ChurnModelling(ANN, AdaptedValueCounts).ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import seaborn as sns\n",
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "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>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",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>15634602</td>\n",
       "      <td>Hargrave</td>\n",
       "      <td>619</td>\n",
       "      <td>France</td>\n",
       "      <td>Female</td>\n",
       "      <td>42</td>\n",
       "      <td>2</td>\n",
       "      <td>0.00</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>101348.88</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>15647311</td>\n",
       "      <td>Hill</td>\n",
       "      <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",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>112542.58</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>15619304</td>\n",
       "      <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",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>113931.57</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>15701354</td>\n",
       "      <td>Boni</td>\n",
       "      <td>699</td>\n",
       "      <td>France</td>\n",
       "      <td>Female</td>\n",
       "      <td>39</td>\n",
       "      <td>1</td>\n",
       "      <td>0.00</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>93826.63</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <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",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   RowNumber  CustomerId   Surname  CreditScore Geography  Gender  Age  \\\n",
       "0          1    15634602  Hargrave          619    France  Female   42   \n",
       "1          2    15647311      Hill          608     Spain  Female   41   \n",
       "2          3    15619304      Onio          502    France  Female   42   \n",
       "3          4    15701354      Boni          699    France  Female   39   \n",
       "4          5    15737888  Mitchell          850     Spain  Female   43   \n",
       "\n",
       "   Tenure    Balance  NumOfProducts  HasCrCard  IsActiveMember  \\\n",
       "0       2       0.00              1          1               1   \n",
       "1       1   83807.86              1          0               1   \n",
       "2       8  159660.80              3          1               0   \n",
       "3       1       0.00              2          0               0   \n",
       "4       2  125510.82              1          1               1   \n",
       "\n",
       "   EstimatedSalary  Exited  \n",
       "0        101348.88       1  \n",
       "1        112542.58       0  \n",
       "2        113931.57       1  \n",
       "3         93826.63       0  \n",
       "4         79084.10       0  "
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "path = 'C:\\\\Users\\\\Ufuk Altan\\\\Downloads\\\\Downloads\\\\Data Science\\\\Machine Learning A-Z\\\\Part 8 - Deep Learning\\\\Section 39 - Artificial Neural Networks (ANN)\\\\Churn_Modelling.csv'\n",
    "df = pd.read_csv(path)\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    7963\n",
       "1    2037\n",
       "Name: Exited, dtype: int64"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['Exited'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.argmin(df['Exited'].value_counts())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.7963"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "7963 / (7963+2037)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "def balance_classes(dataframe, var):\n",
    "    \n",
    "    '''\n",
    "    Apply when the dependent variable is in numeric form and is binary. \n",
    "    '''\n",
    "    \n",
    "    zero = dataframe[dataframe[var] == 0]\n",
    "    one = dataframe[dataframe[var] == 1]\n",
    "    \n",
    "    shape_zero = zero.shape[0]\n",
    "    shape_one = one.shape[0]\n",
    "    \n",
    "    if shape_zero > shape_one:\n",
