assigment2 (3).ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 36,
   "id": "5bb65246-8a3f-44a0-b571-5e029631796c",
   "metadata": {},
   "outputs": [],
   "source": [
    "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 StandardScaler\n",
    "import tensorflow as tf\n",
    "from tensorflow import keras"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "9f3e9a0f-a4cb-4636-8109-1920dd552245",
   "metadata": {},
   "outputs": [],
   "source": [
    "df = pd.read_csv('pima-indians-diabetes.csv', header=None)\n",
    "df.columns =['Pregnancies', 'Glucose', 'BloodPressure', 'SkinThickness', 'Insulin', 'BMI', 'DiabetesPedigreeFunction', 'Age','Outcome']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "39999b71-8d23-4da8-a613-b111b2dc9378",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Pregnancies                 0\n",
       "Glucose                     0\n",
       "BloodPressure               0\n",
       "SkinThickness               0\n",
       "Insulin                     0\n",
       "BMI                         0\n",
       "DiabetesPedigreeFunction    0\n",
       "Age                         0\n",
       "Outcome                     0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.isna().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "755dbe1a-26d4-4680-9d23-f7c83206f512",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Pregnancies</th>\n",
       "      <th>Glucose</th>\n",
       "      <th>BloodPressure</th>\n",
       "      <th>SkinThickness</th>\n",
       "      <th>Insulin</th>\n",
       "      <th>BMI</th>\n",
       "      <th>DiabetesPedigreeFunction</th>\n",
       "      <th>Age</th>\n",
       "      <th>Outcome</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>6</td>\n",
       "      <td>148</td>\n",
       "      <td>72</td>\n",
       "      <td>35</td>\n",
       "      <td>0</td>\n",
       "      <td>33.6</td>\n",
       "      <td>0.627</td>\n",
       "      <td>50</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>85</td>\n",
       "      <td>66</td>\n",
       "      <td>29</td>\n",
       "      <td>0</td>\n",
       "      <td>26.6</td>\n",
       "      <td>0.351</td>\n",
       "      <td>31</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>8</td>\n",
       "      <td>183</td>\n",
       "      <td>64</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>23.3</td>\n",
       "      <td>0.672</td>\n",
       "      <td>32</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1</td>\n",
       "      <td>89</td>\n",
       "      <td>66</td>\n",
       "      <td>23</td>\n",
       "      <td>94</td>\n",
       "      <td>28.1</td>\n",
       "      <td>0.167</td>\n",
       "      <td>21</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0</td>\n",
       "      <td>137</td>\n",
       "      <td>40</td>\n",
       "      <td>35</td>\n",
       "      <td>168</td>\n",
       "      <td>43.1</td>\n",
       "      <td>2.288</td>\n",
       "      <td>33</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Pregnancies  Glucose  BloodPressure  SkinThickness  Insulin   BMI  \\\n",
       "0            6      148             72             35        0  33.6   \n",
       "1            1       85             66             29        0  26.6   \n",
       "2            8      183             64              0        0  23.3   \n",
       "3            1       89             66             23       94  28.1   \n",
       "4            0      137             40             35      168  43.1   \n",
       "\n",
       "   DiabetesPedigreeFunction  Age  Outcome  \n",
       "0                     0.627   50        1  \n",
       "1                     0.351   31        0  \n",
       "2                     0.672   32        1  \n",
       "3                     0.167   21        0  \n",
       "4                     2.288   33        1  "
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "d180d42c-9e36-412f-a87e-2d212328eb77",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Outcome\n",
       "0    500\n",
       "1    268\n",
       "Name: count, dtype: int64"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.Outcome.value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "2d3d91f9-0912-4608-be2e-33eb063a33fe",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Axes: xlabel='Outcome', ylabel='count'>"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.countplot(data=df, x=\"Outcome\", hue=\"Glucose\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "41e019e7-94ea-40d8-b6c1-44c62903d3ce",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Pregnancies</th>\n",
       "      <th>Glucose</th>\n",
       "      <th>BloodPressure</th>\n",
       "      <th>SkinThickness</th>\n",
       "      <th>Insulin</th>\n",
       "      <th>BMI</th>\n",
       "      <th>DiabetesPedigreeFunction</th>\n",
       "      <th>Age</th>\n",
       "      <th>Outcome</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>768.000000</td>\n",
       "      <td>768.000000</td>\n",
       "      <td>768.000000</td>\n",
       "      <td>768.000000</td>\n",
       "      <td>768.000000</td>\n",
       "      <td>768.000000</td>\n",
       "      <td>768.000000</td>\n",
       "      <td>768.000000</td>\n",
       "      <td>768.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>3.845052</td>\n",
       "      <td>120.894531</td>\n",
       "      <td>69.105469</td>\n",
       "      <td>20.536458</td>\n",
       "      <td>79.799479</td>\n",
       "      <td>31.992578</td>\n",
