diabetes-diagnosis.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Using machine learning to create a model to diagnosis diabetes\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
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   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Load dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "execution": {
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   "outputs": [],
   "source": [
    "df = pd.read_csv(\"./diabetes.csv\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### The dataset has 9 columns, the Outcome is the target column"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
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       "  <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",
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       "      <th>3</th>\n",
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       "      <td>135</td>\n",
       "      <td>68</td>\n",
       "      <td>42</td>\n",
       "      <td>250</td>\n",
       "      <td>42.3</td>\n",
       "      <td>0.365</td>\n",
       "      <td>24</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1</td>\n",
       "      <td>139</td>\n",
       "      <td>62</td>\n",
       "      <td>41</td>\n",
       "      <td>480</td>\n",
       "      <td>40.7</td>\n",
       "      <td>0.536</td>\n",
       "      <td>21</td>\n",
       "      <td>0</td>\n",
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       "  </tbody>\n",
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      ],
      "text/plain": [
       "   Pregnancies  Glucose  BloodPressure  SkinThickness  Insulin   BMI  \\\n",
       "0            2      138             62             35        0  33.6   \n",
       "1            0       84             82             31      125  38.2   \n",
       "2            0      145              0              0        0  44.2   \n",
       "3            0      135             68             42      250  42.3   \n",
       "4            1      139             62             41      480  40.7   \n",
       "\n",
       "   DiabetesPedigreeFunction  Age  Outcome  \n",
       "0                     0.127   47        1  \n",
       "1                     0.233   23        0  \n",
       "2                     0.630   31        1  \n",
       "3                     0.365   24        1  \n",
       "4                     0.536   21        0  "
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "First, we need to check if the dataset has some Nan values and fill it with the data according with the column.\n",
    "\n",
    "We can see below that there isn't columns with Nan Values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "execution": {
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    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 2000 entries, 0 to 1999\n",
      "Data columns (total 9 columns):\n",
      " #   Column                    Non-Null Count  Dtype  \n",
      "---  ------                    --------------  -----  \n",
      " 0   Pregnancies               2000 non-null   int64  \n",
      " 1   Glucose                   2000 non-null   int64  \n",
      " 2   BloodPressure             2000 non-null   int64  \n",
      " 3   SkinThickness             2000 non-null   int64  \n",
      " 4   Insulin                   2000 non-null   int64  \n",
      " 5   BMI                       2000 non-null   float64\n",
      " 6   DiabetesPedigreeFunction  2000 non-null   float64\n",
      " 7   Age                       2000 non-null   int64  \n",
      " 8   Outcome                   2000 non-null   int64  \n",
      "dtypes: float64(2), int64(7)\n",
      "memory usage: 140.8 KB\n"
     ]
    }
   ],
   "source": [
    "df.info()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "All columns are numeric, so we'll need only to normalize the values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "execution": {
