Deep_Churning.ipynb

{
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  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "name": "Deep Churning.ipynb",
      "provenance": [],
      "authorship_tag": "ABX9TyNLAcJV5q2jPb/3iEFC1JXV",
      "include_colab_link": true
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    },
    "language_info": {
      "name": "python"
    }
  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/DataMinati/Super-Notebooks-7K/blob/main/Deep_Churning.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "3TUPCxIIPbXl"
      },
      "source": [
        "import pandas as pd\n",
        "import numpy as np\n",
        "import matplotlib.pyplot as plt"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 356
        },
        "id": "OXIPRGd0Pmaj",
        "outputId": "1abbac56-0331-4f3e-9193-bb86e2a59836"
      },
      "source": [
        "df= pd.read_csv(\"https://raw.githubusercontent.com/DataMinati/Datasets-/master/Churn%20Modeling.csv\")\n",
        "df.head(10)"
      ],
      "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>RowNumber</th>\n",
              "      <th>CustomerId</th>\n",
              "      <th>Surname</th>\n",
              "      <th>CreditScore</th>\n",
              "      <th>Geography</th>\n",
              "      <th>Gender</th>\n",
              "      <th>Age</th>\n",
              "      <th>Tenure</th>\n",
              "      <th>Balance</th>\n",
              "      <th>NumOfProducts</th>\n",
              "      <th>HasCrCard</th>\n",
              "      <th>IsActiveMember</th>\n",
              "      <th>EstimatedSalary</th>\n",
              "      <th>Exited</th>\n",
              "    </tr>\n",
              "  </thead>\n",
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              "      <td>608</td>\n",
              "      <td>Spain</td>\n",
              "      <td>Female</td>\n",
              "      <td>41</td>\n",
              "      <td>1</td>\n",
              "      <td>83807.86</td>\n",
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              "      <td>Onio</td>\n",
              "      <td>502</td>\n",
              "      <td>France</td>\n",
              "      <td>Female</td>\n",
              "      <td>42</td>\n",
              "      <td>8</td>\n",
              "      <td>159660.80</td>\n",
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              "      <td>113931.57</td>\n",
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              "      <td>Boni</td>\n",
              "      <td>699</td>\n",
              "      <td>France</td>\n",
              "      <td>Female</td>\n",
              "      <td>39</td>\n",
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              "      <td>0.00</td>\n",
              "      <td>2</td>\n",
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              "      <th>4</th>\n",
              "      <td>5</td>\n",
              "      <td>15737888</td>\n",
              "      <td>Mitchell</td>\n",
              "      <td>850</td>\n",
              "      <td>Spain</td>\n",
              "      <td>Female</td>\n",
              "      <td>43</td>\n",
              "      <td>2</td>\n",
              "      <td>125510.82</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>79084.10</td>\n",
              "      <td>0</td>\n",
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              "    <tr>\n",
              "      <th>5</th>\n",
              "      <td>6</td>\n",
              "      <td>15574012</td>\n",
              "      <td>Chu</td>\n",
              "      <td>645</td>\n",
              "      <td>Spain</td>\n",
              "      <td>Male</td>\n",
              "      <td>44</td>\n",
              "      <td>8</td>\n",
              "      <td>113755.78</td>\n",
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              "      <td>Bartlett</td>\n",
              "      <td>822</td>\n",
              "      <td>France</td>\n",
              "      <td>Male</td>\n",
              "      <td>50</td>\n",
              "      <td>7</td>\n",
              "      <td>0.00</td>\n",
              "      <td>2</td>\n",
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              "      <td>1</td>\n",
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              "      <td>8</td>\n",
              "      <td>15656148</td>\n",
              "      <td>Obinna</td>\n",
              "      <td>376</td>\n",
              "      <td>Germany</td>\n",
              "      <td>Female</td>\n",
              "      <td>29</td>\n",
              "      <td>4</td>\n",
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              "      <td>Male</td>\n",
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              "      <td>Male</td>\n",
              "      <td>27</td>\n",
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            "text/plain": [
              "   RowNumber  CustomerId   Surname  ...  IsActiveMember EstimatedSalary Exited\n",
              "0          1    15634602  Hargrave  ...               1       101348.88      1\n",
              "1          2    15647311      Hill  ...               1       112542.58      0\n",
              "2          3    15619304      Onio  ...               0       113931.57      1\n",
              "3          4    15701354      Boni  ...               0        93826.63      0\n",
              "4          5    15737888  Mitchell  ...               1        79084.10      0\n",
              "5          6    15574012       Chu  ...               0       149756.71      1\n",
              "6          7    15592531  Bartlett  ...               1        10062.80      0\n",
              "7          8    15656148    Obinna  ...               0       119346.88      1\n",
              "8          9    15792365        He  ...               1        74940.50      0\n",
              "9         10    15592389        H?  ...               1        71725.73      0\n",
              "\n",
              "[10 rows x 14 columns]"
            ]
          },
          "metadata": {},
          "execution_count": 4
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "CktpCjsfPnzn"
      },
      "source": [
        "x = df.iloc[:,3:-1]\n",
        "y=df[\"Exited\"]"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "UtMohX-wPskq"
      },
      "source": [
        "x['Gender']=pd.get_dummies(df.Gender, drop_first=True)\n",