       "      <td>0.471876</td>\n",
       "      <td>33.240885</td>\n",
       "      <td>0.348958</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>3.369578</td>\n",
       "      <td>31.972618</td>\n",
       "      <td>19.355807</td>\n",
       "      <td>15.952218</td>\n",
       "      <td>115.244002</td>\n",
       "      <td>7.884160</td>\n",
       "      <td>0.331329</td>\n",
       "      <td>11.760232</td>\n",
       "      <td>0.476951</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.078000</td>\n",
       "      <td>21.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>99.000000</td>\n",
       "      <td>62.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>27.300000</td>\n",
       "      <td>0.243750</td>\n",
       "      <td>24.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>3.000000</td>\n",
       "      <td>117.000000</td>\n",
       "      <td>72.000000</td>\n",
       "      <td>23.000000</td>\n",
       "      <td>30.500000</td>\n",
       "      <td>32.000000</td>\n",
       "      <td>0.372500</td>\n",
       "      <td>29.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>6.000000</td>\n",
       "      <td>140.250000</td>\n",
       "      <td>80.000000</td>\n",
       "      <td>32.000000</td>\n",
       "      <td>127.250000</td>\n",
       "      <td>36.600000</td>\n",
       "      <td>0.626250</td>\n",
       "      <td>41.000000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>17.000000</td>\n",
       "      <td>199.000000</td>\n",
       "      <td>122.000000</td>\n",
       "      <td>99.000000</td>\n",
       "      <td>846.000000</td>\n",
       "      <td>67.100000</td>\n",
       "      <td>2.420000</td>\n",
       "      <td>81.000000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       Pregnancies     Glucose  BloodPressure  SkinThickness     Insulin  \\\n",
       "count   768.000000  768.000000     768.000000     768.000000  768.000000   \n",
       "mean      3.845052  120.894531      69.105469      20.536458   79.799479   \n",
       "std       3.369578   31.972618      19.355807      15.952218  115.244002   \n",
       "min       0.000000    0.000000       0.000000       0.000000    0.000000   \n",
       "25%       1.000000   99.000000      62.000000       0.000000    0.000000   \n",
       "50%       3.000000  117.000000      72.000000      23.000000   30.500000   \n",
       "75%       6.000000  140.250000      80.000000      32.000000  127.250000   \n",
       "max      17.000000  199.000000     122.000000      99.000000  846.000000   \n",
       "\n",
       "              BMI  DiabetesPedigreeFunction         Age     Outcome  \n",
       "count  768.000000                768.000000  768.000000  768.000000  \n",
       "mean    31.992578                  0.471876   33.240885    0.348958  \n",
       "std      7.884160                  0.331329   11.760232    0.476951  \n",
       "min      0.000000                  0.078000   21.000000    0.000000  \n",
       "25%     27.300000                  0.243750   24.000000    0.000000  \n",
       "50%     32.000000                  0.372500   29.000000    0.000000  \n",
       "75%     36.600000                  0.626250   41.000000    1.000000  \n",
       "max     67.100000                  2.420000   81.000000    1.000000  "
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "529998e9-90b7-460d-9e81-3cb1100b82ad",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "163f1740-e484-416e-b8d5-3598b6707a0a",
   "metadata": {},
   "outputs": [],
   "source": [
    "#Remove the feature with correlation to outcome less than 0.2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "f1788c6b-c817-4ed6-ac5a-05576b379d50",
   "metadata": {},
   "outputs": [],
   "source": [
    "y = df['Outcome']\n",
    "X = df.drop(columns=['Outcome'])\n",
    "X2 = X.drop(columns=['BloodPressure','SkinThickness','Insulin','DiabetesPedigreeFunction'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "a26c930b-7652-417b-9e6e-f2389768dddb",
   "metadata": {},
   "outputs": [],
   "source": [
    "X_train, X_test, y_train, y_test = train_test_split(X2, y, test_size=0.25, random_state=42)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "51b75c1a-7324-4aab-9fc5-4896111a4cfe",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/100\n",
      "18/18 [==============================] - 2s 28ms/step - loss: 0.4991 - accuracy: 0.7569 - val_loss: 0.5566 - val_accuracy: 0.7292\n",
      "Epoch 2/100\n",
      "18/18 [==============================] - 0s 6ms/step - loss: 0.4642 - accuracy: 0.7691 - val_loss: 0.5599 - val_accuracy: 0.7031\n",
      "Epoch 3/100\n",
      "18/18 [==============================] - 0s 6ms/step - loss: 0.4593 - accuracy: 0.7760 - val_loss: 0.5408 - val_accuracy: 0.7240\n",
      "Epoch 4/100\n",
      "18/18 [==============================] - 0s 6ms/step - loss: 0.4552 - accuracy: 0.7812 - val_loss: 0.5404 - val_accuracy: 0.7344\n",
      "Epoch 5/100\n",
      "18/18 [==============================] - 0s 7ms/step - loss: 0.4543 - accuracy: 0.7812 - val_loss: 0.5555 - val_accuracy: 0.7292\n",
      "Epoch 6/100\n",
      "18/18 [==============================] - 0s 6ms/step - loss: 0.4561 - accuracy: 0.7760 - val_loss: 0.5430 - val_accuracy: 0.7292\n",
      "Epoch 7/100\n",
      "18/18 [==============================] - 0s 7ms/step - loss: 0.4553 - accuracy: 0.7812 - val_loss: 0.5378 - val_accuracy: 0.7240\n",
      "Epoch 8/100\n",
      "18/18 [==============================] - 0s 6ms/step - loss: 0.4532 - accuracy: 0.7812 - val_loss: 0.5608 - val_accuracy: 0.7188\n",