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       "      <th>Glucose</th>\n",
       "      <th>BloodPressure</th>\n",
       "      <th>SkinThickness</th>\n",
       "      <th>Insulin</th>\n",
       "      <th>BMI</th>\n",
       "      <th>DiabetesPedigreeFunction</th>\n",
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       "      <th>Outcome</th>\n",
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       "      <th>count</th>\n",
       "      <td>2000.000000</td>\n",
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       "      <td>2000.000000</td>\n",
       "      <td>2000.000000</td>\n",
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       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>3.703500</td>\n",
       "      <td>121.182500</td>\n",
       "      <td>69.145500</td>\n",
       "      <td>20.935000</td>\n",
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       "      <td>0.470930</td>\n",
       "      <td>33.090500</td>\n",
       "      <td>0.342000</td>\n",
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       "    <tr>\n",
       "      <th>std</th>\n",
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       "      <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>63.500000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>27.375000</td>\n",
       "      <td>0.244000</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>40.000000</td>\n",
       "      <td>32.300000</td>\n",
       "      <td>0.376000</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>141.000000</td>\n",
       "      <td>80.000000</td>\n",
       "      <td>32.000000</td>\n",
       "      <td>130.000000</td>\n",
       "      <td>36.800000</td>\n",
       "      <td>0.624000</td>\n",
       "      <td>40.000000</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>110.000000</td>\n",
       "      <td>744.000000</td>\n",
       "      <td>80.600000</td>\n",
       "      <td>2.420000</td>\n",
       "      <td>81.000000</td>\n",
       "      <td>1.000000</td>\n",
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      ],
      "text/plain": [
       "       Pregnancies      Glucose  BloodPressure  SkinThickness      Insulin  \\\n",
       "count  2000.000000  2000.000000    2000.000000    2000.000000  2000.000000   \n",
       "mean      3.703500   121.182500      69.145500      20.935000    80.254000   \n",
       "std       3.306063    32.068636      19.188315      16.103243   111.180534   \n",
       "min       0.000000     0.000000       0.000000       0.000000     0.000000   \n",
       "25%       1.000000    99.000000      63.500000       0.000000     0.000000   \n",
       "50%       3.000000   117.000000      72.000000      23.000000    40.000000   \n",
       "75%       6.000000   141.000000      80.000000      32.000000   130.000000   \n",
       "max      17.000000   199.000000     122.000000     110.000000   744.000000   \n",
       "\n",
       "               BMI  DiabetesPedigreeFunction          Age      Outcome  \n",
       "count  2000.000000               2000.000000  2000.000000  2000.000000  \n",
       "mean     32.193000                  0.470930    33.090500     0.342000  \n",
       "std       8.149901                  0.323553    11.786423     0.474498  \n",
       "min       0.000000                  0.078000    21.000000     0.000000  \n",
       "25%      27.375000                  0.244000    24.000000     0.000000  \n",
       "50%      32.300000                  0.376000    29.000000     0.000000  \n",
       "75%      36.800000                  0.624000    40.000000     1.000000  \n",
       "max      80.600000                  2.420000    81.000000     1.000000  "
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.describe()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Then we'll need to separate the dataset between train dataset and test dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-10-02T18:31:03.588402Z",