        "geo=pd.get_dummies(df.Geography, drop_first=False)\n",
        "x=pd.concat([x,geo],axis=1)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "IfIfO4_RPvRN",
        "outputId": "61c1b61f-a393-497c-be1e-033eb2a80d4d"
      },
      "source": [
        "x.drop(\"Geography\",axis=1,inplace=True)\n",
        "x.info()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "<class 'pandas.core.frame.DataFrame'>\n",
            "RangeIndex: 10000 entries, 0 to 9999\n",
            "Data columns (total 12 columns):\n",
            " #   Column           Non-Null Count  Dtype  \n",
            "---  ------           --------------  -----  \n",
            " 0   CreditScore      10000 non-null  int64  \n",
            " 1   Gender           10000 non-null  uint8  \n",
            " 2   Age              10000 non-null  int64  \n",
            " 3   Tenure           10000 non-null  int64  \n",
            " 4   Balance          10000 non-null  float64\n",
            " 5   NumOfProducts    10000 non-null  int64  \n",
            " 6   HasCrCard        10000 non-null  int64  \n",
            " 7   IsActiveMember   10000 non-null  int64  \n",
            " 8   EstimatedSalary  10000 non-null  float64\n",
            " 9   France           10000 non-null  uint8  \n",
            " 10  Germany          10000 non-null  uint8  \n",
            " 11  Spain            10000 non-null  uint8  \n",
            "dtypes: float64(2), int64(6), uint8(4)\n",
            "memory usage: 664.2 KB\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "zZn25bMTPxBH"
      },
      "source": [
        "from sklearn.preprocessing import StandardScaler\n",
        "scaler = StandardScaler()\n",
        "x=scaler.fit_transform(x)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "OGzreGofPzBh"
      },
      "source": [
        "from sklearn.model_selection import train_test_split\n",
        "xtrain, xtest, ytrain, ytest = train_test_split(x, y, test_size=0.33, random_state=42)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "v3r5HrPUP4mt"
      },
      "source": [
        "import keras\n",
        "from keras.models import Sequential\n",
        "from keras.layers import Dense\n",
        "from keras.layers import Dropout"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "vS_PD4UEQR74"
      },
      "source": [
        "#initialising the ANN\n",
        "ann =Sequential()"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "H3B29DmzQUb9",
        "outputId": "5f0d9a69-dd16-40ad-e05d-314233e1195c"
      },
      "source": [
        "#adding the input layer and first hidden layer\n",
        "ann.add(Dense(units=6,kernel_initializer=\"he_uniform\",activation=\"relu\",input_dim=12))\n",
        "#adding the second hidden layer\n",
        "ann.add(Dense(units=6,kernel_initializer=\"he_uniform\",activation=\"relu\"))\n",
        "#adding the last hidden layer\n",
        "ann.add(Dense(units=1,kernel_initializer=\"glorot_uniform\",activation=\"sigmoid\"))\n",
        "ann.compile(optimizer=\"adam\",loss=\"binary_crossentropy\",metrics=[\"accuracy\"])\n",
        "fitted_model=ann.fit(xtrain,ytrain,validation_split=0.4,batch_size=10,epochs=100)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Epoch 1/100\n",
            "402/402 [==============================] - 2s 2ms/step - loss: 0.9610 - accuracy: 0.4104 - val_loss: 0.6525 - val_accuracy: 0.6821\n",
            "Epoch 2/100\n",
            "402/402 [==============================] - 1s 2ms/step - loss: 0.5647 - accuracy: 0.7562 - val_loss: 0.5108 - val_accuracy: 0.7907\n",
            "Epoch 3/100\n",
            "402/402 [==============================] - 1s 2ms/step - loss: 0.4928 - accuracy: 0.7920 - val_loss: 0.4765 - val_accuracy: 0.7918\n",
            "Epoch 4/100\n",
            "402/402 [==============================] - 1s 2ms/step - loss: 0.4622 - accuracy: 0.7948 - val_loss: 0.4541 - val_accuracy: 0.7925\n",
            "Epoch 5/100\n",
            "402/402 [==============================] - 1s 2ms/step - loss: 0.4381 - accuracy: 0.8005 - val_loss: 0.4329 - val_accuracy: 0.7981\n",
            "Epoch 6/100\n",
            "402/402 [==============================] - 1s 2ms/step - loss: 0.4150 - accuracy: 0.8109 - val_loss: 0.4128 - val_accuracy: 0.8086\n",
            "Epoch 7/100\n",
            "402/402 [==============================] - 1s 2ms/step - loss: 0.3959 - accuracy: 0.8199 - val_loss: 0.3998 - val_accuracy: 0.8224\n",
            "Epoch 8/100\n",
            "402/402 [==============================] - 1s 2ms/step - loss: 0.3827 - accuracy: 0.8336 - val_loss: 0.3917 - val_accuracy: 0.8340\n",
            "Epoch 9/100\n",
            "402/402 [==============================] - 1s 2ms/step - loss: 0.3733 - accuracy: 0.8423 - val_loss: 0.3849 - val_accuracy: 0.8366\n",
            "Epoch 10/100\n",
            "402/402 [==============================] - 1s 2ms/step - loss: 0.3675 - accuracy: 0.8453 - val_loss: 0.3815 - val_accuracy: 0.8377\n",
            "Epoch 11/100\n",
            "402/402 [==============================] - 1s 2ms/step - loss: 0.3626 - accuracy: 0.8517 - val_loss: 0.3794 - val_accuracy: 0.8354\n",
            "Epoch 12/100\n",
            "402/402 [==============================] - 1s 2ms/step - loss: 0.3598 - accuracy: 0.8483 - val_loss: 0.3766 - val_accuracy: 0.8392\n",
            "Epoch 13/100\n",
            "402/402 [==============================] - 1s 2ms/step - loss: 0.3573 - accuracy: 0.8495 - val_loss: 0.3742 - val_accuracy: 0.8407\n",
            "Epoch 14/100\n",
            "402/402 [==============================] - 1s 2ms/step - loss: 0.3552 - accuracy: 0.8507 - val_loss: 0.3732 - val_accuracy: 0.8403\n",
            "Epoch 15/100\n",
            "402/402 [==============================] - 1s 2ms/step - loss: 0.3539 - accuracy: 0.8527 - val_loss: 0.3723 - val_accuracy: 0.8425\n",
            "Epoch 16/100\n",
            "402/402 [==============================] - 1s 2ms/step - loss: 0.3522 - accuracy: 0.8522 - val_loss: 0.3722 - val_accuracy: 0.8422\n",
            "Epoch 17/100\n",
            "402/402 [==============================] - 1s 2ms/step - loss: 0.3511 - accuracy: 0.8525 - val_loss: 0.3707 - val_accuracy: 0.8407\n",
            "Epoch 18/100\n",
            "402/402 [==============================] - 1s 2ms/step - loss: 0.3503 - accuracy: 0.8527 - val_loss: 0.3702 - val_accuracy: 0.8410\n",
            "Epoch 19/100\n",