    "        zero = zero.sample(one.shape[0])\n",
    "        df = zero.append(one,ignore_index=True)\n",
    "\n",
    "    else:\n",
    "        one = one.sample(zero.shape[0])\n",
    "        df = one.append(zero,ignore_index=True)\n",
    "                \n",
    "    return df\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1    2037\n",
       "0    2037\n",
       "Name: Exited, dtype: int64"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = balance_classes(df, 'Exited')\n",
    "df['Exited'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['RowNumber', 'CustomerId', 'Surname', 'CreditScore', 'Geography',\n",
       "       'Gender', 'Age', 'Tenure', 'Balance', 'NumOfProducts', 'HasCrCard',\n",
       "       'IsActiveMember', 'EstimatedSalary', 'Exited'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "def data_types(data):\n",
    "    for i in data.columns:\n",
    "        print(i + '\\t\\t\\t' + str(data[i][0]) + '\\t\\t\\t' + str(type(data[i][0])))\n",
    "        print('\\n')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "collapsed": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "RowNumber\t\t\t1\t\t\t<class 'numpy.int64'>\n",
      "\n",
      "\n",
      "CustomerId\t\t\t15634602\t\t\t<class 'numpy.int64'>\n",
      "\n",
      "\n",
      "Surname\t\t\tHargrave\t\t\t<class 'str'>\n",
      "\n",
      "\n",
      "CreditScore\t\t\t619\t\t\t<class 'numpy.int64'>\n",
      "\n",
      "\n",
      "Geography\t\t\tFrance\t\t\t<class 'str'>\n",
      "\n",
      "\n",
      "Gender\t\t\tFemale\t\t\t<class 'str'>\n",
      "\n",
      "\n",
      "Age\t\t\t42\t\t\t<class 'numpy.int64'>\n",
      "\n",
      "\n",
      "Tenure\t\t\t2\t\t\t<class 'numpy.int64'>\n",
      "\n",
      "\n",
      "Balance\t\t\t0.0\t\t\t<class 'numpy.float64'>\n",
      "\n",
      "\n",
      "NumOfProducts\t\t\t1\t\t\t<class 'numpy.int64'>\n",
      "\n",
      "\n",
      "HasCrCard\t\t\t1\t\t\t<class 'numpy.int64'>\n",
      "\n",
      "\n",
      "IsActiveMember\t\t\t1\t\t\t<class 'numpy.int64'>\n",
      "\n",
      "\n",
      "EstimatedSalary\t\t\t101348.88\t\t\t<class 'numpy.float64'>\n",
      "\n",
      "\n",
      "Exited\t\t\t1\t\t\t<class 'numpy.int64'>\n",
      "\n",
      "\n"
     ]
    }
   ],
   "source": [
    "data_types(df)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='Exited', ylabel='EstimatedSalary'>"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1440x1440 with 5 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(20,20))\n",
    "\n",
    "plt.subplot(2,2,1)\n",
    "sns.countplot(data = df, x ='Gender', hue ='Exited' )\n",
    "\n",
    "plt.subplot(2,2,2)\n",
    "sns.boxplot(data = df, x ='Gender', y = 'EstimatedSalary' )\n",
    "\n",
    "plt.subplot(2,2,3)\n",
    "sns.heatmap(df.corr(), annot=True, cmap ='coolwarm')\n",
    "\n",
    "plt.subplot(2,2,4)\n",
    "sns.boxplot(data = df, x ='Exited', y = 'EstimatedSalary' )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "cols = ['CreditScore', 'Geography',\n",
    "       'Gender', 'Age', 'Tenure', 'Balance', 'NumOfProducts', 'HasCrCard',\n",
    "       'IsActiveMember', 'EstimatedSalary', 'Exited']\n",
    "\n",
    "df = df[cols]\n",
    "data = pd.get_dummies(df, drop_first=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "X = data.drop('Exited', axis = 1)\n",
    "y = data['Exited']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.model_selection import train_test_split\n",
    "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size = 0.20, random_state = 0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.preprocessing import StandardScaler\n",
    "sc = StandardScaler()\n",
    "X_train = sc.fit_transform(X_train)\n",
    "X_test = sc.transform(X_test)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Principal Component Analysis"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0.56434072, 0.43565928])"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.decomposition import PCA\n",
    "pca = PCA(n_components=2)\n",