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     "shell.execute_reply.started": "2021-10-02T18:31:03.588369Z"
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   },
   "outputs": [],
   "source": [
    "from sklearn.model_selection import train_test_split\n",
    "X_train, X_test, y_train, y_test = train_test_split(df.drop(columns=[\"Outcome\"]), df[\"Outcome\"], test_size=0.3, random_state=42)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "And normalize the TRAIN dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-10-02T18:31:03.887508Z",
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     "shell.execute_reply.started": "2021-10-02T18:31:03.887471Z"
    }
   },
   "outputs": [],
   "source": [
    "from sklearn.preprocessing import MinMaxScaler\n",
    "scaler = MinMaxScaler()\n",
    "X_train_norm = pd.DataFrame(scaler.fit_transform(X_train))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
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       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1395</th>\n",
       "      <td>0.176471</td>\n",
       "      <td>0.618090</td>\n",
       "      <td>0.819672</td>\n",
       "      <td>0.318182</td>\n",
       "      <td>0.322581</td>\n",
       "      <td>0.710918</td>\n",
       "      <td>0.342442</td>\n",
       "      <td>0.016667</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1396</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.502513</td>\n",
       "      <td>0.573770</td>\n",
       "      <td>0.236364</td>\n",
       "      <td>0.067204</td>\n",
       "      <td>0.382134</td>\n",
       "      <td>0.221605</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1397</th>\n",
       "      <td>0.058824</td>\n",
       "      <td>0.502513</td>\n",
       "      <td>0.540984</td>\n",
       "      <td>0.263636</td>\n",
       "      <td>0.263441</td>\n",
       "      <td>0.397022</td>\n",
       "      <td>0.156277</td>\n",
       "      <td>0.350000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1398</th>\n",
       "      <td>0.235294</td>\n",
       "      <td>0.477387</td>\n",
       "      <td>0.524590</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.397022</td>\n",
       "      <td>0.035440</td>\n",
       "      <td>0.166667</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1399</th>\n",
       "      <td>0.529412</td>\n",
       "      <td>0.773869</td>\n",
       "      <td>0.639344</td>\n",
       "      <td>0.272727</td>\n",
       "      <td>0.134409</td>\n",
       "      <td>0.383375</td>\n",
       "      <td>0.036721</td>\n",
       "      <td>0.400000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>1400 rows × 8 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "             0         1         2         3         4         5         6  \\\n",
       "0     0.352941  0.623116  0.590164  0.000000  0.000000  0.342432  0.123826   \n",
       "1     0.058824  0.597990  0.360656  0.427273  0.084677  0.440447  0.086251   \n",
       "2     0.470588  0.552764  0.622951  0.000000  0.000000  0.344913  0.067891   \n",
       "3     0.000000  0.643216  0.557377  0.172727  0.241935  0.378412  0.560632   \n",
       "4     0.000000  0.542714  0.557377  0.181818  0.000000  0.338710  0.302733   \n",
       "...        ...       ...       ...       ...       ...       ...       ...   \n",
       "1395  0.176471  0.618090  0.819672  0.318182  0.322581  0.710918  0.342442   \n",
       "1396  0.000000  0.502513  0.573770  0.236364  0.067204  0.382134  0.221605   \n",
       "1397  0.058824  0.502513  0.540984  0.263636  0.263441  0.397022  0.156277   \n",
       "1398  0.235294  0.477387  0.524590  0.000000  0.000000  0.397022  0.035440   \n",
       "1399  0.529412  0.773869  0.639344  0.272727  0.134409  0.383375  0.036721   \n",
       "\n",
