experiments.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import csv\n",
    "import pandas as pd\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.preprocessing import StandardScaler, LabelEncoder, OneHotEncoder\n",
    "import pickle"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "    }\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": [
    "data= pd.read_csv(\"Churn_Modelling.csv\")\n",
    "data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "### Drop irrelevant columns\n",
    "data=data.drop(['RowNumber','CustomerId','Surname'],axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "## Preprocess the data\n",
    "\n",
    "\n",
    "\n",
    "label_encoder_gender=LabelEncoder()\n",
    "data['Gender']=label_encoder_gender.fit_transform(data['Gender'])\n",
    "\n",
    "onehot_encoder_geo=OneHotEncoder()\n",
    "geo_encoder=onehot_encoder_geo.fit_transform(data[['Geography']]).toarray()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
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       "    }\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>CreditScore</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",
       "      <th>Geography_France</th>\n",
       "      <th>Geography_Germany</th>\n",
       "      <th>Geography_Spain</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>619</td>\n",
       "      <td>0</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",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>608</td>\n",
       "      <td>0</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",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>502</td>\n",
       "      <td>0</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",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>699</td>\n",
       "      <td>0</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",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>850</td>\n",
       "      <td>0</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",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   CreditScore  Gender  Age  Tenure    Balance  NumOfProducts  HasCrCard  \\\n",
       "0          619       0   42       2       0.00              1          1   \n",
       "1          608       0   41       1   83807.86              1          0   \n",
       "2          502       0   42       8  159660.80              3          1   \n",
       "3          699       0   39       1       0.00              2          0   \n",
       "4          850       0   43       2  125510.82              1          1   \n",
       "\n",
       "   IsActiveMember  EstimatedSalary  Exited  Geography_France  \\\n",
       "0               1        101348.88       1               1.0   \n",
       "1               1        112542.58       0               0.0   \n",
       "2               0        113931.57       1               1.0   \n",
       "3               0         93826.63       0               1.0   \n",
       "4               1         79084.10       0               0.0   \n",
       "\n",
       "   Geography_Germany  Geography_Spain  \n",
       "0                0.0              0.0  \n",
       "1                0.0              1.0  \n",
       "2                0.0              0.0  \n",
       "3                0.0              0.0  \n",
       "4                0.0              1.0  "
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "geo_encoded_df=pd.DataFrame(geo_encoder,columns=onehot_encoder_geo.get_feature_names_out(['Geography']))\n",
    "data=pd.concat([data.drop('Geography',axis=1),geo_encoded_df],axis=1)\n",
    "data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "#\n",
    "\n",
    "with open('label_encoder_gender.pkl','wb') as file:\n",
    "    pickle.dump(label_encoder_gender,file)\n",
    "\n",
    "with open('onehot_encoder_geo.pkl','wb') as file:\n",
    "    pickle.dump(onehot_encoder_geo,file)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "## DiVide the dataset into indepent and dependent features\n",
    "X=data.drop('Exited',axis=1)\n",
    "y=data['Exited']\n",
    "\n",
    "## Split the data in training and tetsing sets\n",
    "X_train,X_test,y_train,y_test=train_test_split(X,y,test_size=0.2,random_state=42)\n",
    "\n",
    "## Scale these features\n",
    "scaler=StandardScaler()\n",
    "X_train=scaler.fit_transform(X_train)\n",
    "X_test=scaler.transform(X_test)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "with open('scaler.pkl','wb') as file:\n",
    "    pickle.dump(scaler,file)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "WARNING:tensorflow:From e:\\projects\\Churn classification\\venv\\Lib\\site-packages\\keras\\src\\losses.py:2976: The name tf.losses.sparse_softmax_cross_entropy is deprecated. Please use tf.compat.v1.losses.sparse_softmax_cross_entropy instead.\n",
      "\n"
     ]
    }
   ],
   "source": [
    "import tensorflow as tf\n",
    "from tensorflow.keras.models import Sequential\n",
    "from tensorflow.keras.layers import Dense\n",