    "X_train = pca.fit_transform(X_train)\n",
    "X_test = pca.transform(X_test)\n",
    "explained_variance = pca.explained_variance_ratio_\n",
    "explained_variance"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Random Forest"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "RandomForestClassifier(criterion='entropy', n_estimators=15, random_state=0)"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.ensemble import RandomForestClassifier\n",
    "classifier =RandomForestClassifier(n_estimators=15, criterion='entropy', random_state=0)\n",
    "classifier.fit(X_train,y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [],
   "source": [
    "def return_feature_importances(model):\n",
    "\n",
    "    importances = model.feature_importances_\n",
    "    features = pd.DataFrame({'Features':X.columns, 'Importances': importances})\n",
    "    features = features.sort_values('Importances', ascending = False)\n",
    "    \n",
    "    plt.figure(figsize = (16,8))\n",
    "    sns.barplot(x= 'Features', y ='Importances', data = features)\n",
    "    plt.xticks(rotation = 90)\n",
    "    \n",
    "    return features"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1152x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "features = return_feature_importances(classifier)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1.0"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "features['Importances'].sum()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Deep Learning"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [],
   "source": [
    "from tensorflow.keras.models import Sequential\n",
    "from tensorflow.keras.layers import  Dense, Activation, Flatten, Dropout"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [],
   "source": [
    "model = Sequential()\n",
    "\n",
    "model.add(Dense(8, input_shape = X_train.shape[1:]))\n",
    "model.add(Activation('relu'))\n",
    "\n",
    "model.add(Dense(8))\n",
    "model.add(Activation('relu'))\n",
    "\n",
    "model.add(Dense(12))\n",
    "model.add(Activation('relu'))\n",
    "\n",
    "model.add(Dense(1))\n",
    "model.add(Activation('sigmoid'))\n",
    "\n",
    "model.compile(loss = 'binary_crossentropy', optimizer='rmsprop', metrics = ['accuracy'])\n",
    "y_train = np.array(y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [],
   "source": [
    "from tensorflow.keras.callbacks import EarlyStopping\n",
    "early_stop = EarlyStopping(monitor = 'val_accuracy', mode ='max', verbose= 1, patience=5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Train on 2933 samples, validate on 326 samples\n",
      "Epoch 1/45\n",
      "WARNING:tensorflow:From C:\\Anaconda3\\lib\\site-packages\\tensorflow_core\\python\\ops\\nn_impl.py:183: where (from tensorflow.python.ops.array_ops) is deprecated and will be removed in a future version.\n",
      "Instructions for updating:\n",
      "Use tf.where in 2.0, which has the same broadcast rule as np.where\n",
      "WARNING:tensorflow:Entity <function Function._initialize_uninitialized_variables.<locals>.initialize_variables at 0x000002F9D62B5400> could not be transformed and will be executed as-is. Please report this to the AutoGraph team. When filing the bug, set the verbosity to 10 (on Linux, `export AUTOGRAPH_VERBOSITY=10`) and attach the full output. Cause: module 'gast' has no attribute 'Num'\n",
      "WARNING: Entity <function Function._initialize_uninitialized_variables.<locals>.initialize_variables at 0x000002F9D62B5400> could not be transformed and will be executed as-is. Please report this to the AutoGraph team. When filing the bug, set the verbosity to 10 (on Linux, `export AUTOGRAPH_VERBOSITY=10`) and attach the full output. Cause: module 'gast' has no attribute 'Num'\n",
      "2933/2933 [==============================] - 2s 532us/sample - loss: 0.6827 - accuracy: 0.5769 - val_loss: 0.6879 - val_accuracy: 0.5307\n",
      "Epoch 2/45\n",
      "2933/2933 [==============================] - 0s 80us/sample - loss: 0.6603 - accuracy: 0.6089 - val_loss: 0.6762 - val_accuracy: 0.5429\n",