       "             7  \n",
       "0     0.133333  \n",
       "1     0.066667  \n",
       "2     0.616667  \n",
       "3     0.066667  \n",
       "4     0.183333  \n",
       "...        ...  \n",
       "1395  0.016667  \n",
       "1396  0.000000  \n",
       "1397  0.350000  \n",
       "1398  0.166667  \n",
       "1399  0.400000  \n",
       "\n",
       "[1400 rows x 8 columns]"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_train_norm"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### The dataset is ready to be use*d* in a training process. Let's try to create a neural network using the lib Keras.\n",
    "The warning messages can be ignored"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-10-02T18:31:03.918128Z",
     "iopub.status.busy": "2021-10-02T18:31:03.917894Z",
     "iopub.status.idle": "2021-10-02T18:31:05.676702Z",
     "shell.execute_reply": "2021-10-02T18:31:05.675779Z",
     "shell.execute_reply.started": "2021-10-02T18:31:03.918102Z"
    }
   },
   "outputs": [],
   "source": [
    "from tensorflow.keras.models import Sequential\n",
    "from tensorflow.keras.layers import Dense\n",
    "\n",
    "neural_network = Sequential()\n",
    "neural_network.add(Dense(units=16,activation='relu', input_shape = X_train_norm.iloc[0].shape))\n",
    "neural_network.add(Dense(units=10,activation='relu'))\n",
    "neural_network.add(Dense(units=2,activation='softmax'))\n",
    "\n",
    "neural_network.compile(optimizer='adam', loss='binary_crossentropy',metrics=['accuracy'])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This is how the network architecture should be like"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-10-02T18:31:05.678065Z",
     "iopub.status.busy": "2021-10-02T18:31:05.677827Z",
     "iopub.status.idle": "2021-10-02T18:31:05.794260Z",
     "shell.execute_reply": "2021-10-02T18:31:05.793185Z",
     "shell.execute_reply.started": "2021-10-02T18:31:05.678040Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "('You must install pydot (`pip install pydot`) and install graphviz (see instructions at https://graphviz.gitlab.io/download/) ', 'for plot_model/model_to_dot to work.')\n"
     ]
    }
   ],
   "source": [
    "from tensorflow.keras.utils import plot_model\n",
    "plot_model(neural_network, show_shapes=True, show_layer_names=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The array y_train need to be encoded with the values that represents the diagnosis\n",
    "\n",
    "The index position representing the class will be set with 1.\n",
    "\n",
    "[1, 0] - Class 0: negative diagnostic\n",
    "\n",
    "[0, 1] - Class 1: positive diagnostic"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-10-02T18:31:05.796156Z",
     "iopub.status.busy": "2021-10-02T18:31:05.795731Z",
     "iopub.status.idle": "2021-10-02T18:31:05.805558Z",
     "shell.execute_reply": "2021-10-02T18:31:05.804948Z",
     "shell.execute_reply.started": "2021-10-02T18:31:05.796121Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "matrix([[0., 1.],\n",
       "        [1., 0.],\n",
       "        [1., 0.],\n",
       "        ...,\n",
       "        [1., 0.],\n",
       "        [0., 1.],\n",
       "        [1., 0.]])"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.preprocessing import OneHotEncoder\n",
    "encoder = OneHotEncoder()\n",
    "y_train_encoded = encoder.fit_transform(y_train.values.reshape(-1, 1)).todense()\n",
    "y_train_encoded"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's train the neural network"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-10-02T18:31:05.807189Z",
     "iopub.status.busy": "2021-10-02T18:31:05.806623Z",
     "iopub.status.idle": "2021-10-02T18:31:18.965575Z",
     "shell.execute_reply": "2021-10-02T18:31:18.964639Z",
     "shell.execute_reply.started": "2021-10-02T18:31:05.807156Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/100\n",