            "402/402 [==============================] - 1s 2ms/step - loss: 0.3491 - accuracy: 0.8552 - val_loss: 0.3695 - val_accuracy: 0.8422\n",
            "Epoch 20/100\n",
            "402/402 [==============================] - 1s 2ms/step - loss: 0.3484 - accuracy: 0.8535 - val_loss: 0.3691 - val_accuracy: 0.8440\n",
            "Epoch 21/100\n",
            "402/402 [==============================] - 1s 2ms/step - loss: 0.3476 - accuracy: 0.8552 - val_loss: 0.3686 - val_accuracy: 0.8437\n",
            "Epoch 22/100\n",
            "402/402 [==============================] - 1s 2ms/step - loss: 0.3473 - accuracy: 0.8552 - val_loss: 0.3676 - val_accuracy: 0.8433\n",
            "Epoch 23/100\n",
            "402/402 [==============================] - 1s 2ms/step - loss: 0.3463 - accuracy: 0.8555 - val_loss: 0.3678 - val_accuracy: 0.8425\n",
            "Epoch 24/100\n",
            "402/402 [==============================] - 1s 2ms/step - loss: 0.3461 - accuracy: 0.8562 - val_loss: 0.3674 - val_accuracy: 0.8422\n",
            "Epoch 25/100\n",
            "402/402 [==============================] - 1s 2ms/step - loss: 0.3454 - accuracy: 0.8552 - val_loss: 0.3679 - val_accuracy: 0.8414\n",
            "Epoch 26/100\n",
            "402/402 [==============================] - 1s 2ms/step - loss: 0.3447 - accuracy: 0.8580 - val_loss: 0.3665 - val_accuracy: 0.8455\n",
            "Epoch 27/100\n",
            "402/402 [==============================] - 1s 2ms/step - loss: 0.3438 - accuracy: 0.8580 - val_loss: 0.3665 - val_accuracy: 0.8425\n",
            "Epoch 28/100\n",
            "402/402 [==============================] - 1s 2ms/step - loss: 0.3439 - accuracy: 0.8565 - val_loss: 0.3659 - val_accuracy: 0.8440\n",
            "Epoch 29/100\n",
            "402/402 [==============================] - 1s 2ms/step - loss: 0.3434 - accuracy: 0.8580 - val_loss: 0.3663 - val_accuracy: 0.8429\n",
            "Epoch 30/100\n",
            "402/402 [==============================] - 1s 2ms/step - loss: 0.3430 - accuracy: 0.8595 - val_loss: 0.3654 - val_accuracy: 0.8455\n",
            "Epoch 31/100\n",
            "402/402 [==============================] - 1s 2ms/step - loss: 0.3426 - accuracy: 0.8590 - val_loss: 0.3659 - val_accuracy: 0.8440\n",
            "Epoch 32/100\n",
            "402/402 [==============================] - 1s 2ms/step - loss: 0.3422 - accuracy: 0.8627 - val_loss: 0.3658 - val_accuracy: 0.8429\n",
            "Epoch 33/100\n",
            "402/402 [==============================] - 1s 2ms/step - loss: 0.3417 - accuracy: 0.8609 - val_loss: 0.3648 - val_accuracy: 0.8437\n",
            "Epoch 34/100\n",
            "402/402 [==============================] - 1s 2ms/step - loss: 0.3412 - accuracy: 0.8614 - val_loss: 0.3650 - val_accuracy: 0.8455\n",
            "Epoch 35/100\n",
            "402/402 [==============================] - 1s 2ms/step - loss: 0.3410 - accuracy: 0.8634 - val_loss: 0.3648 - val_accuracy: 0.8444\n",
            "Epoch 36/100\n",
            "402/402 [==============================] - 1s 2ms/step - loss: 0.3409 - accuracy: 0.8590 - val_loss: 0.3641 - val_accuracy: 0.8451\n",
            "Epoch 37/100\n",
            "402/402 [==============================] - 1s 2ms/step - loss: 0.3407 - accuracy: 0.8595 - val_loss: 0.3638 - val_accuracy: 0.8463\n",
            "Epoch 38/100\n",
            "402/402 [==============================] - 1s 2ms/step - loss: 0.3400 - accuracy: 0.8614 - val_loss: 0.3649 - val_accuracy: 0.8437\n",
            "Epoch 39/100\n",
            "402/402 [==============================] - 1s 2ms/step - loss: 0.3396 - accuracy: 0.8614 - val_loss: 0.3647 - val_accuracy: 0.8437\n",
            "Epoch 40/100\n",
            "402/402 [==============================] - 1s 2ms/step - loss: 0.3395 - accuracy: 0.8592 - val_loss: 0.3639 - val_accuracy: 0.8463\n",
            "Epoch 41/100\n",
            "402/402 [==============================] - 1s 2ms/step - loss: 0.3395 - accuracy: 0.8629 - val_loss: 0.3629 - val_accuracy: 0.8429\n",
            "Epoch 42/100\n",
            "402/402 [==============================] - 1s 2ms/step - loss: 0.3388 - accuracy: 0.8612 - val_loss: 0.3642 - val_accuracy: 0.8437\n",
            "Epoch 43/100\n",
            "402/402 [==============================] - 1s 2ms/step - loss: 0.3390 - accuracy: 0.8617 - val_loss: 0.3639 - val_accuracy: 0.8407\n",
            "Epoch 44/100\n",
            "402/402 [==============================] - 1s 2ms/step - loss: 0.3389 - accuracy: 0.8617 - val_loss: 0.3635 - val_accuracy: 0.8429\n",
            "Epoch 45/100\n",
            "402/402 [==============================] - 1s 2ms/step - loss: 0.3380 - accuracy: 0.8607 - val_loss: 0.3629 - val_accuracy: 0.8437\n",
            "Epoch 46/100\n",
            "402/402 [==============================] - 1s 2ms/step - loss: 0.3376 - accuracy: 0.8632 - val_loss: 0.3637 - val_accuracy: 0.8425\n",
            "Epoch 47/100\n",
            "402/402 [==============================] - 1s 2ms/step - loss: 0.3375 - accuracy: 0.8619 - val_loss: 0.3633 - val_accuracy: 0.8451\n",
            "Epoch 48/100\n",
            "402/402 [==============================] - 1s 2ms/step - loss: 0.3372 - accuracy: 0.8607 - val_loss: 0.3633 - val_accuracy: 0.8451\n",
            "Epoch 49/100\n",
            "402/402 [==============================] - 1s 2ms/step - loss: 0.3373 - accuracy: 0.8604 - val_loss: 0.3619 - val_accuracy: 0.8451\n",
            "Epoch 50/100\n",
            "402/402 [==============================] - 1s 2ms/step - loss: 0.3366 - accuracy: 0.8629 - val_loss: 0.3628 - val_accuracy: 0.8455\n",
            "Epoch 51/100\n",
            "402/402 [==============================] - 1s 2ms/step - loss: 0.3370 - accuracy: 0.8624 - val_loss: 0.3629 - val_accuracy: 0.8433\n",
            "Epoch 52/100\n",
            "402/402 [==============================] - 1s 2ms/step - loss: 0.3362 - accuracy: 0.8634 - val_loss: 0.3632 - val_accuracy: 0.8399\n",
            "Epoch 53/100\n",
            "402/402 [==============================] - 1s 2ms/step - loss: 0.3361 - accuracy: 0.8642 - val_loss: 0.3635 - val_accuracy: 0.8448\n",
            "Epoch 54/100\n",
            "402/402 [==============================] - 1s 2ms/step - loss: 0.3356 - accuracy: 0.8619 - val_loss: 0.3613 - val_accuracy: 0.8437\n",
            "Epoch 55/100\n",
            "402/402 [==============================] - 1s 2ms/step - loss: 0.3353 - accuracy: 0.8612 - val_loss: 0.3615 - val_accuracy: 0.8459\n",
            "Epoch 56/100\n",