      "Epoch 3/45\n",
      "2933/2933 [==============================] - 0s 78us/sample - loss: 0.6351 - accuracy: 0.6393 - val_loss: 0.6669 - val_accuracy: 0.5583\n",
      "Epoch 4/45\n",
      "2933/2933 [==============================] - 0s 95us/sample - loss: 0.6145 - accuracy: 0.6570 - val_loss: 0.6635 - val_accuracy: 0.5798\n",
      "Epoch 5/45\n",
      "2933/2933 [==============================] - 0s 94us/sample - loss: 0.5987 - accuracy: 0.6758 - val_loss: 0.6599 - val_accuracy: 0.5828\n",
      "Epoch 6/45\n",
      "2933/2933 [==============================] - 0s 95us/sample - loss: 0.5863 - accuracy: 0.6884 - val_loss: 0.6573 - val_accuracy: 0.6012\n",
      "Epoch 7/45\n",
      "2933/2933 [==============================] - 0s 69us/sample - loss: 0.5762 - accuracy: 0.7047 - val_loss: 0.6506 - val_accuracy: 0.6258\n",
      "Epoch 8/45\n",
      "2933/2933 [==============================] - 0s 75us/sample - loss: 0.5678 - accuracy: 0.7058 - val_loss: 0.6448 - val_accuracy: 0.6442\n",
      "Epoch 9/45\n",
      "2933/2933 [==============================] - 0s 97us/sample - loss: 0.5608 - accuracy: 0.7064 - val_loss: 0.6403 - val_accuracy: 0.6380\n",
      "Epoch 10/45\n",
      "2933/2933 [==============================] - 0s 94us/sample - loss: 0.5544 - accuracy: 0.7218 - val_loss: 0.6319 - val_accuracy: 0.6656\n",
      "Epoch 11/45\n",
      "2933/2933 [==============================] - 0s 76us/sample - loss: 0.5480 - accuracy: 0.7245 - val_loss: 0.6279 - val_accuracy: 0.6718\n",
      "Epoch 12/45\n",
      "2933/2933 [==============================] - 0s 74us/sample - loss: 0.5423 - accuracy: 0.7324 - val_loss: 0.6219 - val_accuracy: 0.6718\n",
      "Epoch 13/45\n",
      "2933/2933 [==============================] - 0s 78us/sample - loss: 0.5367 - accuracy: 0.7402 - val_loss: 0.6137 - val_accuracy: 0.6871\n",
      "Epoch 14/45\n",
      "2933/2933 [==============================] - 0s 90us/sample - loss: 0.5316 - accuracy: 0.7470 - val_loss: 0.6068 - val_accuracy: 0.6933\n",
      "Epoch 15/45\n",
      "2933/2933 [==============================] - 0s 72us/sample - loss: 0.5260 - accuracy: 0.7480 - val_loss: 0.6005 - val_accuracy: 0.6933\n",
      "Epoch 16/45\n",
      "2933/2933 [==============================] - 0s 76us/sample - loss: 0.5203 - accuracy: 0.7552 - val_loss: 0.5930 - val_accuracy: 0.7025\n",
      "Epoch 17/45\n",
      "2933/2933 [==============================] - 0s 69us/sample - loss: 0.5150 - accuracy: 0.7569 - val_loss: 0.5855 - val_accuracy: 0.7055\n",
      "Epoch 18/45\n",
      "2933/2933 [==============================] - 0s 73us/sample - loss: 0.5093 - accuracy: 0.7624 - val_loss: 0.5788 - val_accuracy: 0.7117\n",
      "Epoch 19/45\n",
      "2933/2933 [==============================] - 0s 75us/sample - loss: 0.5035 - accuracy: 0.7600 - val_loss: 0.5728 - val_accuracy: 0.7209\n",
      "Epoch 20/45\n",
      "2933/2933 [==============================] - 0s 87us/sample - loss: 0.4993 - accuracy: 0.7624 - val_loss: 0.5648 - val_accuracy: 0.7270\n",
      "Epoch 21/45\n",
      "2933/2933 [==============================] - 0s 72us/sample - loss: 0.4935 - accuracy: 0.7647 - val_loss: 0.5617 - val_accuracy: 0.7301\n",
      "Epoch 22/45\n",
      "2933/2933 [==============================] - 0s 73us/sample - loss: 0.4903 - accuracy: 0.7634 - val_loss: 0.5518 - val_accuracy: 0.7454\n",
      "Epoch 23/45\n",
      "2933/2933 [==============================] - 0s 70us/sample - loss: 0.4857 - accuracy: 0.7654 - val_loss: 0.5448 - val_accuracy: 0.7515\n",
      "Epoch 24/45\n",
      "2933/2933 [==============================] - 0s 76us/sample - loss: 0.4822 - accuracy: 0.7668 - val_loss: 0.5396 - val_accuracy: 0.7546\n",
      "Epoch 25/45\n",
      "2933/2933 [==============================] - 0s 94us/sample - loss: 0.4790 - accuracy: 0.7682 - val_loss: 0.5322 - val_accuracy: 0.7515\n",
      "Epoch 26/45\n",