      "140/140 [==============================] - 1s 1ms/step - loss: 0.6653 - accuracy: 0.6650\n",
      "Epoch 2/100\n",
      "140/140 [==============================] - 0s 1ms/step - loss: 0.6416 - accuracy: 0.6621\n",
      "Epoch 3/100\n",
      "140/140 [==============================] - 0s 1ms/step - loss: 0.6208 - accuracy: 0.6650\n",
      "Epoch 4/100\n",
      "140/140 [==============================] - 0s 1ms/step - loss: 0.5987 - accuracy: 0.6793\n",
      "Epoch 5/100\n",
      "140/140 [==============================] - 0s 1ms/step - loss: 0.5777 - accuracy: 0.6950\n",
      "Epoch 6/100\n",
      "140/140 [==============================] - 0s 1ms/step - loss: 0.5593 - accuracy: 0.7093\n",
      "Epoch 7/100\n",
      "140/140 [==============================] - 0s 1ms/step - loss: 0.5463 - accuracy: 0.7121\n",
      "Epoch 8/100\n",
      "140/140 [==============================] - 0s 1ms/step - loss: 0.5331 - accuracy: 0.7286\n",
      "Epoch 9/100\n",
      "140/140 [==============================] - 0s 1ms/step - loss: 0.5213 - accuracy: 0.7336\n",
      "Epoch 10/100\n",
      "140/140 [==============================] - 0s 1ms/step - loss: 0.5141 - accuracy: 0.7400\n",
      "Epoch 11/100\n",
      "140/140 [==============================] - 0s 1ms/step - loss: 0.5078 - accuracy: 0.7564\n",
      "Epoch 12/100\n",
      "140/140 [==============================] - 0s 1ms/step - loss: 0.4981 - accuracy: 0.7550\n",
      "Epoch 13/100\n",
      "140/140 [==============================] - 0s 1ms/step - loss: 0.4910 - accuracy: 0.7707\n",
      "Epoch 14/100\n",
      "140/140 [==============================] - 0s 1ms/step - loss: 0.4898 - accuracy: 0.7607\n",
      "Epoch 15/100\n",
      "140/140 [==============================] - 0s 1ms/step - loss: 0.4856 - accuracy: 0.7643\n",
      "Epoch 16/100\n",
      "140/140 [==============================] - 0s 1ms/step - loss: 0.4816 - accuracy: 0.7600\n",
      "Epoch 17/100\n",
      "140/140 [==============================] - 0s 1ms/step - loss: 0.4807 - accuracy: 0.7636\n",
      "Epoch 18/100\n",
      "140/140 [==============================] - 0s 1ms/step - loss: 0.4778 - accuracy: 0.7650\n",
      "Epoch 19/100\n",
      "140/140 [==============================] - 0s 1ms/step - loss: 0.4751 - accuracy: 0.7629\n",
      "Epoch 20/100\n",
      "140/140 [==============================] - 0s 1ms/step - loss: 0.4730 - accuracy: 0.7679\n",
      "Epoch 21/100\n",
      "140/140 [==============================] - 0s 1ms/step - loss: 0.4704 - accuracy: 0.7693\n",
      "Epoch 22/100\n",
      "140/140 [==============================] - 0s 1ms/step - loss: 0.4696 - accuracy: 0.7657\n",
      "Epoch 23/100\n",
      "140/140 [==============================] - 0s 1ms/step - loss: 0.4662 - accuracy: 0.7700\n",
      "Epoch 24/100\n",
      "140/140 [==============================] - 0s 1ms/step - loss: 0.4631 - accuracy: 0.7707\n",
      "Epoch 25/100\n",
      "140/140 [==============================] - 0s 1ms/step - loss: 0.4669 - accuracy: 0.7629\n",
      "Epoch 26/100\n",
      "140/140 [==============================] - 0s 1ms/step - loss: 0.4653 - accuracy: 0.7686\n",
      "Epoch 27/100\n",
      "140/140 [==============================] - 0s 1ms/step - loss: 0.4646 - accuracy: 0.7643\n",
      "Epoch 28/100\n",
      "140/140 [==============================] - 0s 1ms/step - loss: 0.4635 - accuracy: 0.7700\n",
      "Epoch 29/100\n",
      "140/140 [==============================] - 0s 1ms/step - loss: 0.4635 - accuracy: 0.7700\n",
      "Epoch 30/100\n",
      "140/140 [==============================] - 0s 1ms/step - loss: 0.4593 - accuracy: 0.7736\n",
      "Epoch 31/100\n",
      "140/140 [==============================] - 0s 1ms/step - loss: 0.4616 - accuracy: 0.7714\n",
      "Epoch 32/100\n",
      "140/140 [==============================] - 0s 1ms/step - loss: 0.4609 - accuracy: 0.7721\n",
      "Epoch 33/100\n",