      "Epoch 9/100\n",
      "18/18 [==============================] - 0s 6ms/step - loss: 0.4555 - accuracy: 0.7726 - val_loss: 0.5401 - val_accuracy: 0.7344\n",
      "Epoch 10/100\n",
      "18/18 [==============================] - 0s 6ms/step - loss: 0.4586 - accuracy: 0.7847 - val_loss: 0.5634 - val_accuracy: 0.7083\n",
      "Epoch 11/100\n",
      "18/18 [==============================] - 0s 7ms/step - loss: 0.4469 - accuracy: 0.7865 - val_loss: 0.5496 - val_accuracy: 0.7344\n",
      "Epoch 12/100\n",
      "18/18 [==============================] - 0s 7ms/step - loss: 0.4462 - accuracy: 0.7812 - val_loss: 0.5393 - val_accuracy: 0.7344\n",
      "Epoch 13/100\n",
      "18/18 [==============================] - 0s 7ms/step - loss: 0.4462 - accuracy: 0.7847 - val_loss: 0.5417 - val_accuracy: 0.7344\n",
      "Epoch 14/100\n",
      "18/18 [==============================] - 0s 10ms/step - loss: 0.4476 - accuracy: 0.7812 - val_loss: 0.5439 - val_accuracy: 0.7240\n",
      "Epoch 15/100\n",
      "18/18 [==============================] - 0s 6ms/step - loss: 0.4497 - accuracy: 0.7656 - val_loss: 0.5486 - val_accuracy: 0.7344\n",
      "Epoch 16/100\n",
      "18/18 [==============================] - 0s 7ms/step - loss: 0.4433 - accuracy: 0.7865 - val_loss: 0.5612 - val_accuracy: 0.7292\n",
      "Epoch 17/100\n",
      "18/18 [==============================] - 0s 8ms/step - loss: 0.4457 - accuracy: 0.7795 - val_loss: 0.5447 - val_accuracy: 0.7396\n",
      "Epoch 18/100\n",
      "18/18 [==============================] - 0s 7ms/step - loss: 0.4415 - accuracy: 0.7917 - val_loss: 0.5642 - val_accuracy: 0.7344\n",
      "Epoch 19/100\n",
      "18/18 [==============================] - 0s 7ms/step - loss: 0.4441 - accuracy: 0.7812 - val_loss: 0.5491 - val_accuracy: 0.7396\n",
      "Epoch 20/100\n",
      "18/18 [==============================] - 0s 6ms/step - loss: 0.4485 - accuracy: 0.7760 - val_loss: 0.5421 - val_accuracy: 0.7135\n",
      "Epoch 21/100\n",
      "18/18 [==============================] - 0s 7ms/step - loss: 0.4413 - accuracy: 0.7917 - val_loss: 0.5491 - val_accuracy: 0.7344\n",
      "Epoch 22/100\n",
      "18/18 [==============================] - 0s 9ms/step - loss: 0.4394 - accuracy: 0.7812 - val_loss: 0.5419 - val_accuracy: 0.7292\n",
      "Epoch 23/100\n",
      "18/18 [==============================] - 0s 7ms/step - loss: 0.4407 - accuracy: 0.7795 - val_loss: 0.5577 - val_accuracy: 0.7448\n",
      "Epoch 24/100\n",
      "18/18 [==============================] - 0s 7ms/step - loss: 0.4398 - accuracy: 0.7865 - val_loss: 0.5521 - val_accuracy: 0.7292\n",
      "Epoch 25/100\n",
      "18/18 [==============================] - 0s 7ms/step - loss: 0.4392 - accuracy: 0.7917 - val_loss: 0.5406 - val_accuracy: 0.7552\n",
      "Epoch 26/100\n",
      "18/18 [==============================] - 0s 7ms/step - loss: 0.4381 - accuracy: 0.7865 - val_loss: 0.5472 - val_accuracy: 0.7344\n",
      "Epoch 27/100\n",
      "18/18 [==============================] - 0s 7ms/step - loss: 0.4370 - accuracy: 0.7882 - val_loss: 0.5471 - val_accuracy: 0.7448\n",
      "Epoch 28/100\n",
      "18/18 [==============================] - 0s 7ms/step - loss: 0.4381 - accuracy: 0.7847 - val_loss: 0.5351 - val_accuracy: 0.7500\n",
      "Epoch 29/100\n",
      "18/18 [==============================] - 0s 7ms/step - loss: 0.4333 - accuracy: 0.7812 - val_loss: 0.5330 - val_accuracy: 0.7396\n",
      "Epoch 30/100\n",
      "18/18 [==============================] - 0s 7ms/step - loss: 0.4345 - accuracy: 0.7865 - val_loss: 0.5435 - val_accuracy: 0.7604\n",
      "Epoch 31/100\n",
      "18/18 [==============================] - 0s 6ms/step - loss: 0.4281 - accuracy: 0.7899 - val_loss: 0.5416 - val_accuracy: 0.7448\n",
      "Epoch 32/100\n",
      "18/18 [==============================] - 0s 6ms/step - loss: 0.4328 - accuracy: 0.7760 - val_loss: 0.5379 - val_accuracy: 0.7708\n",
      "Epoch 33/100\n",
      "18/18 [==============================] - 0s 7ms/step - loss: 0.4309 - accuracy: 0.7882 - val_loss: 0.5462 - val_accuracy: 0.7552\n",
      "Epoch 34/100\n",
      "18/18 [==============================] - 0s 6ms/step - loss: 0.4300 - accuracy: 0.7830 - val_loss: 0.5440 - val_accuracy: 0.7552\n",
      "Epoch 35/100\n",
      "18/18 [==============================] - 0s 7ms/step - loss: 0.4294 - accuracy: 0.7865 - val_loss: 0.5365 - val_accuracy: 0.7552\n",
      "Epoch 36/100\n",
      "18/18 [==============================] - 0s 6ms/step - loss: 0.4300 - accuracy: 0.7830 - val_loss: 0.5422 - val_accuracy: 0.7448\n",
      "Epoch 37/100\n",
      "18/18 [==============================] - 0s 7ms/step - loss: 0.4303 - accuracy: 0.7847 - val_loss: 0.5300 - val_accuracy: 0.7552\n",
      "Epoch 38/100\n",
      "18/18 [==============================] - 0s 8ms/step - loss: 0.4324 - accuracy: 0.7743 - val_loss: 0.5367 - val_accuracy: 0.7448\n",
      "Epoch 39/100\n",
      "18/18 [==============================] - 0s 8ms/step - loss: 0.4257 - accuracy: 0.7847 - val_loss: 0.5577 - val_accuracy: 0.7500\n",
      "Epoch 40/100\n",
      "18/18 [==============================] - 0s 9ms/step - loss: 0.4218 - accuracy: 0.7847 - val_loss: 0.5323 - val_accuracy: 0.7604\n",
      "Epoch 41/100\n",
      "18/18 [==============================] - 0s 7ms/step - loss: 0.4301 - accuracy: 0.7812 - val_loss: 0.5377 - val_accuracy: 0.7604\n",
      "Epoch 42/100\n",