            "402/402 [==============================] - 1s 2ms/step - loss: 0.3343 - accuracy: 0.8637 - val_loss: 0.3620 - val_accuracy: 0.8425\n",
            "Epoch 57/100\n",
            "402/402 [==============================] - 1s 2ms/step - loss: 0.3346 - accuracy: 0.8634 - val_loss: 0.3627 - val_accuracy: 0.8422\n",
            "Epoch 58/100\n",
            "402/402 [==============================] - 1s 2ms/step - loss: 0.3344 - accuracy: 0.8647 - val_loss: 0.3626 - val_accuracy: 0.8448\n",
            "Epoch 59/100\n",
            "402/402 [==============================] - 1s 2ms/step - loss: 0.3342 - accuracy: 0.8624 - val_loss: 0.3615 - val_accuracy: 0.8448\n",
            "Epoch 60/100\n",
            "402/402 [==============================] - 1s 2ms/step - loss: 0.3338 - accuracy: 0.8652 - val_loss: 0.3614 - val_accuracy: 0.8459\n",
            "Epoch 61/100\n",
            "402/402 [==============================] - 1s 2ms/step - loss: 0.3336 - accuracy: 0.8629 - val_loss: 0.3620 - val_accuracy: 0.8459\n",
            "Epoch 62/100\n",
            "402/402 [==============================] - 1s 2ms/step - loss: 0.3333 - accuracy: 0.8607 - val_loss: 0.3608 - val_accuracy: 0.8437\n",
            "Epoch 63/100\n",
            "402/402 [==============================] - 1s 2ms/step - loss: 0.3323 - accuracy: 0.8614 - val_loss: 0.3616 - val_accuracy: 0.8455\n",
            "Epoch 64/100\n",
            "402/402 [==============================] - 1s 2ms/step - loss: 0.3326 - accuracy: 0.8619 - val_loss: 0.3612 - val_accuracy: 0.8437\n",
            "Epoch 65/100\n",
            "402/402 [==============================] - 1s 2ms/step - loss: 0.3325 - accuracy: 0.8624 - val_loss: 0.3619 - val_accuracy: 0.8451\n",
            "Epoch 66/100\n",
            "402/402 [==============================] - 1s 2ms/step - loss: 0.3321 - accuracy: 0.8639 - val_loss: 0.3613 - val_accuracy: 0.8437\n",
            "Epoch 67/100\n",
            "402/402 [==============================] - 1s 2ms/step - loss: 0.3320 - accuracy: 0.8629 - val_loss: 0.3603 - val_accuracy: 0.8448\n",
            "Epoch 68/100\n",
            "402/402 [==============================] - 1s 2ms/step - loss: 0.3316 - accuracy: 0.8639 - val_loss: 0.3604 - val_accuracy: 0.8440\n",
            "Epoch 69/100\n",
            "402/402 [==============================] - 1s 2ms/step - loss: 0.3310 - accuracy: 0.8627 - val_loss: 0.3608 - val_accuracy: 0.8478\n",
            "Epoch 70/100\n",
            "402/402 [==============================] - 1s 2ms/step - loss: 0.3307 - accuracy: 0.8600 - val_loss: 0.3602 - val_accuracy: 0.8485\n",
            "Epoch 71/100\n",
            "402/402 [==============================] - 1s 2ms/step - loss: 0.3314 - accuracy: 0.8634 - val_loss: 0.3602 - val_accuracy: 0.8489\n",
            "Epoch 72/100\n",
            "402/402 [==============================] - 1s 2ms/step - loss: 0.3307 - accuracy: 0.8642 - val_loss: 0.3609 - val_accuracy: 0.8433\n",
            "Epoch 73/100\n",
            "402/402 [==============================] - 1s 2ms/step - loss: 0.3304 - accuracy: 0.8595 - val_loss: 0.3602 - val_accuracy: 0.8478\n",
            "Epoch 74/100\n",
            "402/402 [==============================] - 1s 2ms/step - loss: 0.3299 - accuracy: 0.8647 - val_loss: 0.3598 - val_accuracy: 0.8444\n",
            "Epoch 75/100\n",
            "402/402 [==============================] - 1s 2ms/step - loss: 0.3304 - accuracy: 0.8634 - val_loss: 0.3616 - val_accuracy: 0.8466\n",
            "Epoch 76/100\n",
            "402/402 [==============================] - 1s 2ms/step - loss: 0.3292 - accuracy: 0.8637 - val_loss: 0.3644 - val_accuracy: 0.8444\n",
            "Epoch 77/100\n",
            "402/402 [==============================] - 1s 2ms/step - loss: 0.3303 - accuracy: 0.8619 - val_loss: 0.3603 - val_accuracy: 0.8463\n",
            "Epoch 78/100\n",
            "402/402 [==============================] - 1s 2ms/step - loss: 0.3297 - accuracy: 0.8649 - val_loss: 0.3599 - val_accuracy: 0.8489\n",
            "Epoch 79/100\n",
            "402/402 [==============================] - 1s 2ms/step - loss: 0.3297 - accuracy: 0.8624 - val_loss: 0.3597 - val_accuracy: 0.8463\n",
            "Epoch 80/100\n",
            "402/402 [==============================] - 1s 2ms/step - loss: 0.3289 - accuracy: 0.8612 - val_loss: 0.3610 - val_accuracy: 0.8463\n",
            "Epoch 81/100\n",
            "402/402 [==============================] - 1s 2ms/step - loss: 0.3281 - accuracy: 0.8639 - val_loss: 0.3624 - val_accuracy: 0.8440\n",
            "Epoch 82/100\n",
            "402/402 [==============================] - 1s 2ms/step - loss: 0.3290 - accuracy: 0.8627 - val_loss: 0.3612 - val_accuracy: 0.8459\n",
            "Epoch 83/100\n",
            "402/402 [==============================] - 1s 2ms/step - loss: 0.3288 - accuracy: 0.8624 - val_loss: 0.3600 - val_accuracy: 0.8466\n",
            "Epoch 84/100\n",
            "402/402 [==============================] - 1s 2ms/step - loss: 0.3282 - accuracy: 0.8652 - val_loss: 0.3610 - val_accuracy: 0.8455\n",
            "Epoch 85/100\n",
            "402/402 [==============================] - 1s 2ms/step - loss: 0.3280 - accuracy: 0.8672 - val_loss: 0.3603 - val_accuracy: 0.8470\n",
            "Epoch 86/100\n",
            "402/402 [==============================] - 1s 2ms/step - loss: 0.3280 - accuracy: 0.8637 - val_loss: 0.3615 - val_accuracy: 0.8466\n",
            "Epoch 87/100\n",
            "402/402 [==============================] - 1s 2ms/step - loss: 0.3289 - accuracy: 0.8619 - val_loss: 0.3608 - val_accuracy: 0.8485\n",
            "Epoch 88/100\n",
            "402/402 [==============================] - 1s 2ms/step - loss: 0.3285 - accuracy: 0.8629 - val_loss: 0.3605 - val_accuracy: 0.8474\n",
            "Epoch 89/100\n",
            "402/402 [==============================] - 1s 2ms/step - loss: 0.3280 - accuracy: 0.8637 - val_loss: 0.3612 - val_accuracy: 0.8466\n",
            "Epoch 90/100\n",
            "402/402 [==============================] - 1s 2ms/step - loss: 0.3279 - accuracy: 0.8659 - val_loss: 0.3612 - val_accuracy: 0.8459\n",
            "Epoch 91/100\n",
            "402/402 [==============================] - 1s 2ms/step - loss: 0.3273 - accuracy: 0.8634 - val_loss: 0.3624 - val_accuracy: 0.8500\n",
            "Epoch 92/100\n",
            "402/402 [==============================] - 1s 2ms/step - loss: 0.3278 - accuracy: 0.8637 - val_loss: 0.3603 - val_accuracy: 0.8451\n",