      "140/140 [==============================] - 0s 1ms/step - loss: 0.4601 - accuracy: 0.7707\n",
      "Epoch 34/100\n",
      "140/140 [==============================] - 0s 1ms/step - loss: 0.4608 - accuracy: 0.7707\n",
      "Epoch 35/100\n",
      "140/140 [==============================] - 0s 1ms/step - loss: 0.4586 - accuracy: 0.7743\n",
      "Epoch 36/100\n",
      "140/140 [==============================] - 0s 1ms/step - loss: 0.4572 - accuracy: 0.7729\n",
      "Epoch 37/100\n",
      "140/140 [==============================] - 0s 1ms/step - loss: 0.4576 - accuracy: 0.7771\n",
      "Epoch 38/100\n",
      "140/140 [==============================] - 0s 1ms/step - loss: 0.4564 - accuracy: 0.7757\n",
      "Epoch 39/100\n",
      "140/140 [==============================] - 0s 1ms/step - loss: 0.4561 - accuracy: 0.7721\n",
      "Epoch 40/100\n",
      "140/140 [==============================] - 0s 1ms/step - loss: 0.4546 - accuracy: 0.7714\n",
      "Epoch 41/100\n",
      "140/140 [==============================] - 0s 1ms/step - loss: 0.4554 - accuracy: 0.7707\n",
      "Epoch 42/100\n",
      "140/140 [==============================] - 0s 1ms/step - loss: 0.4546 - accuracy: 0.7721\n",
      "Epoch 43/100\n",
      "140/140 [==============================] - 0s 1ms/step - loss: 0.4529 - accuracy: 0.7743\n",
      "Epoch 44/100\n",
      "140/140 [==============================] - 0s 1ms/step - loss: 0.4520 - accuracy: 0.7743\n",
      "Epoch 45/100\n",
      "140/140 [==============================] - 0s 1ms/step - loss: 0.4532 - accuracy: 0.7721\n",
      "Epoch 46/100\n",
      "140/140 [==============================] - 0s 1ms/step - loss: 0.4533 - accuracy: 0.7814\n",
      "Epoch 47/100\n",
      "140/140 [==============================] - 0s 1ms/step - loss: 0.4500 - accuracy: 0.7779\n",
      "Epoch 48/100\n",
      "140/140 [==============================] - 0s 1ms/step - loss: 0.4547 - accuracy: 0.7743\n",
      "Epoch 49/100\n",
      "140/140 [==============================] - 0s 1ms/step - loss: 0.4516 - accuracy: 0.7757\n",
      "Epoch 50/100\n",
      "140/140 [==============================] - 0s 1ms/step - loss: 0.4489 - accuracy: 0.7743\n",
      "Epoch 51/100\n",
      "140/140 [==============================] - 0s 1ms/step - loss: 0.4498 - accuracy: 0.7807\n",
      "Epoch 52/100\n",
      "140/140 [==============================] - 0s 1ms/step - loss: 0.4506 - accuracy: 0.7764\n",
      "Epoch 53/100\n",
      "140/140 [==============================] - 0s 1ms/step - loss: 0.4499 - accuracy: 0.7771\n",
      "Epoch 54/100\n",
      "140/140 [==============================] - 0s 1ms/step - loss: 0.4486 - accuracy: 0.7814\n",
      "Epoch 55/100\n",
      "140/140 [==============================] - 0s 1ms/step - loss: 0.4473 - accuracy: 0.7829\n",
      "Epoch 56/100\n",
      "140/140 [==============================] - 0s 1ms/step - loss: 0.4498 - accuracy: 0.7857\n",
      "Epoch 57/100\n",
      "140/140 [==============================] - 0s 1ms/step - loss: 0.4494 - accuracy: 0.7743\n",
      "Epoch 58/100\n",
      "140/140 [==============================] - 0s 2ms/step - loss: 0.4484 - accuracy: 0.7786\n",
      "Epoch 59/100\n",
      "140/140 [==============================] - 0s 2ms/step - loss: 0.4431 - accuracy: 0.7793\n",
      "Epoch 60/100\n",
      "140/140 [==============================] - 0s 1ms/step - loss: 0.4433 - accuracy: 0.7950\n",
      "Epoch 61/100\n",
      "140/140 [==============================] - 0s 1ms/step - loss: 0.4442 - accuracy: 0.7821\n",
      "Epoch 62/100\n",
      "140/140 [==============================] - 0s 1ms/step - loss: 0.4469 - accuracy: 0.7714\n",
      "Epoch 63/100\n",
      "140/140 [==============================] - 0s 1ms/step - loss: 0.4430 - accuracy: 0.7800\n",
      "Epoch 64/100\n",
      "140/140 [==============================] - 0s 1ms/step - loss: 0.4426 - accuracy: 0.7807\n",
