binary_accuracy: 0.6814\n",
            "Epoch 161/200\n",
            "44/44 [==============================] - 0s 4ms/step - loss: 0.5657 - binary_accuracy: 0.6979 - val_loss: 0.5932 - val_binary_accuracy: 0.6828\n",
            "Epoch 162/200\n",
            "44/44 [==============================] - 0s 4ms/step - loss: 0.5636 - binary_accuracy: 0.6989 - val_loss: 0.5917 - val_binary_accuracy: 0.6812\n",
            "Epoch 163/200\n",
            "44/44 [==============================] - 0s 4ms/step - loss: 0.5603 - binary_accuracy: 0.6979 - val_loss: 0.5902 - val_binary_accuracy: 0.6861\n",
            "Epoch 164/200\n",
            "44/44 [==============================] - 0s 4ms/step - loss: 0.5637 - binary_accuracy: 0.6998 - val_loss: 0.5908 - val_binary_accuracy: 0.6818\n",
            "Epoch 165/200\n",
            "44/44 [==============================] - 0s 4ms/step - loss: 0.5604 - binary_accuracy: 0.7019 - val_loss: 0.5887 - val_binary_accuracy: 0.6830\n",
            "Epoch 166/200\n",
            "44/44 [==============================] - 0s 4ms/step - loss: 0.5628 - binary_accuracy: 0.6995 - val_loss: 0.5895 - val_binary_accuracy: 0.6876\n",
            "Epoch 167/200\n",
            "44/44 [==============================] - 0s 4ms/step - loss: 0.5613 - binary_accuracy: 0.7019 - val_loss: 0.5905 - val_binary_accuracy: 0.6839\n",
            "Epoch 168/200\n",
            "44/44 [==============================] - 0s 4ms/step - loss: 0.5599 - binary_accuracy: 0.7042 - val_loss: 0.5871 - val_binary_accuracy: 0.6911\n",
            "Epoch 169/200\n",
            "44/44 [==============================] - 0s 4ms/step - loss: 0.5595 - binary_accuracy: 0.7043 - val_loss: 0.5884 - val_binary_accuracy: 0.6849\n",
            "Epoch 170/200\n",
            "44/44 [==============================] - 0s 3ms/step - loss: 0.5580 - binary_accuracy: 0.7063 - val_loss: 0.5868 - val_binary_accuracy: 0.6849\n",
            "Epoch 171/200\n",
            "44/44 [==============================] - 0s 4ms/step - loss: 0.5594 - binary_accuracy: 0.7023 - val_loss: 0.5874 - val_binary_accuracy: 0.6803\n",
            "Epoch 172/200\n",
            "44/44 [==============================] - 0s 4ms/step - loss: 0.5632 - binary_accuracy: 0.6972 - val_loss: 0.5854 - val_binary_accuracy: 0.6849\n",
            "Epoch 173/200\n",
            "44/44 [==============================] - 0s 4ms/step - loss: 0.5594 - binary_accuracy: 0.7009 - val_loss: 0.5864 - val_binary_accuracy: 0.6859\n",
            "Epoch 174/200\n",
            "44/44 [==============================] - 0s 3ms/step - loss: 0.5535 - binary_accuracy: 0.7047 - val_loss: 0.5826 - val_binary_accuracy: 0.6880\n",
            "Epoch 175/200\n",
            "44/44 [==============================] - 0s 3ms/step - loss: 0.5558 - binary_accuracy: 0.7050 - val_loss: 0.5845 - val_binary_accuracy: 0.6886\n",
            "Epoch 176/200\n",
            "44/44 [==============================] - 0s 4ms/step - loss: 0.5565 - binary_accuracy: 0.7034 - val_loss: 0.5883 - val_binary_accuracy: 0.6853\n",
            "Epoch 177/200\n",
            "44/44 [==============================] - 0s 3ms/step - loss: 0.5595 - binary_accuracy: 0.7005 - val_loss: 0.5839 - val_binary_accuracy: 0.6909\n",
            "Epoch 178/200\n",
            "44/44 [==============================] - 0s 3ms/step - loss: 0.5567 - binary_accuracy: 0.7058 - val_loss: 0.5838 - val_binary_accuracy: 0.6905\n",
            "Epoch 179/200\n",
            "44/44 [==============================] - 0s 4ms/step - loss: 0.5603 - binary_accuracy: 0.7030 - val_loss: 0.5820 - val_binary_accuracy: 0.6924\n",