      "18/18 [==============================] - 0s 8ms/step - loss: 0.4298 - accuracy: 0.7795 - val_loss: 0.5393 - val_accuracy: 0.7396\n",
      "Epoch 43/100\n",
      "18/18 [==============================] - 0s 8ms/step - loss: 0.4230 - accuracy: 0.7986 - val_loss: 0.5253 - val_accuracy: 0.7500\n",
      "Epoch 44/100\n",
      "18/18 [==============================] - 0s 7ms/step - loss: 0.4302 - accuracy: 0.7795 - val_loss: 0.5547 - val_accuracy: 0.7448\n",
      "Epoch 45/100\n",
      "18/18 [==============================] - 0s 8ms/step - loss: 0.4248 - accuracy: 0.7865 - val_loss: 0.5335 - val_accuracy: 0.7552\n",
      "Epoch 46/100\n",
      "18/18 [==============================] - 0s 7ms/step - loss: 0.4205 - accuracy: 0.7830 - val_loss: 0.5281 - val_accuracy: 0.7604\n",
      "Epoch 47/100\n",
      "18/18 [==============================] - 0s 7ms/step - loss: 0.4250 - accuracy: 0.7847 - val_loss: 0.5284 - val_accuracy: 0.7656\n",
      "Epoch 48/100\n",
      "18/18 [==============================] - 0s 7ms/step - loss: 0.4213 - accuracy: 0.7882 - val_loss: 0.5320 - val_accuracy: 0.7552\n",
      "Epoch 49/100\n",
      "18/18 [==============================] - 0s 7ms/step - loss: 0.4232 - accuracy: 0.7951 - val_loss: 0.5408 - val_accuracy: 0.7448\n",
      "Epoch 50/100\n",
      "18/18 [==============================] - 0s 8ms/step - loss: 0.4215 - accuracy: 0.7795 - val_loss: 0.5192 - val_accuracy: 0.7656\n",
      "Epoch 51/100\n",
      "18/18 [==============================] - 0s 8ms/step - loss: 0.4178 - accuracy: 0.7865 - val_loss: 0.5362 - val_accuracy: 0.7396\n",
      "Epoch 52/100\n",
      "18/18 [==============================] - 0s 9ms/step - loss: 0.4216 - accuracy: 0.7899 - val_loss: 0.5325 - val_accuracy: 0.7552\n",
      "Epoch 53/100\n",
      "18/18 [==============================] - 0s 7ms/step - loss: 0.4264 - accuracy: 0.7812 - val_loss: 0.5417 - val_accuracy: 0.7708\n",
      "Epoch 54/100\n",
      "18/18 [==============================] - 0s 9ms/step - loss: 0.4240 - accuracy: 0.7830 - val_loss: 0.5315 - val_accuracy: 0.7552\n",
      "Epoch 55/100\n",
      "18/18 [==============================] - 0s 7ms/step - loss: 0.4130 - accuracy: 0.7934 - val_loss: 0.5277 - val_accuracy: 0.7708\n",
      "Epoch 56/100\n",
      "18/18 [==============================] - 0s 7ms/step - loss: 0.4188 - accuracy: 0.7899 - val_loss: 0.5363 - val_accuracy: 0.7396\n",
      "Epoch 57/100\n",
      "18/18 [==============================] - 0s 10ms/step - loss: 0.4186 - accuracy: 0.7795 - val_loss: 0.5274 - val_accuracy: 0.7760\n",
      "Epoch 58/100\n",
      "18/18 [==============================] - 0s 8ms/step - loss: 0.4137 - accuracy: 0.7812 - val_loss: 0.5473 - val_accuracy: 0.7292\n",
      "Epoch 59/100\n",
      "18/18 [==============================] - 0s 8ms/step - loss: 0.4109 - accuracy: 0.7865 - val_loss: 0.5359 - val_accuracy: 0.7448\n",
      "Epoch 60/100\n",
      "18/18 [==============================] - 0s 8ms/step - loss: 0.4103 - accuracy: 0.7795 - val_loss: 0.5436 - val_accuracy: 0.7396\n",
      "Epoch 61/100\n",
      "18/18 [==============================] - 0s 9ms/step - loss: 0.4175 - accuracy: 0.7899 - val_loss: 0.5466 - val_accuracy: 0.7240\n",
      "Epoch 62/100\n",
      "18/18 [==============================] - 0s 8ms/step - loss: 0.4120 - accuracy: 0.7969 - val_loss: 0.5280 - val_accuracy: 0.7604\n",
      "Epoch 63/100\n",
      "18/18 [==============================] - 0s 7ms/step - loss: 0.4100 - accuracy: 0.7760 - val_loss: 0.5347 - val_accuracy: 0.7500\n",
      "Epoch 64/100\n",
      "18/18 [==============================] - 0s 7ms/step - loss: 0.4086 - accuracy: 0.8021 - val_loss: 0.5453 - val_accuracy: 0.7396\n",
      "Epoch 65/100\n",
      "18/18 [==============================] - 0s 7ms/step - loss: 0.4057 - accuracy: 0.7778 - val_loss: 0.5222 - val_accuracy: 0.7552\n",
      "Epoch 66/100\n",
      "18/18 [==============================] - 0s 7ms/step - loss: 0.4120 - accuracy: 0.7778 - val_loss: 0.5397 - val_accuracy: 0.7448\n",
      "Epoch 67/100\n",
      "18/18 [==============================] - 0s 7ms/step - loss: 0.4153 - accuracy: 0.7899 - val_loss: 0.5641 - val_accuracy: 0.7135\n",
      "Epoch 68/100\n",
      "18/18 [==============================] - 0s 8ms/step - loss: 0.4113 - accuracy: 0.7865 - val_loss: 0.5195 - val_accuracy: 0.7448\n",
      "Epoch 69/100\n",
      "18/18 [==============================] - 0s 7ms/step - loss: 0.4105 - accuracy: 0.7865 - val_loss: 0.5617 - val_accuracy: 0.7292\n",
      "Epoch 70/100\n",
      "18/18 [==============================] - 0s 8ms/step - loss: 0.4157 - accuracy: 0.7917 - val_loss: 0.5464 - val_accuracy: 0.7604\n",
      "Epoch 71/100\n",
      "18/18 [==============================] - 0s 7ms/step - loss: 0.4093 - accuracy: 0.7917 - val_loss: 0.5250 - val_accuracy: 0.7500\n",
      "Epoch 72/100\n",
      "18/18 [==============================] - 0s 10ms/step - loss: 0.4079 - accuracy: 0.7917 - val_loss: 0.5485 - val_accuracy: 0.7396\n",
      "Epoch 73/100\n",
      "18/18 [==============================] - 0s 7ms/step - loss: 0.4147 - accuracy: 0.7934 - val_loss: 0.5410 - val_accuracy: 0.7500\n",
      "Epoch 74/100\n",
      "18/18 [==============================] - 0s 7ms/step - loss: 0.4023 - accuracy: 0.7882 - val_loss: 0.5255 - val_accuracy: 0.7604\n",
      "Epoch 75/100\n",
      "18/18 [==============================] - 0s 7ms/step - loss: 0.4017 - accuracy: 0.7882 - val_loss: 0.5287 - val_accuracy: 0.7396\n",
      "Epoch 76/100\n",