      "Epoch 65/100\n",
      "140/140 [==============================] - 0s 1ms/step - loss: 0.4419 - accuracy: 0.7871\n",
      "Epoch 66/100\n",
      "140/140 [==============================] - 0s 1ms/step - loss: 0.4428 - accuracy: 0.7786\n",
      "Epoch 67/100\n",
      "140/140 [==============================] - 0s 1ms/step - loss: 0.4410 - accuracy: 0.7836\n",
      "Epoch 68/100\n",
      "140/140 [==============================] - 0s 2ms/step - loss: 0.4434 - accuracy: 0.7779\n",
      "Epoch 69/100\n",
      "140/140 [==============================] - 0s 1ms/step - loss: 0.4421 - accuracy: 0.7807\n",
      "Epoch 70/100\n",
      "140/140 [==============================] - 0s 1ms/step - loss: 0.4419 - accuracy: 0.7786\n",
      "Epoch 71/100\n",
      "140/140 [==============================] - 0s 1ms/step - loss: 0.4402 - accuracy: 0.7836\n",
      "Epoch 72/100\n",
      "140/140 [==============================] - 0s 1ms/step - loss: 0.4389 - accuracy: 0.7850\n",
      "Epoch 73/100\n",
      "140/140 [==============================] - 0s 1ms/step - loss: 0.4398 - accuracy: 0.7793\n",
      "Epoch 74/100\n",
      "140/140 [==============================] - 0s 1ms/step - loss: 0.4405 - accuracy: 0.7814\n",
      "Epoch 75/100\n",
      "140/140 [==============================] - 0s 1ms/step - loss: 0.4407 - accuracy: 0.7850\n",
      "Epoch 76/100\n",
      "140/140 [==============================] - 0s 1ms/step - loss: 0.4409 - accuracy: 0.7829\n",
      "Epoch 77/100\n",
      "140/140 [==============================] - 0s 1ms/step - loss: 0.4360 - accuracy: 0.7864\n",
      "Epoch 78/100\n",
      "140/140 [==============================] - 0s 1ms/step - loss: 0.4379 - accuracy: 0.7936\n",
      "Epoch 79/100\n",
      "140/140 [==============================] - 0s 1ms/step - loss: 0.4397 - accuracy: 0.7829\n",
      "Epoch 80/100\n",
      "140/140 [==============================] - 0s 1ms/step - loss: 0.4344 - accuracy: 0.7971\n",
      "Epoch 81/100\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "140/140 [==============================] - 0s 1ms/step - loss: 0.4356 - accuracy: 0.7779\n",
      "Epoch 82/100\n",
      "140/140 [==============================] - 0s 1ms/step - loss: 0.4354 - accuracy: 0.7821\n",
      "Epoch 83/100\n",
      "140/140 [==============================] - 0s 1ms/step - loss: 0.4364 - accuracy: 0.7821\n",
      "Epoch 84/100\n",
      "140/140 [==============================] - 0s 1ms/step - loss: 0.4335 - accuracy: 0.7864\n",
      "Epoch 85/100\n",
      "140/140 [==============================] - 0s 1ms/step - loss: 0.4338 - accuracy: 0.7857\n",
      "Epoch 86/100\n",
      "140/140 [==============================] - 0s 1ms/step - loss: 0.4350 - accuracy: 0.7879\n",
      "Epoch 87/100\n",
      "140/140 [==============================] - 0s 1ms/step - loss: 0.4320 - accuracy: 0.7857\n",
      "Epoch 88/100\n",
      "140/140 [==============================] - 0s 1ms/step - loss: 0.4322 - accuracy: 0.7871\n",
      "Epoch 89/100\n",
      "140/140 [==============================] - 0s 1ms/step - loss: 0.4334 - accuracy: 0.7900\n",
      "Epoch 90/100\n",
      "140/140 [==============================] - 0s 1ms/step - loss: 0.4314 - accuracy: 0.7900\n",
      "Epoch 91/100\n",
      "140/140 [==============================] - 0s 1ms/step - loss: 0.4313 - accuracy: 0.7871\n",
      "Epoch 92/100\n",
      "140/140 [==============================] - 0s 1ms/step - loss: 0.4303 - accuracy: 0.7914\n",
      "Epoch 93/100\n",
      "140/140 [==============================] - 0s 1ms/step - loss: 0.4321 - accuracy: 0.7879\n",
      "Epoch 94/100\n",
      "140/140 [==============================] - 0s 1ms/step - loss: 0.4330 - accuracy: 0.7907\n",
      "Epoch 95/100\n",
      "140/140 [==============================] - 0s 1ms/step - loss: 0.4317 - accuracy: 0.7871\n",
      "Epoch 96/100\n",