            "Epoch 180/200\n",
            "44/44 [==============================] - 0s 3ms/step - loss: 0.5563 - binary_accuracy: 0.7052 - val_loss: 0.5819 - val_binary_accuracy: 0.6913\n",
            "Epoch 181/200\n",
            "44/44 [==============================] - 0s 3ms/step - loss: 0.5551 - binary_accuracy: 0.7037 - val_loss: 0.5846 - val_binary_accuracy: 0.6928\n",
            "Epoch 182/200\n",
            "44/44 [==============================] - 0s 3ms/step - loss: 0.5591 - binary_accuracy: 0.7030 - val_loss: 0.5813 - val_binary_accuracy: 0.6895\n",
            "Epoch 183/200\n",
            "44/44 [==============================] - 0s 3ms/step - loss: 0.5520 - binary_accuracy: 0.7070 - val_loss: 0.5849 - val_binary_accuracy: 0.6892\n",
            "Epoch 184/200\n",
            "44/44 [==============================] - 0s 4ms/step - loss: 0.5552 - binary_accuracy: 0.7037 - val_loss: 0.5834 - val_binary_accuracy: 0.6828\n",
            "Epoch 185/200\n",
            "44/44 [==============================] - 0s 4ms/step - loss: 0.5532 - binary_accuracy: 0.7082 - val_loss: 0.5823 - val_binary_accuracy: 0.6880\n",
            "Epoch 186/200\n",
            "44/44 [==============================] - 0s 4ms/step - loss: 0.5526 - binary_accuracy: 0.7114 - val_loss: 0.5800 - val_binary_accuracy: 0.6911\n",
            "Epoch 187/200\n",
            "44/44 [==============================] - 0s 4ms/step - loss: 0.5558 - binary_accuracy: 0.7057 - val_loss: 0.5836 - val_binary_accuracy: 0.6870\n",
            "Epoch 188/200\n",
            "44/44 [==============================] - 0s 4ms/step - loss: 0.5561 - binary_accuracy: 0.7025 - val_loss: 0.5877 - val_binary_accuracy: 0.6791\n",
            "Epoch 189/200\n",
            "44/44 [==============================] - 0s 4ms/step - loss: 0.5529 - binary_accuracy: 0.7091 - val_loss: 0.5779 - val_binary_accuracy: 0.6917\n",
            "Epoch 190/200\n",
            "44/44 [==============================] - 0s 4ms/step - loss: 0.5473 - binary_accuracy: 0.7156 - val_loss: 0.5798 - val_binary_accuracy: 0.6897\n",
            "Epoch 191/200\n",
            "44/44 [==============================] - 0s 4ms/step - loss: 0.5516 - binary_accuracy: 0.7113 - val_loss: 0.5780 - val_binary_accuracy: 0.6924\n",
            "Epoch 192/200\n",
            "44/44 [==============================] - 0s 4ms/step - loss: 0.5548 - binary_accuracy: 0.7035 - val_loss: 0.5789 - val_binary_accuracy: 0.6932\n",
            "Epoch 193/200\n",
            "44/44 [==============================] - 0s 3ms/step - loss: 0.5505 - binary_accuracy: 0.7099 - val_loss: 0.5762 - val_binary_accuracy: 0.6917\n",
            "Epoch 194/200\n",
            "44/44 [==============================] - 0s 4ms/step - loss: 0.5484 - binary_accuracy: 0.7104 - val_loss: 0.5827 - val_binary_accuracy: 0.6903\n",
            "Epoch 195/200\n",
            "44/44 [==============================] - 0s 4ms/step - loss: 0.5519 - binary_accuracy: 0.7068 - val_loss: 0.5789 - val_binary_accuracy: 0.6915\n",
            "Epoch 196/200\n",
            "44/44 [==============================] - 0s 3ms/step - loss: 0.5545 - binary_accuracy: 0.7053 - val_loss: 0.5780 - val_binary_accuracy: 0.6907\n",
            "Epoch 197/200\n",
            "44/44 [==============================] - 0s 4ms/step - loss: 0.5509 - binary_accuracy: 0.7063 - val_loss: 0.5753 - val_binary_accuracy: 0.6957\n",
            "Epoch 198/200\n",
            "44/44 [==============================] - 0s 4ms/step - loss: 0.5503 - binary_accuracy: 0.7125 - val_loss: 0.5805 - val_binary_accuracy: 0.6884\n",
            "Epoch 199/200\n",