      "2933/2933 [==============================] - 0s 74us/sample - loss: 0.4757 - accuracy: 0.7719 - val_loss: 0.5292 - val_accuracy: 0.7362\n",
      "Epoch 27/45\n",
      "2933/2933 [==============================] - 0s 67us/sample - loss: 0.4731 - accuracy: 0.7709 - val_loss: 0.5224 - val_accuracy: 0.7454\n",
      "Epoch 28/45\n",
      "2933/2933 [==============================] - 0s 73us/sample - loss: 0.4706 - accuracy: 0.7716 - val_loss: 0.5207 - val_accuracy: 0.7485\n",
      "Epoch 29/45\n",
      "2933/2933 [==============================] - 0s 68us/sample - loss: 0.4687 - accuracy: 0.7729 - val_loss: 0.5197 - val_accuracy: 0.7393\n",
      "Epoch 30/45\n",
      "2933/2933 [==============================] - 0s 106us/sample - loss: 0.4667 - accuracy: 0.7729 - val_loss: 0.5155 - val_accuracy: 0.7393\n",
      "Epoch 31/45\n",
      "2933/2933 [==============================] - 0s 68us/sample - loss: 0.4648 - accuracy: 0.7733 - val_loss: 0.5145 - val_accuracy: 0.7393\n",
      "Epoch 32/45\n",
      "2933/2933 [==============================] - 0s 74us/sample - loss: 0.4637 - accuracy: 0.7740 - val_loss: 0.5117 - val_accuracy: 0.7331\n",
      "Epoch 33/45\n",
      "2933/2933 [==============================] - 0s 80us/sample - loss: 0.4619 - accuracy: 0.7719 - val_loss: 0.5105 - val_accuracy: 0.7454\n",
      "Epoch 34/45\n",
      "2933/2933 [==============================] - 0s 79us/sample - loss: 0.4609 - accuracy: 0.7791 - val_loss: 0.5066 - val_accuracy: 0.7423\n",
      "Epoch 35/45\n",
      "2933/2933 [==============================] - 0s 92us/sample - loss: 0.4599 - accuracy: 0.7753 - val_loss: 0.5064 - val_accuracy: 0.7423\n",
      "Epoch 36/45\n",
      "2933/2933 [==============================] - 0s 71us/sample - loss: 0.4588 - accuracy: 0.7743 - val_loss: 0.5039 - val_accuracy: 0.7331\n",
      "Epoch 37/45\n",
      "2933/2933 [==============================] - 0s 69us/sample - loss: 0.4583 - accuracy: 0.7726 - val_loss: 0.4979 - val_accuracy: 0.7454\n",
      "Epoch 38/45\n",
      "2933/2933 [==============================] - 0s 67us/sample - loss: 0.4575 - accuracy: 0.7767 - val_loss: 0.4984 - val_accuracy: 0.7454\n",
      "Epoch 39/45\n",
      "2933/2933 [==============================] - 0s 79us/sample - loss: 0.4568 - accuracy: 0.7774 - val_loss: 0.4986 - val_accuracy: 0.7485\n",
      "Epoch 40/45\n",
      "2933/2933 [==============================] - 0s 99us/sample - loss: 0.4560 - accuracy: 0.7780 - val_loss: 0.4981 - val_accuracy: 0.7423\n",
      "Epoch 41/45\n",
      "2933/2933 [==============================] - 0s 90us/sample - loss: 0.4553 - accuracy: 0.7801 - val_loss: 0.5001 - val_accuracy: 0.7423\n",
      "Epoch 42/45\n",
      "2933/2933 [==============================] - 0s 80us/sample - loss: 0.4550 - accuracy: 0.7818 - val_loss: 0.4962 - val_accuracy: 0.7423\n",
      "Epoch 43/45\n",
      "2933/2933 [==============================] - 0s 73us/sample - loss: 0.4547 - accuracy: 0.7808 - val_loss: 0.4955 - val_accuracy: 0.7423\n",
      "Epoch 44/45\n",
      "2933/2933 [==============================] - 0s 80us/sample - loss: 0.4537 - accuracy: 0.7811 - val_loss: 0.4960 - val_accuracy: 0.7485\n",
      "Epoch 45/45\n",
      "2933/2933 [==============================] - 0s 155us/sample - loss: 0.4538 - accuracy: 0.7811 - val_loss: 0.4960 - val_accuracy: 0.7393\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<tensorflow.python.keras.callbacks.History at 0x2f9d62915f8>"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model.fit(X_train, y_train, batch_size = 40, validation_split = 0.1, epochs = 45, verbose = 1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 146,
   "metadata": {},
   "outputs": [],
   "source": [
    "y_pred = model.predict(X_test)\n",
    "y_pred = (y_pred>0.5)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Unbalanced Classes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 147,
   "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>loss</th>\n",
       "      <th>accuracy</th>\n",
       "      <th>val_loss</th>\n",