      "140/140 [==============================] - 0s 1ms/step - loss: 0.4282 - accuracy: 0.7879\n",
      "Epoch 97/100\n",
      "140/140 [==============================] - 0s 1ms/step - loss: 0.4304 - accuracy: 0.7821\n",
      "Epoch 98/100\n",
      "140/140 [==============================] - 0s 1ms/step - loss: 0.4274 - accuracy: 0.7914\n",
      "Epoch 99/100\n",
      "140/140 [==============================] - 0s 1ms/step - loss: 0.4283 - accuracy: 0.7900\n",
      "Epoch 100/100\n",
      "140/140 [==============================] - 0s 1ms/step - loss: 0.4298 - accuracy: 0.7886\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<tensorflow.python.keras.callbacks.History at 0x14ed6b110>"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "neural_network.fit(X_train_norm, y_train_encoded, epochs=100, batch_size=10)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now, we can test the accurracy achieved using the test dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-10-02T18:31:18.970063Z",
     "iopub.status.busy": "2021-10-02T18:31:18.969747Z",
     "iopub.status.idle": "2021-10-02T18:31:19.097246Z",
     "shell.execute_reply": "2021-10-02T18:31:19.096172Z",
     "shell.execute_reply.started": "2021-10-02T18:31:18.970032Z"
    }
   },
   "outputs": [],
   "source": [
    "X_test_norm = pd.DataFrame(scaler.transform(X_test))\n",
    "predict = neural_network.predict(X_test_norm)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-10-02T18:31:19.098855Z",
     "iopub.status.busy": "2021-10-02T18:31:19.098575Z",
     "iopub.status.idle": "2021-10-02T18:31:19.106233Z",
     "shell.execute_reply": "2021-10-02T18:31:19.105243Z",
     "shell.execute_reply.started": "2021-10-02T18:31:19.098808Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[3.3870909e-01, 6.6129088e-01],\n",
       "       [9.9937373e-01, 6.2633603e-04],\n",
       "       [2.1825982e-02, 9.7817403e-01],\n",
       "       ...,\n",
       "       [7.2536439e-01, 2.7463558e-01],\n",
       "       [1.3363432e-02, 9.8663658e-01],\n",
       "       [9.3858087e-01, 6.1419114e-02]], dtype=float32)"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "predict"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "y_pred will be contain the classes predict"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-10-02T18:31:19.107828Z",
     "iopub.status.busy": "2021-10-02T18:31:19.107529Z",
     "iopub.status.idle": "2021-10-02T18:31:19.119006Z",
     "shell.execute_reply": "2021-10-02T18:31:19.117806Z",
     "shell.execute_reply.started": "2021-10-02T18:31:19.107800Z"
    }
   },
   "outputs": [],
   "source": [
    "y_pred = [np.argmax(x) for x in predict]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-10-02T18:31:19.120601Z",
     "iopub.status.busy": "2021-10-02T18:31:19.120370Z",
     "iopub.status.idle": "2021-10-02T18:31:19.129901Z",
     "shell.execute_reply": "2021-10-02T18:31:19.129157Z",
     "shell.execute_reply.started": "2021-10-02T18:31:19.120575Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[1, 0, 1, 1, 1]"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_pred[:5]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Measuring the accuracy score"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-10-02T18:31:19.132014Z",
     "iopub.status.busy": "2021-10-02T18:31:19.131137Z",
     "iopub.status.idle": "2021-10-02T18:31:19.145917Z",
     "shell.execute_reply": "2021-10-02T18:31:19.144884Z",
     "shell.execute_reply.started": "2021-10-02T18:31:19.131976Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.7916666666666666"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.metrics import accuracy_score\n",
    "accuracy_score(y_test, y_pred)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### This means that the neural network classifies the cases with 79% of accuracy 😃"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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