            "44/44 [==============================] - 0s 4ms/step - loss: 0.5518 - binary_accuracy: 0.7086 - val_loss: 0.5787 - val_binary_accuracy: 0.6982\n",
            "Epoch 200/200\n",
            "44/44 [==============================] - 0s 4ms/step - loss: 0.5473 - binary_accuracy: 0.7134 - val_loss: 0.5757 - val_binary_accuracy: 0.7011\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<tensorflow.python.keras.callbacks.History at 0x7fe3b91da7f0>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 68
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "jF66RsXbVkFV",
        "outputId": "8ce92d83-9dc5-4b91-8852-edacb2da3abd",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 282
        }
      },
      "source": [
        "pd.DataFrame(model_new.history.history)[['loss','val_loss']].plot() #The graph shows that, by adding in Dropout layers, we have reduced the overfitting issue compared with the old model"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7fe3b91ad400>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 69
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "AKfzO7NxVqlF",
        "outputId": "f0bf583c-cd2b-4f16-e822-e4a54f74eeba",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "source": [
        "predictions_new = (model_new.predict_proba(X_test) >= 0.2).astype('int')\n",
        "\n",
        "print(\n",
        "        confusion_matrix(y_test,predictions_new), \n",
        "        '\\n', \n",
        "        classification_report(y_test,predictions_new)\n",
        ")"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "WARNING:tensorflow:From <ipython-input-70-0e49b3c00232>:1: Sequential.predict_proba (from tensorflow.python.keras.engine.sequential) is deprecated and will be removed after 2021-01-01.\n",
            "Instructions for updating:\n",
            "Please use `model.predict()` instead.\n",
            "[[ 522 1915]\n",
            " [  53 2337]] \n",
            "               precision    recall  f1-score   support\n",
            "\n",
            "           0       0.91      0.21      0.35      2437\n",
            "           1       0.55      0.98      0.70      2390\n",
            "\n",
            "    accuracy                           0.59      4827\n",
            "   macro avg       0.73      0.60      0.53      4827\n",
            "weighted avg       0.73      0.59      0.52      4827\n",
            "\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "i_B5YLjJV1AV"
      },
      "source": [
        "dump(scaler, open('scaler.pkl', 'wb'))\n",
        "model_new.save('my_model_lending_club.h5')"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "37Nhj_JRV4PV"
      },
      "source": [
        "later_scaler = load(open('scaler.pkl', 'rb'))\n",
        "later_model = load_model('my_model_lending_club.h5')\n",
        "\n"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "S-TAxP3OWPcl",
        "outputId": "33705fd8-11ee-4178-917f-69994b81547f",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "source": [
        "X_OOT = to_pred.drop('not.fully.paid', axis=1).values\n",
        "to_pred.drop('not.fully.paid', axis=1).values\n",
        "\n",
        "print(X_OOT.shape)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "(0, 15)\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "mmWaKZ_gZ0UD"
      },
      "source": [
        ""
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "QOMVEPGAVwl1"
      },
      "source": [
        "\n"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "3M-9kCkGaRDW"
      },
      "source": [
        ""
      ],
      "execution_count": null,
      "outputs": []
    }
  ]
}