            "Epoch 93/100\n",
            "402/402 [==============================] - 1s 2ms/step - loss: 0.3279 - accuracy: 0.8654 - val_loss: 0.3602 - val_accuracy: 0.8481\n",
            "Epoch 94/100\n",
            "402/402 [==============================] - 1s 2ms/step - loss: 0.3268 - accuracy: 0.8639 - val_loss: 0.3611 - val_accuracy: 0.8489\n",
            "Epoch 95/100\n",
            "402/402 [==============================] - 1s 2ms/step - loss: 0.3275 - accuracy: 0.8642 - val_loss: 0.3618 - val_accuracy: 0.8478\n",
            "Epoch 96/100\n",
            "402/402 [==============================] - 1s 2ms/step - loss: 0.3270 - accuracy: 0.8649 - val_loss: 0.3603 - val_accuracy: 0.8478\n",
            "Epoch 97/100\n",
            "402/402 [==============================] - 1s 2ms/step - loss: 0.3271 - accuracy: 0.8657 - val_loss: 0.3607 - val_accuracy: 0.8474\n",
            "Epoch 98/100\n",
            "402/402 [==============================] - 1s 2ms/step - loss: 0.3268 - accuracy: 0.8639 - val_loss: 0.3611 - val_accuracy: 0.8478\n",
            "Epoch 99/100\n",
            "402/402 [==============================] - 1s 2ms/step - loss: 0.3266 - accuracy: 0.8627 - val_loss: 0.3610 - val_accuracy: 0.8478\n",
            "Epoch 100/100\n",
            "402/402 [==============================] - 1s 2ms/step - loss: 0.3261 - accuracy: 0.8649 - val_loss: 0.3611 - val_accuracy: 0.8459\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "x1O4VlaPQYZr",
        "outputId": "5e75520a-d918-479f-b06e-c75585d9ef29"
      },
      "source": [
        "fitted_model.history.keys()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "dict_keys(['loss', 'accuracy', 'val_loss', 'val_accuracy'])"
            ]
          },
          "metadata": {},
          "execution_count": 13
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 401
        },
        "id": "dDNMcbrhQxGT",
        "outputId": "7f67b190-60c8-4a06-f484-52013d990619"
      },
      "source": [
        "plt.figure(figsize=(12,5))\n",
        "plt.plot(fitted_model.history[\"accuracy\"])\n",
        "plt.plot(fitted_model.history[\"val_accuracy\"])\n",
        "plt.xlabel(\"Epoch\",color='green',size=15)\n",
        "plt.xticks(color='green',size=15)\n",
        "plt.ylabel(\"Accuracy\",color='green',size=15)\n",
        "plt.yticks(color='green',size=15)\n",
        "plt.title(\"Training graphs\\n\",color='green',size=18)\n",
        "plt.legend(['train','val_test'])"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.legend.Legend at 0x7f9dfe9cdfd0>"
            ]
          },
          "metadata": {},
          "execution_count": 33
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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B4ZH1N5XqUIDykgAd/fFDbl8a8HHGcfM4d1kda5bVcfbS2jHV1fZojIc3tfPQxnYe39bJcCo9Mq+iJPOaBqkOBVm/r5ehRIozmufx3vOXcs3pTYSCrgoZS6T42bN7ufvRV9jZOcjS+nKuOa2J3qHEyIZRWzQ2aYCbSF1FyUjY2tzWx6bWKABVpQHOW17PhSvqGUqk3Htmdw+d3rLLS/ycsKDSe0+UZQW1EEOJFM/s7OKZHd28sKdn5Pk2VJVO+P4O+AwlAR9Bv4+gt8EX9PtIpe3I91Qo6OOsJbWcu6yOc1vqWHVczZSFgmQqzbpd3Ty0sZ2HN7WPea8H/YYldeWsaKikrqKEjv5hbyPLbWANDqcmXW6J38dJjVWsWlzNqYtqOG1xDSc1VhEK+hlOptncFnXfJzl8xuZXlrB8fuUh3+GhoI89XUPs9jYQ93rXXQPDbmPYW0eZDeSSgM+9h0JjP+/VY/523xmVpa6Y0R9PsuPgANsP9vPKwX62dwzwysEB9nUPjtlIHs8YWDa/YmRD/9TF1Vy4Yv6kt88nhW6FbpFpsdbSPZhgX/cQxrgfsuwQFvAZXtzb68Lohlb2dA3h9xkuWF5PU02IHR3uS7N7XKCYTMBnWNFQ6VWHqjhpYRXVZUF++cI+/vuF/fTHkxy/oJJ3rGnmrWcdR2150As1qZGg2x9PsqNjgC1tUba097OlLUp7NH7Yx/YZWNFQOVIVXbWoGmMMD2xwGxGtvTGCfsMlJzSwpqWO1t4hVwU62M/+3tirXdVjVJUG6B9OjgT14xdUcmbzPM5cUsv8yhLao16AyQoy+7OqWBl+n6GuooQSv88LDYaAz0cw4COVTvNye/9IIF1UE+LMJbWcflwNpQEfA1nrdCiRZCCeorzEn7VxMFp9rS0vobosMOEu4HgyxZa2PjbsywTaXrYf6GdgiuCwoKqUszJVyyW1nLa4hpKAj0TKVeAyFbRE2lJbHpx0Ywlc94ZtB/rdY+/rxecznNtSxzle0J5MNJZwFcN9rmq2YX+U7Qf7R16TqlCAeCI9JqBOxu8zlAf9I5+bspKA91lyl/mVpSypc7vrl9SV01xbPtJdIp22RGOZPQvDdPYPE0ukRl6H+ZWl7jX2dvGn05aOgTht3vujLRpjV+cg63Z1s2FfL6m0xe8zI3sBNuzr5cW9vQAsqSvnipULef0pCzi5sZqqUOCQriHRWIJfPLuXHz65i+0HB6gtD3LdmmYqSgL84PGddA4Mc8ZxNdx06QrecGoj/gkq7LGE23gL+AyBcUE20y3Cha0B16XhwAC7uwZZNr+Ci46v58Lj53P64ppDNiittezpGuL5Pd08t6ubHZ2DtPUO0dobO2SD2+8zrFpcM7LRtaallnnlJaTSls7++Miek9beGAf74iNVYFfBddX5tLWc0lTNucvqWLWoZlrdLMbrGRxmu/d98krHANsPuOuewcSYvRyZ6/qKEmrKSsYE1uzQmqvugWFiydTI+g9m7UUq5J686RiIJ2mLZn3/9Q5RVhLgtMU1rFxUnZfuOkdCoVuhW+aY3qEEz+7qYl9WdTJbScDHcbXuh7ypJjRh1TOWSLG3e8jt2u4eZHent3vbm9Yfn7yq4PcZUmlLwGe4+IT5XLWqkStWNlI3rl9sdn/AnsFhFzqCXugodQGkpixIS33FpD9cA/Ekv/lzK/c8s5vnd/dgDJNWjzPP/YQFLsCf3FjFiQurqK84NGRZLO3ReFa46h0T0ksCPl5zQgNXn9bI609ZSE3ZodWrweGkq8L0DFHi942Eq0zAKgv68U/wA2ax9MWSHMzqItDphat55UHOXFLL6uPm5dRf1VpL71BibBjvHeJgf5zhpB0JrAkvsFrg5MaqkTDfWBM67GPMpGQqTTSWHFMhHk6mWbmoetr9TQtlIJ5kU6vbeHjl4ADlpf4Jq3eVpf6R9/hk3XRmq/3P7e7m6R1dPL2ji/X7ejmpsYrLT1nIFSsXcsKCypzbaa3lie2d/PsTu3hoUzuptOV1Jy/gxtcs57xldUfF883WH0+ObIj4DJzRPI+KoyScydyk0K3QLbMsnXZ9F5Mpy3AyTV8sOaY/XiZ8hIL+MZWN+ZWuonGwP84zO7p5ekcnT+/sZnNbdMrgmc3vMyyaF2JJXTl1FaW09Q6xp2uItujYKm1pwDe26uYdLGMMWd1CRnePH7+gkitOWVjQA5m2tPVx//pWrLXjqobu7+a6clrqy494hIADfTFe2hdlKJHikhPmU3WU9CcXORq1R2PEEimW1lfMdlNEjhoK3QrdMolU2rL1QN9oX7fdPQwOp1wftvmuH1umP1tVKMBOr39ZZhfo9oP9tPbGSE/0WbK4g4RSdtK+sLkI+MxIX82yoJ+zls7j3JZ61iyr5fgFlfgmqCwNDafY0z3oHaA12gevcyBOU03ZyK7sJfWjfzdUlR51VSoREZFjyWShW/tXZEZlNuJmOrh19MdHDrLb3TVIwGdGuiaUl/gp83blpi1en7vRXemZo6mz+4MOe0dr7+gY4MU9PSN9TGvLg6xunkd1WZAdHQP8/Ll9k3ax8BnXD3J5QyXnLqubsP8iMGbEhUy/uYDPR1UoMMEBJUFiidSYUQkyoxTUlgdZ01LHqsU1OQ/D1VxXDiuObJ2LiIjIzFHolhmRSlt+9uwevvrgyxgDl5+ykMtXLuTCFfU5jbGZSls6vANYMge/7O4a5OV2F7Q7+kePeK8sDZBK25FRCKbD7zPuYBGfC8KL5pXx1rOOGzloq6W+fMwGg7WWg31xtnlV7f54kpb6ClY0VLDkCMcPPZzKUjeihYiISN4MD0IgBD6drKdQ1L1EXrUntnfy+V9vZGNrlLOWzGNBVYg/bT3I4HCKihI/rzmxgStWLmRJXfm4o45Hx9Zt74sf0v0iFPRx4kI3ioU7IK6aExsraah0XSDSaUssmRoZjmsokcLvY6SiHPAbr8rsGwna+RjbWETkqJcYgqEeiPVArNddEoOQiEEy+zIMLRdBy8Wz3eLJJYeh9UXYtw5Kq2HJ+VC33I0ZNxfE+6HjZRjqhsqFUNUIZXUzE44TQ7Dlfnjxp7DtYZjXDGs+BGe+G8rmTX3f5DBE97n3UOa9lLlOJ6Gy0bU10+by+UUb6NWnW6F7xu3qHOCL929i7UvtLJ5XxievOpk3nd6EMYZYIsUT2zt5aFM7D29s50Df2GHbyoL+kbF1G6tDNM0LjZyUoGmeG1u1tjyo/sWSO2tdkCitfvVf9OkUpIYhWDYzbRMppOQw7PgTbPwlbP89DHRA6vBDZ46x9CK49BOw7NKJw2xyGLb8Bp77IQx2wvGXw4lXwuKzwTfJHkBroXsn9O6BhpOhckFubRnohL1Pw+4nYc9TsO+5Q59PRQM0nwfN50Lz+VDbMnG7SyrcZbqS8bFBc7gPQrVeyFwA/gkOuI71uufbvQt6drllBMsgUOoqzJlLrAcObIKDW9yld/ehy/IFvTC70F2H5rmQfMh1zdi/g2WQTsPOR+HP98LGX7m2Vy2CldfC/udhz5MQLIcz3gHnfhgWnOweM52G9vXwyh9hxx9h1+NuQy1Xxg/lde56IoESCHjrI3u9pBJuXSWH3HXCu7aTDNFZXg8NJ7n3VOa6brlb/ixR6FbonhGxRIpnvUH9f/zUbgJ+w0cuW8EHL1k+cpKE8dJpy/p9vXQNDrPIO1FBdaiwZ/Sbkw6+DOvvhYGDsPRiWH5p7j9ih5NKQNv60R+40kr3IzZvqbuuXQpltUe+/MEuOLjZu2yB/nb3o5mpkFQ1uqpJeZ2r+mRXVGK9rgLU3w59bWOvU8PuB6f5XO8H+DwXAkrKp25PvA/2rnPPd/eT7u9kDJZe6ILEiW+E+qOwc3zvPveD2fnK9O5n0y60jFQ5vR+41DDULoOmM6BptfvhOpINmGgrdG4bfT1Lq8YGoEz4an3BVSz3vwC9e2HpBW5dL7/syINR23r3Xjn0SbvXub8d+lqhz7vub4dYdOLlBUOwcJVbH4tWu3VSkXWyjcQQdGz1wtJm6N4BVU3uh3/+Se56fPUw3udCWPdOF8T8JaOfrXlL3GNO9Lwy7/HhARemMkErVONCbjIO2//gQtWW37jPSUkVnHA51DRPHMyC5e7xsgMgFp7/D/ifO9z6aT7Phe8Vr3evYcdWeO4H8MI9MNgB1ce5aumep8GmXAA64Q3usuAUaH9p9HVufdG1K6Oqya3TpjPcpeEkiO4f/V7IfEcMHHS39wXd7Zac7z7jx53rvhMyYXzPU9B1uM+CgfknjL7Hm86AptPdxvrAwXGPvQU6t8NQl/ucTLXM8nr3Xq9ocN9PPbvcda78pTD/RBd4M8GxvN57v7ZDf9voe3bg4Oj34XD/4ZfrL3FBu6TKBe3Tr3N7MjIbR/tfgKe/C+v/030vLLvUvU92POqeO7i2Lb/Mra+y2kPfS75A1ndx22ibBzsnGd/Vug23TLBOxka/j/zB0ffjyPuzdJLwbqH/gPf52+X+B9eexWfDDQ/m/hrMIIVuhe4jkvIC82PbOnh8ewfP7OxmOJkm4DP8xZmLueXKk1hQXdgxfolF3Qcqn33RrHUh4OAWOLhpbDjMfAEcsoWeXcHITA+NqzxkXWfm+6dxaEX/Adjwc3jxJ+6HzPigpBLiXmhYsNJ9YS6/1H1pR/eN/YHv3um+sMvrDw24oWpo/bMXtJ8drWhULXJfhJkv34xQDVQvHr1/1cLRa18wKyBnheXMD2rmRxQgWOGWMdAB8V5yFprnfrSzH7d8PnRudQHg4GZ3O18AGk+HmsWHLsNa6NkN7Ru8KoqBhae6oFFSDlsfdq8/QN0KFwiXnO+tj56xzzEZc1/yyy91r8NUG5XxfhdG+lq9Xf3jNir8JaOvT/brFO+D3U+5oL37KYjuzX19jWf8h753fX73Hkl5x1CUVLlA0rTadTlYdqnbAJuItS78PP0d2HSf292cESz3nkeTe4y2P4+GL1/QhbOqJldJG+5zQWHZJXDClS40Vi70Pu/jfnRzqYBOxF+S9Z5d6AXjCV6veNR9Jrp3jE6rXuw2wHp2j/2RN36oOc59R2QHtMpGF/ISg27dDnZO3baqRW6jNlA6GlwOF95Kq936Tgy6z+VJ17hwteK1bjlHIhGDF/4DHr3Dvc8Wn+1eg12Pued60lVw9vthxevc6zLUDdt+B1sfdJfsNvtL3OcqE3LnLXGfz8wGV8fLjKzH7OfUcNJoAF10Fiw+6/B7n/oPuPdCf/vE8wc6RjcAovvGPl48a+OrtMYF4Prj3fflmA2dee5zMNSdteHvbcgNHHDzM8WJ2pbRjapg2ehGbiIrbJZUuNtMtodgKqmE+00c+Q7p9q6zvleGB9zn98Srpi5ADHTCc9+Hdd9376fll7qgvew1UL1o+m0rtOFB9/2f2Wiyabj8tllpikK3Qve0rX2pjf/7i/Ujp9Y9ubGKi46fz0XH13PusvqZOfNTOuW+EAIht3U7vhoW3Tf6Bbnfq5b0t43exhccDQ7BMveDOC/ri652qfvfH5zgy27IVVwzW+fZW+g9u8ZWECoa3Bd/9SIXSMYvZ3zFMDN//A/JRDIbEJnwU1o5NpyHatxl/3OukmVT7sfr9Oth1dtc5a31RXjlEbcLcPeTh1ZljM9VpGqXumr4YOfoc45lVQWNHxpP8ypJXqU4E1ZjvaO7Sbt3uku0dbRa2N8+NmiNLnT0OVQuzNoN6FV0qhePbjwlhsZWr4e6XPAbv8ESqjn8rsPBLtj7jBfInj50oyEjs1t6yXlw3Bq37Gzdu1yIePm3rvIzPtQFK1y7jM/tNs8sc9lrXEhdeqF7Lq0vjlb8OrZyyHvD+EefXzLurc9JzuZZtci1N/MaLTx18l24k5lsYy85PBqIMu1tW+9VoEqg5RK38XHiG9xnLBGDDT+Dp77jwnSoBs58jwtj2e+zzGcrFXfV40VeAFuwcjQYJodh9xPw8lq3vru2j22bLzha/fIF3HsvMz27Alo1SUAoKXfhvqx2ev1/h3rcc8t8D3W94j5LY3Znr3DvyXTKBfLsamnHy6N7i8bsMWpx3yfdWZ+pzCU1fOhGV2WjC2jx6Lg+tb2AheOvcO+7mdytnhyGF38Mj30dMK7v7+r/7TZYJpNOub1FXevuutcAACAASURBVK+49+aCUybufpExPABtG9x6qlns1mdVU/77aPcfcBtVrS+492j98aOvZ1Xj3OkjLgWn0K3QnbNEKs2X127hrj+9wunH1fDBS5Zz4Yr6VzeiRvcu13csO7B173I/TplgYXxjd3NmV1eNz+2ubTrDfYHDoQE3Meh2t3fv9CoY03hv+wLuxy3zA1fT7O3mO9k9bkX99J+zta5t2T+Q2ZXfxNDowUvZBzON+UH1DniK97o2nX4dnHbdaJ+7iSRirvrXtcPt9q1tcYF7sh/iRMwFvKFu96MzWSXzcNJp93r1tbnwnQmQM9HH+mgxPOBCVGm1e36l1WPXa+9e1/8xswE0vtpWvXjsLu2a5tEKWknl2B/5dNrrRtM2WkULlLpQWdNc2ECQSmSF4bWumgTuszFw0L3uC1bCuTe69+iRdA2ZSMc21xc1Hj10Qzc57CrOS86HRWeq/72IHDUUuhW6c9LWG+Ov73mOZ3Z2894LlvKpa05xw+IND3o//t5loMNVfZvOcLtUJwoAndtd/8KNv3KVhIyy2qxKdIvbdTdRFdr4RytiC0+d3g95Mu4CUPcOL9inRrtzjFxKXVtm8sjwfEmn3IaHKi/HDmtdQN/7tPusNJ4BlQ2z3aqZ0bndhe9tD7n+2ms+6Crgen+KiCh0K3Qf3mPbOvjYPc/RmNjN588a4Exedn17o/vG9nUbr7x+9ECYxtNHw3b7ejd/8Tmw8s1uV3vdskN334uIiIjMETojpUwqHevjiZ9+idi2R/l9YBvVvj54AVf9PW6N6yM40qcwa/zNnt1en0+v3+fj3xjt09t8Plz5j3DKm1wXBxEREZEiptBd7IYH2PfN/8VFfS/QXtZM2clvhpYLXD/J+uOn3l1ctRCa14z+n4i5A4cqFxwbRzqLiIiIFIhCdzEbHuTgd65lUfRF7mmJ8I73f+zVjZ0dDLn+1yIiIiIyxlF85JjkVWKI6L+9nbqOdXy77hO8/b0362Q1IiIiInmi0F2MEjFi//FOKlsf5/bSm3n3hz5O0K+3goiIiEi+KGkVm2Sc5E/eTWjXHwjbm7j+g7cwr3wGT6QgIiIiIodQ6C4myWHsve8jsP0h/j7xQV7/rr/l+AVVs90qERERkTlPobtYWAv33Yx5+QE+nfgAJ1z1V1x20oLZbpWIiIhIUVDoLhL28W/Ai/fwtcTbSZ71l3zgopbZbpKIiIhI0dCQgUVgeNNvCTz0WX6TOo+NJ9zEt65dpZFKRERERApIoXuOa932AtU//QAvp5ey6+KvcNcVp+HzKXCLiIiIFJJC9xz22PqtHPfz64kRJHrtD/jo2afPdpNEREREipL6dM9B6bTlm7/bhP3P99NEB4m3/zsXnq0zRYqIiIjMFoXuOSaeTPHXP3me8j+Eudi3Aa65g8ZVl812s0RERESKWsG7l5iIWQl8A7gA6AHuBiI2bFOHud85wBeBc7xJzwGfsmH7VB6be0wZiCf58A+fZfmOH/GB4Frs+R+hZM17Z7tZIiIiIkWvoJVuEzG1wMOABa4FPgf8HRA5zP2avfsFgPd4lwDwkImYpfls87Gia2CYd333SVbv/Fc+F/wBnHgV5orPz3azRERERITCV7pvAsqAt9qwjeJCczVwm4mYL3nTJnINUAX8hQ3bXgATMY8DHcDVwLfz3/Sj1/6eId579xP87+h3+UDgfjjtOnjLt8Cv42RFREREjgaF7tN9FbB2XLj+CS6IXzrF/YJAEhjImtbvTSvq8e+2Hejn+m/9iZv77uADvvvhvJvgL74D/uBsN01EREREPIUO3ScDm7Mn2LDdDQx68ybzc+82XzURs8BEzALgDqAb+M88tfWo9+yubt7z7T/wxcQ/8WbzJ3jtp+GNt4NPx8eKiIiIHE0K3f+gFnfw5Hjd3rwJ2bDdbyLmtcCvgZu9ya3AlTZsD854K49ym1qjfP3hrTz+0nZ+WP41Tk9vhmu+BmtumO2miYiIiMgEjolOvyZimnAV7WeBD3qTPwr8xkTMhV61fOx9jLkRuBFgyZIlhWpqXm1p6+Prv3uZh9fv4T2lj/I/1fdRlezCvP17sOqts908EREREZlEoUN3N1AzwfRab95kbsH16367DdsEgImY3wNbgY8zWv0eYa29C7gL4JxzzrGvrtmzJ522vLQ/yp1/2s7D63fx3pI/8Wz1r6kaPgAL1sAVP4ClF8x2M0VERERkCoUO3ZsZ13fbGw6wnHF9vcc5GXgpE7gBbNgOm4h5CViRj4bOlkQqzYZ9vTyzs4und3TxzM5u4kP9vL/kDzxbeT+ViQ5ovAAuvQuWXwamqI8jFRERETkmFDp0PwDcYiKmyoZtnzftemAI+OMU99sFXG0ipsSG7TCAiZhSYBVwXz4bnBepBPS3Q1879LdBXyupaBtP/XkT/T0HqEgPcKEZ4E3+QeaZQUKhAQwWFl8Cl34SWi5W2BYRERE5hhQ6dN+J6wryCxMx/wQsB24DvpY9jKCJmG3AH23YZo4MvBvXl/u/TMR8CzdM4EeBJrwuJMeM7l3wrfMhMThmss/4OCFdRbK0ltKqeipqFlFaWQ9l8yA0z1W11Y1ERERE5JhU0NBtw7bbRMzrgX/BVah7cEP/3TZBu/xZ93vWRMwbgTDwQ2/yeuAKG7Yv5rvdM+rARhe4L/0kLDoLqhZCZSO3/a6de59r4/lbriAU9B9+OSIiIiJyzCj46CU2bDcCrzvMbVommPY74Hd5albhRPe767PfD9WLAHew5AMbX+LSExsUuEVERETmIJ1FpdD6WsH4oGLByKQX9vZwoC/OlasWzmLDRERERCRfFLoLLdrqArd/dCfD2pfaCPgMrztZoVtERERkLlLoLrS+/VDdNPKvtZYHX2rnghX11JQFZ7FhIiIiIpIvCt2F1tcGVYtG/t16oJ8dHQNceWrjLDZKRERERPJJobvQomMr3b/d0IYx8IaV6loiIiIiMlcpdBdSYghiPVA1GrrXvtTGmc3zWFAdmsWGiYiIiEg+KXQXUma4QC907+ka5KX9Ud64Sl1LREREROYyhe5C6mt11173kgc3tgOoP7eIiIjIHKfQXUh9be7aO5By7UttnNxYxdL6illslIiIiIjkm0J3IWW6l1Q30dEf55mdXbxBVW4RERGROU+hu5D6WiFYAaXVPLyxHWvhylM1aomIiIjIXKfQXUjR/VDVCMaw9qU2muvKWNlUPdutEhEREZE8U+gupL5WqF5EXyzBY9s6uXJlI8aY2W6ViIiIiOSZQnch9bVCVRN/2HKQ4VSaKzVUoIiIiEhRUOguFGvd6CXVTax9qY35lSWctaR2tlslIiIiIgWg0F0og52QGiZR0cgjmw9wxcpG/D51LREREREpBgrdheINF7g3WcPAcIpLT2yY5QaJiIiISKEodBeKd2KcvhIXtueVB2ezNSIiIiJSQArdhdLnKt39wQUAlAa06kVERESKhZJfoURbAUNfsA6AUNA/u+0RERERkYJR6C6Uvv1Q0cBQyoVtVbpFREREioeSX6FEW6GqkXgyBUCpKt0iIiIiRUOhu1D62qB6EfFkGoCQKt0iIiIiRUPJr1D69kNVE7GEKt0iIiIixUahuxCScXdynOpFxBOqdIuIiIgUGyW/QuhrdddVTcSSKfw+Q8CvVS8iIiJSLJT8CiE6GrrjibRGLhEREREpMkp/hZCpdFc3EU+mNUa3iIiISJFR6C6E7O4liZQq3SIiIiJFRumvEKL7IRCCslpVukVERESKkEJ3IfS1QlUTGKNKt4iIiEgRUvorhKgXuoF4Mq0xukVERESKjEJ3IfS1QrUL3ap0i4iIiBQfpb98s3a0ewlepVuhW0RERKSoKP3l21A3JGNQvQhAB1KKiIiIFCGF7nzLGi4QIK7uJSIiIiJFR+kv36LjQrcq3SIiIiJFR6E737LORgk6kFJERESkGCn95dv47iXJNKUBVbpFREREiolCd75F90N5PQRKAYgnU4SCWu0iIiIixUTpL9/6WqHKjVySSlsSKatKt4iIiEiRCRT6AU3ErAS+AVwA9AB3AxEbtqkp7nMbEJ5k9v+1YfuPM93OGRPdD1WNgKtyA6p0i4iIiBSZgoZuEzG1wMPARuBaYAXwVVzF/dNT3PVu4Lfjpr0F+CTwwMy3dAb1tcGi1QDEEmkAHUgpIiIiUmQKXem+CSgD3mrDNgo8ZCKmGrjNRMyXvGmHsGG7F9ibPc1EzGeAzTZsX8h3o49YKgEDB0e6l4xWutW9RERERKSYFLrkehWwdly4/gkuiF+a60JMxNQDVwD3zGzzZlhfG2BHhguMZyrd6l4iIiIiUlQKnf5OBjZnT7BhuxsY9Obl6m1AkKM+dGeGC3SV7phX6daBlCIiIiLFpdChuxZ38OR43d68XL0DeM6G7dYZaVW+jIRu70BKr9KtAylFREREiktO6c/rznFUMBHThOuKMmWV2xhzozFmnTFm3cGDBwvTuPEyp4Cv9irdCVW6RURERIpRriXX/SZi7jURc5WJmFdTpu0GaiaYXuvNy8V1gAF+OtWNrLV3WWvPsdae09DQML1WzpS+/eAvcSfHwZ2NElTpFhERESk2uaa/DwMLgF8De0zEfNFEzIlH8HibGdd320RMM1DOuL7eU3gH8D82bPccweMXVrTVdS0xBlClW0RERKRY5RS6bdh+34btZcAJwL8C7wI2mYh5zETMDSZiKnN8vAeAK03EVGVNux4YAv54uDubiGkBzudoP4AyI+tslDBa6dY43SIiIiLFZVrpz4btKzZsP2vDtgU3ZF8KuAtoMxHzfRMxZx1mEXcCceAXJmIuNxFzI3Ab8LXsYQRNxGwzEfOvE9z/HUAS+M/ptHvW9LWOHEQJ2d1LVOkWERERKSbTLrmaiCk3EfN+4LPAxbizS94BnAI8YyLmlsnua8O2G3g94AfuAyLefcef4j3g3Wa8dwC/s2HbMd12F5y1rntJ9Wile7R7iSrdIiIiIsUk5zNSmoh5DfAB3BjZFndSm0/YsH3au8lnTMR8ArgV+PJky7FhuxF43VSP5VXSJ5q+Otf2zrp4FBIDUNU0OinTvUSVbhEREZGiklPoNhGzHWgBHgduBu61YTs4wU1/B9w+Y607lo0bLhBU6RYREREpVrlWun8GfM+G7ZapbmTD9lkKf8Kdo1Pffnc9UaVboVtERESkqOQUum3YfjLfDZlz+trc9ZgDKVOUBnwYbwhBERERESkOuZ6R8h9MxHxnknl3moj5/Mw2aw444Up436+hpnlkUjyRVpVbREREpAjlmgDfCTw6ybxHceN2S7aKelh2CQRKRibFkykNFygiIiJShHIN3YuAfZPM2+/Nl8OIJdKU6hTwIiIiIkUn1wTYBkx24puzgIMz05y5LZ5MEdIp4EVERESKTq6h+17gsyZirsmeaCLmauAzuDG75TBU6RYREREpTrkOGfhZYDVwn4mYTqAVaALqgAdxwVsOw41eokq3iIiISLHJdcjAGPAGEzFXAq8F6oFO3CnZH8pj++aUeCJNSJVuERERkaKT82ngAWzYrgXW5qktc14smaK6LDjbzRARERGRAptW6DYREwCWAKHx82zYbpypRs1VqnSLiIiIFKecQreJmCDwz8D7gNJJbqbOyocRU59uERERkaKUa9n1s8D/Am4ADPBXwAeA3wE7gTflo3FzjSrdIiIiIsUp1wR4HXAbbuhAgKdt2P67Dds3AP8DXJuHts058WRalW4RERGRIpRr6G4GXrZhmwJiQG3WvB8Bb5vphs1FsUSK0oAq3SIiIiLFJtcE2ArM8/7eAbwma96KGW3RHGWtdZXuoCrdIiIiIsUm19FLHgEuAe4Dvgt82UTM8UAcuB64Jy+tm0PiyTSAKt0iIiIiRSjX0P0pYD6ADdv/30SMAd4OlAHfAD6Xn+bNHZnQHVKlW0RERKToHDZ0e8MFrsB1KwHAhu0dwB15bNecE0+mAFW6RURERIpRLgkwBfweODnPbZnT4gl1LxEREREpVodNgDZs08BWoDH/zZm7MpVudS8RERERKT65ll0/BXzWRMxp+WzMXBZTpVtERESkaOV6IOWngXrgBRMx+4B2wGbfwIbtuTPctjlFlW4RERGR4pVr6N7gXeQIqdItIiIiUrxyCt02bD+Q74bMdap0i4iIiBQvlV0LZGT0kqBWuYiIiEixyanSbSLm3sPdxobtda++OXNXbGScblW6RURERIpNrn26GyaYVosbu7sT2DJjLZqjMpXukCrdIiIiIkUn1z7dr51ouomYZuC/0NkpDyuWUKVbREREpFi9qrKrDds9wD8CX5qZ5sxd8aQq3SIiIiLFaiYSYAo4bgaWM6dlQneJX6FbREREpNjkeiDlygkmlwCnAJ8HnpnJRs1FsUSKgM8QUOgWERERKTrTOTmOnWC6AdYBH5yxFs1R8WRaY3SLiIiIFKlcQ/dEB1LGgL02bPfNYHvmrFgipbNRioiIiBSpXEcv+WO+GzLXqdItIiIiUrxyKr2aiHmHiZhbJpl3i4kYnRjnMOLJtCrdIiIiIkUq1xT497juJBMZ8ObLFGKJFKWqdIuIiIgUpVxD9/G4gyknsgk4YWaaM3ep0i0iIiJSvHJNgYNMPhZ3MxCfmebMXTqQUkRERKR45ZoCHwY+YyJmQfZEEzENwKeAB2e6YXONDqQUERERKV65Dhn4SeBJYLuJmN8CrUATcCXQA3wi1wf0TrTzDeAC7753AxEbtqkc7vtWXP/xVbjq+zPA22zYDuT6+LMlnkhRWlU6280QERERkVmQU6Xbhu1u4AzgX3DdSa7yrr8BnGXDdk8uyzERU4urmlvgWuBzwN8BkRzu+0Hgx8AD3uN/ENhK7hsOs0qVbhEREZHilXNgtWF7kFc/SslNQBnwVhu2UeAhEzHVwG0mYr7kTTuEiZj5wB3AX9uw/W7WrP96le0pmLj6dIuIiIgUrVzH6T7DRMzVk8y72kTM6Tk+3lXA2nHh+ie4IH7pFPfLjAP+gxwf56gTS6YpDSp0i4iIiBSjXFPgHcB5k8xb483PxcnA5uwJXteVQW/eZM4DtgA3mIjZayImYSLmKRMxF+b4uLMunkgRCqh7iYiIiEgxyjV0nwU8Nsm8J4Azc1xOLe7gyfG6vXmTaQROAj6NO6jzTbiT8vzWRMzCHB97VqnSLSIiIlK8ck2BfqBiknkVQMnMNGdSBqgEbrBh+yMbtr8F3gKkgL+a8A7G3GiMWWeMWXfw4ME8N29qyVSaVNqq0i0iIiJSpHIN3c8AN04y70ZgXY7L6QZqJphe682b6n4WeCQzwesX/iywcqI7WGvvstaeY609p6GhIcfm5Uc8mQZQpVtERESkSOU6esltwMMmYp7CHczYhhun+73AauDyHJezmXF9t03ENAPljOvrPc4mXLXbjJtugHSOjz1rYgk3BLmGDBQREREpTrmO0/0n4A24gPsN4GfA14Ek8HrciXNy8QBwpYmYqqxp1wNDwB+nuN+vvevXZiaYiKkBzgZezPGxZ81IpVtDBoqIiIgUpZxToA3bR2zYXgBU4U6MUw2EgfcD7Tku5k4gDvzCRMzlJmJuxFXRv5Y9jKCJmG0mYv4167HXAb8C/tVEzPtMxFwD/DeQAL6Z63OYLZlKd6n6dIuIiIgUpSMpvZ6OO+37NmAt7syS9+RyRxu23bjKuB+4D3cmyjtw4T1bwLtNtncDvwS+hqu0J4DXecs8qmUq3SH16RYREREpSjn16TYRcxrwTuAdwFJgGDdiyd8B/2LDNpnrA9qw3Qi87jC3aZlgWj/wf7zLMWW0e4kq3SIiIiLFaNLQbSJmOS5ovxM4Bdd/+0HgM7j+17uB56YTuIvVSPcSVbpFREREitJUle5tuGH6ngI+DPw805XDO4hRcqRKt4iIiEhxm6r0ugs3JN8q4DLgQhMxuQ4xKFlGD6RUpVtERESkGE2aAm3YLgMuBL6PO/jxPqDdRMx3vf9tIRo4F4weSKlKt4iIiEgxmrL0asP2SRu2NwOLceN0/xJ4G270EIAPmYg5J79NPPap0i0iIiJS3HI9OU7ahu3DNmxvABYCfwHc610/ZSJmUx7beMxTpVtERESkuE27j7YN2wTuRDW/MhFTDrwFN5SgTCKu0UtEREREitqrOjDShu0g8GPvIpMYqXRr9BIRERGRoqTSawHEEymMgaDfzHZTRERERGQWKHQXQCyZpjTgwxiFbhEREZFipNBdAPFESgdRioiIiBQxhe4CiHuVbhEREREpTkqCBRBTpVtERESkqCl0F4Aq3SIiIiLFTUmwAGKJFKUaLlBERESkaCl0F0A8mSakE+OIiIiIFC0lwQJw3UtU6RYREREpVgrdBeAOpNSqFhERESlWSoIFoEq3iIiISHFT6C6AWCJFqSrdIiIiIkVLSbAAVOkWERERKW4K3QXghgzUqhYREREpVkqCBeCGDFSlW0RERKRYKXTnmbWWYZ2RUkRERKSoKQnmWTyZBlClW0RERKSIKXTnWTzhQrcq3SIiIiLFS0kwz2LJFICGDBQREREpYkqCeZapdIc0ZKCIiIhI0VLozrO4Kt0iIiIiRU9JMM9iqnSLiIiIFD2F7jxTpVtERERElATzbKTSrSEDRURERIqWQneejVS6NWSgiIiISNFSEsyzzMlxStWnW0RERKRoKXTnWSzhKt0h9ekWERERKVpKgnmmSreIiIiIKHTnmSrdIiIiIqIkmGeqdIuIiIiIQneeZSrdGr1EREREpHgpCeZZPJmmxO/D5zOz3RQRERERmSUK3XkWT6RV5RYREREpcoFCP6CJmJXAN4ALgB7gbiBiwzY1xX1agB0TzPqpDdt35KGZMyaWTFGqs1GKiIiIFLWChm4TMbXAw8BG4FpgBfBVXMX90zks4uPAY1n/d8x0G2eaKt0iIiIiUuhK901AGfBWG7ZR4CETMdXAbSZivuRNm8oWG7ZP5r2VMyiWTGm4QBEREZEiV+g0eBWwdly4/gkuiF9a4LYUhKt0q3uJiIiISDErdOg+GdicPcGG7W5g0Jt3OP9mIiZlIqbVRMzXTMSU5aORMymeTFGqSreIiIhIUSt095Ja3MGT43V78yYTB74JPAhEgcuAT+L6hF87s02cWfFEmpAq3SIiIiJFreCjlxwJG7atwF9lTXrEREw78C0TMWfYsH1x/H2MMTcCNwIsWbKkMA2dQDyZoraiZNYeX0RERERmX6H7PXQDNRNMr/XmTcfPvOuzJ5pprb3LWnuOtfachoaGaS565sRU6RYREREpeoUO3ZsZ13fbREwzUM64vt45sOOuj0rq0y0iIiIihU6DDwBXmoipypp2PTAE/HGay3q7d/3sTDQsX2Iap1tERESk6BW6T/edwM3AL0zE/BOwHLgN+Fr2MIImYrYBf7Rhe4P3/21AFe7EOFHgNcAtwC9s2P65kE9guuLJFCGdkVJERESkqBW0BGvDtht4PeAH7gMiwB1AeNxNA95tMjbjxvH+N+B+4F3Al73ro1o8qUq3iIiISLEr+OglNmw3Aq87zG1axv3/E9xJdI4p1lpiCVW6RURERIqdSrB5lExb0hZVukVERESKnNJgHsUSKQBVukVERESKnEJ3HsWTaUCVbhEREZFipzSYR6OhW5VuERERkWKm0J1Hme4lOjmOiIiISHFTGsyjeEKVbhERERFR6M6rWDJzIKVWs4iIiEgxUxrMI1W6RURERAQUuvMqnlSfbhERERFR6M6rmFfpDqnSLSIiIlLUFLrzSJVuEREREQGF7rzK9OnWGSlFREREiptCdx6NVLp1RkoRERGRoqY0mEcxVbpFREREBIXuvFKlW0REREQAArPdgLksnkzjMxDwmdluioiIiEhOEokEe/fuJRaLzXZTjmqhUIjjjjuOYDCY0+0VuvMolkgRCvoxRqFbREREjg179+6lqqqKlpYWZZhJWGvp7Oxk7969LFu2LKf7qN9DHsWTaXUtERERkWNKLBajvr5egXsKxhjq6+untTdAiTCPMpVuERERkWOJAvfhTXcdKXTnkSrdIiIiItPT09PDt771rWnf7+qrr6anpycPLZoZSoR5FE+kKdUp4EVERERyNlnoTiaTU97v/vvvZ968eflq1qumAynzKJZMEdIp4EVERERyduutt7J9+3ZWr15NMBgkFApRW1vL5s2befnll3nLW97Cnj17iMVifOxjH+PGG28EoKWlhXXr1tHf389VV13FxRdfzOOPP87ixYv51a9+RVlZ2aw+L4XuPFKlW0RERI5lkfteYuP+6Iwuc+WiasJvOnXS+bfffjsbNmzghRde4JFHHuGaa65hw4YNI6OEfO9736Ouro6hoSHWrFnD2972Nurr68csY+vWrdxzzz1897vf5brrruPnP/857373u2f0eUyXyrB5FEumKFWlW0REROSInXvuuWOG5fvnf/5nzjjjDM4//3z27NnD1q1bD7nPsmXLWL16NQBnn302O3fuLFRzJ6VKdx7FE2nqK1TpFhERkWPTVBXpQqmoqBj5+5FHHuHhhx/miSeeoLy8nMsuu2zCYftKS0tH/vb7/QwNDRWkrVNRGTaP4urTLSIiIjItVVVV9PX1TTivt7eX2tpaysvL2bx5M08++WSBW3fkVOnOo5j6dIuIiIhMS319PRdddBGrVq2irKyMhQsXjsx74xvfyJ133skpp5zCSSedxPnnnz+LLZ0ehe48iifT6tMtIiIiMk0//vGPJ5xeWlrKAw88MOG8TL/t+fPns2HDhpHpH//4x2e8fUdCiTCP4okUIVW6RURERIqeQnceqdItIiIiIqDQnTeptGU4lValW0REREQUuvNlOJkGUKVbRERERBS68yWeTAFQGtAqFhERESl2SoR5Eku4SncoqO4lIiIiIsVOoTtPVOkWERERkQwlwjxRpVtEREQk/yorKyedt3PnzknH/M7FF7/4xSO+73gK3XmiSreISIG3HQAADw9JREFUiIjI7DqaQrfOSJkn8aQq3SIiInKMe+BWaFs/s8tsPA2uun3S2bfeeivNzc189KMfBeC2224jEAjwhz/8ge7ubhKJBF/4whe49tprD/tQt956K5s2bWL16tW8733v4+abb+bWW2/lkUceIR6P89GPfpQPf/jDtLa2cv311xONRkkmk3z729/mN7/5DUNDQ6xevZpTTz2VH/3oR6/qaSt050ksoUq3iIiIyHRdf/31/M3f/M1I6L733ntZu3YtN998M9XV1XR0dHD++efz5je/GWPMlMu6/fbb+cpXvsKvf/3/2rv/KKnK+47j7y84YUUBVwpBQQPGGpCCSjCtNZqIp6CeoMSEgw2BEi0kOTVyIA3RGHt91BgJUZuAxXJychJPjEYJagGpRBGoibGiUq2CaFuwSNQFl19FEMm3fzx39TIsuuvOnWFmPq9z5uze5z73znf4srvfufM8z10EwLx58+jRowdPPvkke/bs4cwzz2TkyJEsWLCAUaNGcfXVV7Nv3z527drFWWedxZw5c1i9enVJXpeK7pzsScd0d9HNcURERKRavc8V6bycdtppvPHGG2zatImmpiYaGxvp06cP06ZNY+XKlXTq1IlXX32V119/nT59+rTr3EuXLuXZZ59l/vz5AGzbto2XXnqJ008/nUsvvZS9e/cyZswYTj311JK/LhXdOdmdjulu0M1xRERERNpl7NixzJ8/n9dee41x48Zx55130tTUxFNPPUWhUKB///7s3r273ed1d2bPns2oUaMO2Ldy5UoWL17MpEmTmD59OhMnTizFS3lX2YtuC3YyMBs4A9gK/AQInvi+Nh7fCfh34JPAaE98UV6xdoSudIuIiIh8OOPGjWPy5Mls3ryZFStWcM8999C7d28KhQKPPvooGzZsaNN5unXrxo4dO97dHjVqFHPnzmXEiBEUCgXWrVtH37592bx5M/369WPy5Mns2bOHp59+mokTJ1IoFNi7dy+FQqHDr6msRbcFawQeBl4ALgI+DtxMXEXlu208zd8C/XIJsITOHdSbhZd/mo/26FLpUERERESqyuDBg9mxYwd9+/blmGOOYfz48YwePZohQ4YwfPhwBg4c2KbzDB06lM6dO3PKKacwadIkpk6dyvr16xk2bBjuTq9evbj//vtZvnw5s2bNolAocOSRR3LHHXcAMGXKFIYOHcqwYcM6PJHS3L1DJ2jXkwW7CpgBfMwT3562zQCuBfq0tL3P8Y3AOuBK4hXyNl3pHj58uK9ataqD0YuIiIjUvjVr1jBo0KBKh1EVWvu3MrOn3H14cd9yDzg+H3ioqLi+Gzgc+Ewbjr8e+C3wSA6xiYiIiIjkotxjugcCy7INnvgrFmxXum/hwQ60YEOBS4GhuUYoIiIiIlXlueeeY8KECfu1denShSeeeKJCER2o3EV3I3HyZLHmdN/7mQ3M8cRftmD9SxyXiIiIiFSpIUOGlGw97bxUxZKBFuwS4BPA6DYfYzYFmAJw/PHH5xSZiIiISO1x9w+88Uy9a++8yHKP6W4GerTS3pjuO4AFKwCzgJlAJwt2FNA93X2EBevW2nHuPs/dh7v78F69enU8chEREZE60NDQwJYtW9pdVNYTd2fLli00NDS0+ZhyX+leSxy7/S4LdhzQNd3XmiOISwTekj6y7gb+CzixtGGKiIiI1Kd+/fqxceNGmpqaKh3KIa2hoYF+/dq+inW5i+4lwLcsWDdPvGWl8nHAW8CKgxyzEzinqK0PcBfwHYomZoqIiIjIh1coFBgwYEClw6g55S66bweuABZYsJnACcQ1um/JLiNowV4GVnjil3ni7wDLsyfJTKR8zhM/dKalioiIiIi0oqxjuj3xZuBcoDNxecAA3AokRV0PS/uIiIiIiFS9st6RslJ0R0oRERERKYeD3ZGyLopuM2sCNlTgqf8E2FyB55XyU67rh3JdP5Tr+qFc149y5Ppj7n7A0nl1UXRXipmtau2djtQe5bp+KNf1Q7muH8p1/ahkrsu9TreIiIiISN1R0S0iIiIikjMV3fmaV+kApGyU6/qhXNcP5bp+KNf1o2K51phuEREREZGc6Uq3iIiIiEjOyn1HyrpgwU4GZgNnAFuBnwDBE99X0cDkQ7NgY4EJwCeBHsCLwA898buK+k0GZgDHAc8DMzzxR8ocrpSQBetLzPcRQDdPfGfabsBVwNeJS1A9CVzhia+uVKzSfhbsMODvgcuA44Em4F5PfFqmj3JdAyzYJcTfzycB24BHgCs98U2ZPsp1lbFgJwLfItZcg4F/88Q/W9SnTXnNu37Tle4Ss2CNwMOAAxcB1wHfJN59U6rXdGAnMA24EHgU+KUF+0ZLBwv218DtwB3A+cSie5EF+7PyhyslNIuY+2JXAtcAM4HRaZ+HLVifMsYmHfcz4Argh8BIYl7fKuqjXFc5C3YhcBfwO+Lf5m8DZwOLLVi2FlKuq89g4ALixZF1B+nzgXktR/2mK92l9zXgcOBiT3w78BsL1h241oL9IG2T6jPaE88upr/Mgh1LLMZnp23XAj/3xK8HsGArgNOIP+xfLmOsUiIW7GzgPOBGYvHd0t5AzOv3PfE5advjwHrgcuC7ZQ9W2s2CnQeMA07xxF84SB/lujZ8CXjaE7+8pcGCbQceAD4BrFGuq9ZCT/wBAAs2n3gl+13tyGvu9ZuudJfe+cBDRcm5m5jIz1QmJOmoooK7xTPAsQAW7ATiR5b3ZI75I3Av8f+EVBkL1pn4huo6Drx72V8C3dk/3/8HLET5riaXAssOVnCnlOvaUCAOKcnamn619KtyXYXSv7Xvp615zb1+U9FdegOBtdkGT/wVYFe6T2rHGbz3UVZLbtcW9VkDHG3BDrgdrBzyvgZ0AW5rZd9AYB/wUlH7GvRzXk3+HFhnweZYsO0WbJcFW5B+itVCua4NPwXOsmATLVh3C3YScAP7v+lSrmtTW/Oae/2morv0Gnnv3XNWc7pPaoAFOxcYA9ycNrXktjj3zUX7pQpYsJ7A9cB0T3xvK10agZ2tTK5pBrpasI/kHaOURB9gEnAqcAnwFeJk6fvSiVegXNcET3wxMdfziFe8XwQ6A1/IdFOua1Nb85p7/aaiW6SdLFh/4JfAA574zyoajOTle8DvPfEHKx2I5MrSx0We+IOe+K+IqxR9ChhR0cikpCzYOcSJ7j8CziG+yTqa+AarcyVjk/qhorv0molLyhVr5L2rnlKlLNjRwBJgAzA+s6slt8W5byzaL4c4CzaYONb3Ogt2lAU7Cuia7u5hwQ4n5vPIVv5YNwK7PPG3yxexdEAz8JwnviXT9hjwNnBypo9yXf1uBv7FE/+2J748fYM1BvgscaUKUK5rVVvzmnv9pqK79NZSNPbHgh1H/KNdPN5XqogF6wosAj4CfM4T35XZ3ZLb4nFfA4E3PfGmMoQopfGnxElXjxN/0Tbz3rjujcTJlWuJH02fWHTsAWMC5ZC2hvcm0WUZ0DI5S7muDQOB/dZk9sRfJC4P+fG0SbmuTW3Na+71m4ru0lsCjLJg3TJt44g/2CsqE5J0VHoDjXuJBdl5nvgb2f2e+H8TJ1WOzRzTKd1eUsZQpeMeI378nH3MTPddQFw68HfAdvbPd1fi+q/Kd/VYBAyxYNklxs4mvun6j3Rbua4NG4Bh2QYLNoi4MsX6tEm5rk1tzWvu9ZvW6S6924k3WlhgwWYCJxDXb75Fa3RXtX8iFlxTgZ7pRLsWz3jie4h5/oUFWw/8FvgbYpH+pfKGKh2RLg+5PNuWjuOHeKezljtS3gRcY8GaiVdBphMvZMxGqsU84u/rhRbsRqAb8Q3Ww574YwCe+G7luibcDtxqwTYRi6uPAv9ALLgfBOW6WqUF9AXpZl+guwX7Yrr9oCe+q415zb1+U9FdYp54c7qyxRziGpBbgVuJiZPqNTL9+qNW9g0A1nvid1mwI4l3OruGeEfKz3ni/1mmGKW8biL+0r4K6AmsAv7KE3+9olFJm3ni2y3YCODHxPV43ybeLGVaUVfluvr9mJjfrxOXA91K/FTrqnTN5hbKdfXpTfwkOqtlewDxjdUH5rUc9Zu5e6nOJSIiIiIirdCYbhERERGRnKnoFhERERHJmYpuEREREZGcqegWEREREcmZim4RERERkZyp6BYRERERyZnW6RYRqXIW7FogOcjuCZ74L8oYDhbMgW944nPK+bwiIocyFd0iIrVhG3BeK+0vlzsQERE5kIpuEZHa8I4n/vtKByEiIq1T0S0iUuMsWH/gf4DxwPnAGOAt4DZPPBT1HQF8HziFePX818AMT3xnpk9P4EbgQqAR2ADM9cT/MXOqzhbsRmAy4MTbMk/3xPfk8BJFRA55KrpFRGqEBTvgd7on/k5mcxawCPgicDaQWLDNnvht6fGDgX8FfgN8ATgOuAk4gXToigU7HFgO9AYCsBY4MX1kfRNYBnwZGEos5DcAP+j4KxURqT4qukVEakNPYG9xowUbkNl83hP/avr9QxasN/AdCzbXE/8jcA2xML7QE9+XHv8m8CsLdoYn/jgwERgMDPPEV6fnWtZKPOs98UmZ5zoTuBgV3SJSp7RkoIhIbdgGnN7KY1Omz31FxywAjgX6pdufAu5rKbhTvwbeAT6dbo8AnskU3AeztGj7hczziIjUHV3pFhGpDe944qta22HBWr59o2hXy/YxwCvp19ezHTzxfRZsC3B02tQT+EMb4tlatP020NCG40REapKudIuI1I/eB9n+Q+brfn0sWGdiof1m2rSFWJyLiEg7qOgWEakfny/avphYaG9Mt58APp8W2tk+hwGPpduPAKdZsKF5BioiUms0vEREpDYcZsH+opX2/818P9iC/TNxnPbZwGXA1HQSJcANwDPA/RZsLnEM9kzgoXQSJcAdwN8BS9M7Yb4IDABO8sSvLPFrEhGpGbrSLSJSG3oAj7fy+EqmzwygO7Ho/ipwPfDurdo98eeJ63j3Jk6yvAG4i7jEYEuf3cTJlAuB64Al6XmzEzZFRKSIuXulYxARkRxlbo4z2hNfVNloRETqk650i4iIiIjkTEW3iIiIiEjONLxERERERCRnutItIiIiIpIzFd0iIiIiIjlT0S0iIiIikjMV3SIiIiIiOVPRLSIiIiKSMxXdIiIiIiI5+3+xbgtTReNtpQAAAABJRU5ErkJggg==\n",
            "text/plain": [
              "<Figure size 864x360 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "HBQw-d1HQ3aH",
        "outputId": "9c79a93b-3c7a-43b7-fcfe-5aa4250929d1"
      },
      "source": [
        "ypred=ann.predict(xtest)\n",
        "ypred=(ypred>0.5)\n",
        "from sklearn.metrics import accuracy_score\n",
        "print(accuracy_score(ytest, ypred))"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "0.8618181818181818\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "VjgIQcIMRHea",
        "outputId": "26805baf-4e4a-492f-e32a-41e776c45d71"
      },
      "source": [
        "from sklearn.metrics import confusion_matrix\n",
        "print(confusion_matrix(ytest, ypred))"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "[[2534  123]\n",
            " [ 333  310]]\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "KKAuFIqWRKD_"
      },
      "source": [
        ""
      ],
      "execution_count": null,
      "outputs": []
    }
  ]
}