       "      <th>val_accuracy</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.485771</td>\n",
       "      <td>0.794722</td>\n",
       "      <td>0.449929</td>\n",
       "      <td>0.79500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.453292</td>\n",
       "      <td>0.796111</td>\n",
       "      <td>0.436449</td>\n",
       "      <td>0.79500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.440447</td>\n",
       "      <td>0.796111</td>\n",
       "      <td>0.424734</td>\n",
       "      <td>0.79500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.430850</td>\n",
       "      <td>0.796806</td>\n",
       "      <td>0.415528</td>\n",
       "      <td>0.79375</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.424075</td>\n",
       "      <td>0.804306</td>\n",
       "      <td>0.408844</td>\n",
       "      <td>0.80125</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       loss  accuracy  val_loss  val_accuracy\n",
       "0  0.485771  0.794722  0.449929       0.79500\n",
       "1  0.453292  0.796111  0.436449       0.79500\n",
       "2  0.440447  0.796111  0.424734       0.79500\n",
       "3  0.430850  0.796806  0.415528       0.79375\n",
       "4  0.424075  0.804306  0.408844       0.80125"
      ]
     },
     "execution_count": 147,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "metrics = pd.DataFrame(model.history.history)\n",
    "metrics.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 148,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x1fc9c9cee48>]"
      ]
     },
     "execution_count": 148,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 792x792 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(11,11))\n",
    "plt.plot(metrics['loss'], color='r')\n",
    "plt.plot(metrics['val_loss'], color='b')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 149,
   "metadata": {
    "collapsed": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x1fc9ca1d6a0>]"
      ]
     },
     "execution_count": 149,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 792x792 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(11,11))\n",
    "plt.plot(metrics['accuracy'], color='r')\n",
    "plt.plot(metrics['val_accuracy'], color='b')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 150,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.8555"
      ]
     },
     "execution_count": 150,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.metrics import accuracy_score, confusion_matrix, classification_report\n",
    "accuracy_score(y_pred, y_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 151,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "              precision    recall  f1-score   support\n",
      "\n",
      "       False       0.94      0.88      0.91      1702\n",
      "        True       0.51      0.69      0.59       298\n",
      "\n",
      "    accuracy                           0.86      2000\n",
      "   macro avg       0.73      0.79      0.75      2000\n",
      "weighted avg       0.88      0.86      0.86      2000\n",
      "\n"
     ]
    }
   ],
   "source": [
    "print(classification_report(y_pred, y_test))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 152,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1fc9c98fd68>"
      ]
     },
     "execution_count": 152,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x576 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "cm=confusion_matrix(y_pred, y_test)\n",
    "plt.figure(figsize=(10,8))\n",
    "sns.heatmap(cm,annot=True,cmap='Blues')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 153,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[1504,  198],\n",
       "       [  91,  207]], dtype=int64)"
      ]
     },
     "execution_count": 153,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cm"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Balanced Classes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 110,
   "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>loss</th>\n",