      "18/18 [==============================] - 0s 10ms/step - loss: 0.4101 - accuracy: 0.7882 - val_loss: 0.5321 - val_accuracy: 0.7865\n",
      "Epoch 77/100\n",
      "18/18 [==============================] - 0s 7ms/step - loss: 0.4085 - accuracy: 0.8021 - val_loss: 0.5345 - val_accuracy: 0.7500\n",
      "Epoch 78/100\n",
      "18/18 [==============================] - 0s 8ms/step - loss: 0.4029 - accuracy: 0.7917 - val_loss: 0.5430 - val_accuracy: 0.7188\n",
      "Epoch 79/100\n",
      "18/18 [==============================] - 0s 8ms/step - loss: 0.4068 - accuracy: 0.7812 - val_loss: 0.5477 - val_accuracy: 0.7448\n",
      "Epoch 80/100\n",
      "18/18 [==============================] - 0s 8ms/step - loss: 0.4115 - accuracy: 0.7934 - val_loss: 0.5584 - val_accuracy: 0.7552\n",
      "Epoch 81/100\n",
      "18/18 [==============================] - 0s 7ms/step - loss: 0.4059 - accuracy: 0.7951 - val_loss: 0.5440 - val_accuracy: 0.7500\n",
      "Epoch 82/100\n",
      "18/18 [==============================] - 0s 6ms/step - loss: 0.4042 - accuracy: 0.7865 - val_loss: 0.5359 - val_accuracy: 0.7188\n",
      "Epoch 83/100\n",
      "18/18 [==============================] - 0s 6ms/step - loss: 0.4026 - accuracy: 0.7969 - val_loss: 0.5439 - val_accuracy: 0.7656\n",
      "Epoch 84/100\n",
      "18/18 [==============================] - 0s 9ms/step - loss: 0.4126 - accuracy: 0.7847 - val_loss: 0.5459 - val_accuracy: 0.7760\n",
      "Epoch 85/100\n",
      "18/18 [==============================] - 0s 7ms/step - loss: 0.4079 - accuracy: 0.8021 - val_loss: 0.5474 - val_accuracy: 0.7396\n",
      "Epoch 86/100\n",
      "18/18 [==============================] - 0s 7ms/step - loss: 0.4045 - accuracy: 0.7795 - val_loss: 0.5630 - val_accuracy: 0.7552\n",
      "Epoch 87/100\n",
      "18/18 [==============================] - 0s 7ms/step - loss: 0.4036 - accuracy: 0.8003 - val_loss: 0.5489 - val_accuracy: 0.7604\n",
      "Epoch 88/100\n",
      "18/18 [==============================] - 0s 9ms/step - loss: 0.3952 - accuracy: 0.8056 - val_loss: 0.5354 - val_accuracy: 0.7448\n",
      "Epoch 89/100\n",
      "18/18 [==============================] - 0s 8ms/step - loss: 0.3994 - accuracy: 0.8003 - val_loss: 0.5628 - val_accuracy: 0.7656\n",
      "Epoch 90/100\n",
      "18/18 [==============================] - 0s 7ms/step - loss: 0.4007 - accuracy: 0.7986 - val_loss: 0.5303 - val_accuracy: 0.7396\n",
      "Epoch 91/100\n",
      "18/18 [==============================] - 0s 7ms/step - loss: 0.3939 - accuracy: 0.8073 - val_loss: 0.5444 - val_accuracy: 0.7552\n",
      "Epoch 92/100\n",
      "18/18 [==============================] - 0s 8ms/step - loss: 0.3960 - accuracy: 0.8021 - val_loss: 0.5395 - val_accuracy: 0.7396\n",
      "Epoch 93/100\n",
      "18/18 [==============================] - 0s 8ms/step - loss: 0.3916 - accuracy: 0.7899 - val_loss: 0.5496 - val_accuracy: 0.7708\n",
      "Epoch 94/100\n",
      "18/18 [==============================] - 0s 7ms/step - loss: 0.3965 - accuracy: 0.8056 - val_loss: 0.5511 - val_accuracy: 0.7292\n",
      "Epoch 95/100\n",
      "18/18 [==============================] - 0s 7ms/step - loss: 0.3970 - accuracy: 0.8056 - val_loss: 0.5485 - val_accuracy: 0.7656\n",
      "Epoch 96/100\n",
      "18/18 [==============================] - 0s 7ms/step - loss: 0.3959 - accuracy: 0.7899 - val_loss: 0.5501 - val_accuracy: 0.7344\n",
      "Epoch 97/100\n",
      "18/18 [==============================] - 0s 8ms/step - loss: 0.3898 - accuracy: 0.8056 - val_loss: 0.5475 - val_accuracy: 0.7448\n",
      "Epoch 98/100\n",
      "18/18 [==============================] - 0s 8ms/step - loss: 0.3972 - accuracy: 0.8194 - val_loss: 0.5590 - val_accuracy: 0.7240\n",
      "Epoch 99/100\n",
      "18/18 [==============================] - 0s 9ms/step - loss: 0.3939 - accuracy: 0.8003 - val_loss: 0.5295 - val_accuracy: 0.7448\n",
      "Epoch 100/100\n",
      "18/18 [==============================] - 0s 7ms/step - loss: 0.3893 - accuracy: 0.8056 - val_loss: 0.5716 - val_accuracy: 0.7552\n"
     ]
    }
   ],
   "source": [
    "# Set random seed\n",
    "tf.random.set_seed(42)\n",
    "\n",
    "scaler = StandardScaler()\n",
    "X_train = scaler.fit_transform(X_train)\n",
    "X_test = scaler.transform(X_test)\n",
    "\n",
    "normalize = tf.keras.layers.Normalization()\n",
    "normalize.adapt(X_train)\n",
    "\n",
    "model = tf.keras.Sequential([\n",
    "  tf.keras.layers.Dense(20, activation='elu'),\n",
    "    tf.keras.layers.Dense(20, activation='elu'),\n",
    "    tf.keras.layers.Dense(1, activation=\"sigmoid\") \n",
    "])\n",
    "\n",
    "model.compile(loss=tf.keras.losses.binary_crossentropy,\n",
    "                optimizer=tf.keras.optimizers.Adam(learning_rate=0.01),\n",
    "                  metrics=['accuracy'])\n",
    "\n",
    "# Fit the model\n",
    "class_weight = {0: 1, 1: 0.8}\n",
    "history = model.fit(X_train, y_train, epochs=100,  validation_data=(X_test, y_test))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "id": "e2cd25cf-5125-4a65-85cd-8fb6cdd96b09",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Model: \"sequential\"\n",
      "_________________________________________________________________\n",
      " Layer (type)                Output Shape              Param #   \n",
      "=================================================================\n",
      " dense (Dense)               (32, 20)                  100       \n",
      "                                                                 \n",
      " dense_1 (Dense)             (32, 20)                  420       \n",
      "                                                                 \n",
      " dense_2 (Dense)             (32, 1)                   21        \n",
      "                                                                 \n",
      "=================================================================\n",
      "Total params: 541 (2.11 KB)\n",
      "Trainable params: 541 (2.11 KB)\n",
      "Non-trainable params: 0 (0.00 Byte)\n",
      "_________________________________________________________________\n"
     ]
    }
   ],
   "source": [
    "model.summary()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "id": "e931dba5-ddd7-45ef-8fab-4bf324d9add5",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<IPython.core.display.Image object>"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "keras.utils.plot_model(\n",
    "    model, show_shapes=True, dpi=100, to_file=\"model.png\"\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "782f6fcc-d96d-44e4-a15a-971bc78e225a",
   "metadata": {},
   "outputs": [],
   "source": [
    "acc = history.history"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "id": "aa8a6a5a-eee6-4a62-9285-280332160ae2",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'loss': [0.49909061193466187,\n",
       "  0.46415069699287415,\n",
       "  0.45928293466567993,\n",
       "  0.4551975429058075,\n",
       "  0.4542659521102905,\n",
       "  0.45614030957221985,\n",
       "  0.4553070664405823,\n",
       "  0.4532024562358856,\n",
       "  0.4555184543132782,\n",
       "  0.458630234003067,\n",
       "  0.44691312313079834,\n",
       "  0.44620281457901,\n",
       "  0.44621118903160095,\n",
       "  0.44755256175994873,\n",
       "  0.4496752917766571,\n",
       "  0.44331496953964233,\n",
       "  0.44566333293914795,\n",
       "  0.4414874017238617,\n",
       "  0.44407832622528076,\n",
       "  0.4485430121421814,\n",
       "  0.4412625730037689,\n",
       "  0.439414381980896,\n",
       "  0.4406895339488983,\n",
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       "  0.41051486134529114,\n",
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       "  0.40927958488464355,\n",
       "  0.40792524814605713,\n",
       "  0.4147009253501892,\n",
       "  0.40234535932540894,\n",
       "  0.40174728631973267,\n",
       "  0.4101438820362091,\n",
       "  0.4085365831851959,\n",
       "  0.4028571546077728,\n",
       "  0.4068126678466797,\n",
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       "  0.4078733026981354,\n",
       "  0.40450727939605713,\n",
       "  0.4035724103450775,\n",
       "  0.395222544670105,\n",
       "  0.3993825316429138,\n",
       "  0.40066060423851013,\n",
       "  0.39393338561058044,\n",
       "  0.3960459232330322,\n",
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       "  0.3897964060306549,\n",
       "  0.39717328548431396,\n",
       "  0.39392149448394775,\n",
       "  0.38927704095840454],\n",
       " 'accuracy': [0.7569444179534912,\n",
       "  0.7690972089767456,\n",
       "  0.7760416865348816,\n",
       "  0.78125,\n",
       "  0.78125,\n",
       "  0.7760416865348816,\n",
       "  0.78125,\n",
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       "  0.7847222089767456,\n",
       "  0.78125,\n",
       "  0.765625,\n",
       "  0.7864583134651184,\n",
       "  0.7795138955116272,\n",
       "  0.7916666865348816,\n",
       "  0.78125,\n",
       "  0.7760416865348816,\n",
       "  0.7916666865348816,\n",
       "  0.78125,\n",
       "  0.7795138955116272,\n",
       "  0.7864583134651184,\n",
       "  0.7916666865348816,\n",
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       "  0.78125,\n",
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       "  0.7864583134651184,\n",
       "  0.7795138955116272,\n",
       "  0.7899305820465088,\n",
       "  0.796875,\n",
       "  0.7760416865348816,\n",
       "  0.8020833134651184,\n",
       "  0.7777777910232544,\n",
       "  0.7777777910232544,\n",
       "  0.7899305820465088,\n",
       "  0.7864583134651184,\n",
       "  0.7864583134651184,\n",
       "  0.7916666865348816,\n",
       "  0.7916666865348816,\n",
       "  0.7916666865348816,\n",
       "  0.7934027910232544,\n",
       "  0.7881944179534912,\n",
       "  0.7881944179534912,\n",
       "  0.7881944179534912,\n",
       "  0.8020833134651184,\n",
       "  0.7916666865348816,\n",
       "  0.78125,\n",
       "  0.7934027910232544,\n",
       "  0.7951388955116272,\n",
       "  0.7864583134651184,\n",
       "  0.796875,\n",
       "  0.7847222089767456,\n",
       "  0.8020833134651184,\n",
       "  0.7795138955116272,\n",
       "  0.8003472089767456,\n",
       "  0.8055555820465088,\n",
       "  0.8003472089767456,\n",
       "  0.7986111044883728,\n",
       "  0.8072916865348816,\n",
       "  0.8020833134651184,\n",
       "  0.7899305820465088,\n",
       "  0.8055555820465088,\n",
       "  0.8055555820465088,\n",
       "  0.7899305820465088,\n",
       "  0.8055555820465088,\n",
       "  0.8194444179534912,\n",
       "  0.8003472089767456,\n",
       "  0.8055555820465088],\n",
       " 'val_loss': [0.556553304195404,\n",
       "  0.5598669648170471,\n",
       "  0.5408079028129578,\n",
       "  0.5403981804847717,\n",
       "  0.5554971694946289,\n",
       "  0.5429741144180298,\n",
       "  0.5377904772758484,\n",
       "  0.5608286261558533,\n",
       "  0.5400776863098145,\n",
       "  0.5634291172027588,\n",
       "  0.549550473690033,\n",
       "  0.5392982363700867,\n",
       "  0.5417295098304749,\n",
       "  0.5439035296440125,\n",
       "  0.5485994219779968,\n",
       "  0.56119304895401,\n",
       "  0.5447280406951904,\n",
       "  0.564177930355072,\n",
       "  0.5490838885307312,\n",
       "  0.5421330332756042,\n",
       "  0.5491392016410828,\n",
       "  0.5419130325317383,\n",
       "  0.5576972961425781,\n",
       "  0.5521265864372253,\n",
       "  0.5405500531196594,\n",
       "  0.5471969246864319,\n",
       "  0.5471252799034119,\n",
       "  0.5350683331489563,\n",
       "  0.5329818725585938,\n",
       "  0.543495237827301,\n",
       "  0.5415950417518616,\n",
       "  0.5378575921058655,\n",
       "  0.5462236404418945,\n",
       "  0.5440396666526794,\n",
       "  0.5364549160003662,\n",
       "  0.5422003269195557,\n",
       "  0.5300352573394775,\n",
       "  0.5367274284362793,\n",
       "  0.5576539039611816,\n",
       "  0.5323086380958557,\n",
       "  0.5377165675163269,\n",
       "  0.5393127799034119,\n",
       "  0.5252633690834045,\n",
       "  0.554671585559845,\n",
       "  0.5334773659706116,\n",
       "  0.5281187891960144,\n",
       "  0.5284424424171448,\n",
       "  0.5320203900337219,\n",
       "  0.5407567024230957,\n",
       "  0.5191969275474548,\n",
       "  0.5362400412559509,\n",
       "  0.5324590802192688,\n",
       "  0.5416653752326965,\n",
       "  0.5314702391624451,\n",
       "  0.5277454257011414,\n",
       "  0.5362749695777893,\n",
       "  0.5274137258529663,\n",
       "  0.5472924709320068,\n",
       "  0.5359252691268921,\n",
       "  0.5435657501220703,\n",
       "  0.5466108918190002,\n",
       "  0.5279717445373535,\n",
       "  0.5346629619598389,\n",
       "  0.545330822467804,\n",
       "  0.5222248435020447,\n",
       "  0.5397170186042786,\n",
       "  0.5640701055526733,\n",
       "  0.5195347666740417,\n",
       "  0.561718225479126,\n",
       "  0.546395480632782,\n",
       "  0.5249884724617004,\n",
       "  0.5484538674354553,\n",
       "  0.540968656539917,\n",
       "  0.5255072712898254,\n",
       "  0.5287399291992188,\n",
       "  0.5320530533790588,\n",
       "  0.5344744920730591,\n",
       "  0.542962372303009,\n",
       "  0.5477073788642883,\n",
       "  0.5584022402763367,\n",
       "  0.5439878702163696,\n",
       "  0.5359445810317993,\n",
       "  0.5438576340675354,\n",
       "  0.5459111332893372,\n",
       "  0.5474180579185486,\n",
       "  0.5629664063453674,\n",
       "  0.5489348769187927,\n",
       "  0.5354378819465637,\n",
       "  0.5627732872962952,\n",
       "  0.5303413271903992,\n",
       "  0.5443537831306458,\n",
       "  0.5394622087478638,\n",
       "  0.549613893032074,\n",
       "  0.5510625243186951,\n",
       "  0.5484818816184998,\n",
       "  0.5500578284263611,\n",
       "  0.547450602054596,\n",
       "  0.5590419769287109,\n",
       "  0.5295080542564392,\n",
       "  0.5716295838356018],\n",
       " 'val_accuracy': [0.7291666865348816,\n",
       "  0.703125,\n",
       "  0.7239583134651184,\n",
       "  0.734375,\n",
       "  0.7291666865348816,\n",
       "  0.7291666865348816,\n",
       "  0.7239583134651184,\n",
       "  0.71875,\n",
       "  0.734375,\n",
       "  0.7083333134651184,\n",
       "  0.734375,\n",
       "  0.734375,\n",
       "  0.734375,\n",
       "  0.7239583134651184,\n",
       "  0.734375,\n",
       "  0.7291666865348816,\n",
       "  0.7395833134651184,\n",
       "  0.734375,\n",
       "  0.7395833134651184,\n",
       "  0.7135416865348816,\n",
       "  0.734375,\n",
       "  0.7291666865348816,\n",
       "  0.7447916865348816,\n",
       "  0.7291666865348816,\n",
       "  0.7552083134651184,\n",
       "  0.734375,\n",
       "  0.7447916865348816,\n",
       "  0.75,\n",
       "  0.7395833134651184,\n",
       "  0.7604166865348816,\n",
       "  0.7447916865348816,\n",
       "  0.7708333134651184,\n",
       "  0.7552083134651184,\n",
       "  0.7552083134651184,\n",
       "  0.7552083134651184,\n",
       "  0.7447916865348816,\n",
       "  0.7552083134651184,\n",
       "  0.7447916865348816,\n",
       "  0.75,\n",
       "  0.7604166865348816,\n",
       "  0.7604166865348816,\n",
       "  0.7395833134651184,\n",
       "  0.75,\n",
       "  0.7447916865348816,\n",
       "  0.7552083134651184,\n",
       "  0.7604166865348816,\n",
       "  0.765625,\n",
       "  0.7552083134651184,\n",
       "  0.7447916865348816,\n",
       "  0.765625,\n",
       "  0.7395833134651184,\n",
       "  0.7552083134651184,\n",
       "  0.7708333134651184,\n",
       "  0.7552083134651184,\n",
       "  0.7708333134651184,\n",
       "  0.7395833134651184,\n",
       "  0.7760416865348816,\n",
       "  0.7291666865348816,\n",
       "  0.7447916865348816,\n",
       "  0.7395833134651184,\n",
       "  0.7239583134651184,\n",
       "  0.7604166865348816,\n",
       "  0.75,\n",
       "  0.7395833134651184,\n",
       "  0.7552083134651184,\n",
       "  0.7447916865348816,\n",
       "  0.7135416865348816,\n",
       "  0.7447916865348816,\n",
       "  0.7291666865348816,\n",
       "  0.7604166865348816,\n",
       "  0.75,\n",
       "  0.7395833134651184,\n",
       "  0.75,\n",
       "  0.7604166865348816,\n",
       "  0.7395833134651184,\n",
       "  0.7864583134651184,\n",
       "  0.75,\n",
       "  0.71875,\n",
       "  0.7447916865348816,\n",