       "      <th>accuracy</th>\n",
       "      <th>val_loss</th>\n",
       "      <th>val_accuracy</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.701252</td>\n",
       "      <td>0.467099</td>\n",
       "      <td>0.689500</td>\n",
       "      <td>0.561350</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.683243</td>\n",
       "      <td>0.578929</td>\n",
       "      <td>0.670375</td>\n",
       "      <td>0.641104</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.665198</td>\n",
       "      <td>0.634504</td>\n",
       "      <td>0.647671</td>\n",
       "      <td>0.659509</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.641833</td>\n",
       "      <td>0.656325</td>\n",
       "      <td>0.618854</td>\n",
       "      <td>0.659509</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.615716</td>\n",
       "      <td>0.671667</td>\n",
       "      <td>0.591827</td>\n",
       "      <td>0.687117</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       loss  accuracy  val_loss  val_accuracy\n",
       "0  0.701252  0.467099  0.689500      0.561350\n",
       "1  0.683243  0.578929  0.670375      0.641104\n",
       "2  0.665198  0.634504  0.647671      0.659509\n",
       "3  0.641833  0.656325  0.618854      0.659509\n",
       "4  0.615716  0.671667  0.591827      0.687117"
      ]
     },
     "execution_count": 110,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "metrics = pd.DataFrame(model.history.history)\n",
    "metrics.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 111,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x1fc99969c18>]"
      ]
     },
     "execution_count": 111,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 792x792 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(11,11))\n",
    "plt.plot(metrics['loss'], color='r')\n",
    "plt.plot(metrics['val_loss'], color='b')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 112,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x1fc99c73860>]"
      ]
     },
     "execution_count": 112,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 792x792 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(11,11))\n",
    "plt.plot(metrics['accuracy'], color='r')\n",
    "plt.plot(metrics['val_accuracy'], color='b')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 113,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.7803680981595092"
      ]
     },
     "execution_count": 113,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.metrics import accuracy_score, confusion_matrix, classification_report\n",
    "accuracy_score(y_pred, y_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 114,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "              precision    recall  f1-score   support\n",
      "\n",
      "       False       0.82      0.76      0.79       446\n",
      "        True       0.74      0.80      0.77       369\n",
      "\n",
      "    accuracy                           0.78       815\n",
      "   macro avg       0.78      0.78      0.78       815\n",
      "weighted avg       0.78      0.78      0.78       815\n",
      "\n"
     ]
    }
   ],
   "source": [
    "print(classification_report(y_pred, y_test))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 123,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1fc9a2c3eb8>"
      ]
     },
     "execution_count": 123,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 720x576 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "cm=confusion_matrix(y_pred, y_test)\n",
    "plt.figure(figsize=(10,8))\n",
    "sns.heatmap(cm,annot=True,cmap='Blues')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 116,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[339, 107],\n",
       "       [ 72, 297]], dtype=int64)"
      ]
     },
     "execution_count": 116,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cm"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
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