       "  0.7552083134651184,\n",
       "  0.75,\n",
       "  0.71875,\n",
       "  0.765625,\n",
       "  0.7760416865348816,\n",
       "  0.7395833134651184,\n",
       "  0.7552083134651184,\n",
       "  0.7604166865348816,\n",
       "  0.7447916865348816,\n",
       "  0.765625,\n",
       "  0.7395833134651184,\n",
       "  0.7552083134651184,\n",
       "  0.7395833134651184,\n",
       "  0.7708333134651184,\n",
       "  0.7291666865348816,\n",
       "  0.765625,\n",
       "  0.734375,\n",
       "  0.7447916865348816,\n",
       "  0.7239583134651184,\n",
       "  0.7447916865348816,\n",
       "  0.7552083134651184]}"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "acc"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "id": "380fcf8a-d1d4-4bb6-859d-ee07711096fe",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x222f69b3a10>]"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(acc['accuracy'])\n",
    "plt.plot(acc['val_accuracy'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "1003ba5d-fbf2-4b33-b99c-39b671d32ed3",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "6/6 [==============================] - 0s 3ms/step - loss: 0.5716 - accuracy: 0.7552\n",
      "Model loss on test set: 0.5716295838356018\n",
      "Model accuracy on test set: 75.52%\n"
     ]
    }
   ],
   "source": [
    "loss, accuracy = model.evaluate(X_test, y_test)\n",
    "print(f\"Model loss on test set: {loss}\")\n",
    "print(f\"Model accuracy on test set: {(accuracy*100):.2f}%\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "76f22b7c-e46b-4fc8-a3eb-8f126bf8bb24",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "6/6 [==============================] - 0s 2ms/step\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "array([[95, 28],\n",
       "       [19, 50]], dtype=int64)"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Create a confusion matrix\n",
    "from sklearn.metrics import confusion_matrix\n",
    "\n",
    "# Make predictions\n",
    "y_preds = model.predict(X_test)\n",
    "\n",
    "# Create confusion matrix\n",
    "confusion_matrix(y_test, tf.round(y_preds))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "fd0c70c1-80e5-444a-8291-73f23d4c6b2f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x1000 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import itertools\n",
    "\n",
    "figsize = (10, 10)\n",
    "\n",
    "# Create the confusion matrix\n",
    "cm = confusion_matrix(y_test, tf.round(y_preds))\n",
    "cm_norm = cm.astype(\"float\") / cm.sum(axis=1)[:, np.newaxis] # normalize it\n",
    "n_classes = cm.shape[0]\n",
    "\n",
    "# Let's prettify it\n",
    "fig, ax = plt.subplots(figsize=figsize)\n",
    "# Create a matrix plot\n",
    "cax = ax.matshow(cm, cmap=plt.cm.Blues) # https://matplotlib.org/3.2.0/api/_as_gen/matplotlib.axes.Axes.matshow.html\n",
    "fig.colorbar(cax)\n",
    "\n",
    "# Create classes\n",
    "classes = False\n",
    "\n",
    "if classes:\n",
    "  labels = classes\n",
    "else:\n",
    "  labels = np.arange(cm.shape[0])\n",
    "\n",
    "# Label the axes\n",
    "ax.set(title=\"Confusion Matrix\",\n",
    "       xlabel=\"Predicted label\",\n",
    "       ylabel=\"True label\",\n",
    "       xticks=np.arange(n_classes),\n",
    "       yticks=np.arange(n_classes),\n",
    "       xticklabels=labels,\n",
    "       yticklabels=labels)\n",
    "\n",
    "# Set x-axis labels to bottom\n",
    "ax.xaxis.set_label_position(\"bottom\")\n",
    "ax.xaxis.tick_bottom()\n",
    "\n",
    "# Adjust label size\n",
    "ax.xaxis.label.set_size(20)\n",
    "ax.yaxis.label.set_size(20)\n",
    "ax.title.set_size(20)\n",
    "\n",
    "# Set threshold for different colors\n",
    "threshold = (cm.max() + cm.min()) / 2.\n",
    "\n",
    "# Plot the text on each cell\n",
    "for i, j in itertools.product(range(cm.shape[0]), range(cm.shape[1])):\n",
    "  plt.text(j, i, f\"{cm[i, j]} ({cm_norm[i, j]*100:.1f}%)\",\n",
    "           horizontalalignment=\"center\",\n",
    "           color=\"white\" if cm[i, j] > threshold else \"black\",\n",
    "           size=15)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "id": "4641f18b-2015-4a9a-a6d0-bb786aa2314a",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.83      0.77      0.80       123\n",
      "           1       0.64      0.72      0.68        69\n",
      "\n",
      "    accuracy                           0.76       192\n",
      "   macro avg       0.74      0.75      0.74       192\n",
      "weighted avg       0.76      0.76      0.76       192\n",
      "\n"
     ]
    }
   ],
   "source": [
    "from sklearn.metrics import classification_report\n",
    "print(classification_report(y_test, tf.round(y_preds)))\n",
    "                            \n",
    "#confusion_matrixy_test, tf.round(y_preds))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "22ad9cfd-0204-4f9f-96a1-0cd2b0acbd82",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "034b51c5-7928-4bcd-941e-f20c30bac01b",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "ebeaaae3-b153-4e18-91ac-fa55f903285d",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "b51b0557-d5d4-4018-a7db-8292f2985b94",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "a387e49e-89ca-4493-b410-31d997d77ee6",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "da3f5a0f-0064-4d71-b6af-6fcadb419810",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "2fb22942-8b10-45e5-89b9-db6c45af04c4",
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