    "from tensorflow.keras.callbacks import EarlyStopping,TensorBoard\n",
    "import datetime"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "WARNING:tensorflow:From e:\\projects\\Churn classification\\venv\\Lib\\site-packages\\keras\\src\\backend.py:873: The name tf.get_default_graph is deprecated. Please use tf.compat.v1.get_default_graph instead.\n",
      "\n",
      "Model: \"sequential\"\n",
      "_________________________________________________________________\n",
      " Layer (type)                Output Shape              Param #   \n",
      "=================================================================\n",
      " dense (Dense)               (None, 64)                832       \n",
      "                                                                 \n",
      " dense_1 (Dense)             (None, 32)                2080      \n",
      "                                                                 \n",
      " dense_2 (Dense)             (None, 1)                 33        \n",
      "                                                                 \n",
      "=================================================================\n",
      "Total params: 2945 (11.50 KB)\n",
      "Trainable params: 2945 (11.50 KB)\n",
      "Non-trainable params: 0 (0.00 Byte)\n",
      "_________________________________________________________________\n"
     ]
    }
   ],
   "source": [
    "## Build Our ANN Model\n",
    "model=Sequential([\n",
    "    Dense(64,activation='relu',input_shape=(X_train.shape[1],)), ## HL1 Connected wwith input layer\n",
    "    Dense(32,activation='relu'), ## HL2\n",
    "    Dense(1,activation='sigmoid')  ## output layer\n",
    "]\n",
    "\n",
    ")\n",
    "model.summary()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<keras.src.losses.BinaryCrossentropy at 0x27b72615f90>"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import tensorflow\n",
    "opt=tensorflow.keras.optimizers.Adam(learning_rate=0.01)\n",
    "loss=tensorflow.keras.losses.BinaryCrossentropy()\n",
    "loss"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "## compile the model\n",
    "model.compile(optimizer=opt,loss=\"binary_crossentropy\",metrics=['accuracy'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "## Set up the Tensorboard\n",
    "from tensorflow.keras.callbacks import EarlyStopping,TensorBoard\n",
    "\n",
    "log_dir=\"logs/fit/\" + datetime.datetime.now().strftime(\"%Y%m%d-%H%M%S\")\n",
    "tensorflow_callback=TensorBoard(log_dir=log_dir,histogram_freq=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "## Set up Early Stopping\n",
    "early_stopping_callback=EarlyStopping(monitor='val_loss',patience=10,restore_best_weights=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/100\n",
      "WARNING:tensorflow:From e:\\projects\\Churn classification\\venv\\Lib\\site-packages\\keras\\src\\utils\\tf_utils.py:492: The name tf.ragged.RaggedTensorValue is deprecated. Please use tf.compat.v1.ragged.RaggedTensorValue instead.\n",
      "\n",
      "WARNING:tensorflow:From e:\\projects\\Churn classification\\venv\\Lib\\site-packages\\keras\\src\\engine\\base_layer_utils.py:384: The name tf.executing_eagerly_outside_functions is deprecated. Please use tf.compat.v1.executing_eagerly_outside_functions instead.\n",
      "\n",
      "250/250 [==============================] - 4s 8ms/step - loss: 0.4041 - accuracy: 0.8292 - val_loss: 0.3651 - val_accuracy: 0.8590\n",
      "Epoch 2/100\n",
      "250/250 [==============================] - 1s 5ms/step - loss: 0.3546 - accuracy: 0.8541 - val_loss: 0.3430 - val_accuracy: 0.8575\n",
      "Epoch 3/100\n",
      "250/250 [==============================] - 1s 5ms/step - loss: 0.3480 - accuracy: 0.8555 - val_loss: 0.3486 - val_accuracy: 0.8590\n",
      "Epoch 4/100\n",
      "250/250 [==============================] - 1s 5ms/step - loss: 0.3414 - accuracy: 0.8577 - val_loss: 0.3392 - val_accuracy: 0.8580\n",
      "Epoch 5/100\n",
      "250/250 [==============================] - 1s 5ms/step - loss: 0.3406 - accuracy: 0.8624 - val_loss: 0.3427 - val_accuracy: 0.8580\n",
      "Epoch 6/100\n",
      "250/250 [==============================] - 1s 5ms/step - loss: 0.3382 - accuracy: 0.8624 - val_loss: 0.3431 - val_accuracy: 0.8600\n",
      "Epoch 7/100\n",
      "250/250 [==============================] - 1s 5ms/step - loss: 0.3350 - accuracy: 0.8627 - val_loss: 0.3440 - val_accuracy: 0.8570\n",
      "Epoch 8/100\n",
      "250/250 [==============================] - 1s 5ms/step - loss: 0.3371 - accuracy: 0.8608 - val_loss: 0.3408 - val_accuracy: 0.8575\n",
      "Epoch 9/100\n",
      "250/250 [==============================] - 1s 5ms/step - loss: 0.3328 - accuracy: 0.8658 - val_loss: 0.3417 - val_accuracy: 0.8590\n",
      "Epoch 10/100\n",
      "250/250 [==============================] - 1s 5ms/step - loss: 0.3309 - accuracy: 0.8625 - val_loss: 0.3514 - val_accuracy: 0.8500\n",
      "Epoch 11/100\n",
      "250/250 [==============================] - 1s 5ms/step - loss: 0.3291 - accuracy: 0.8669 - val_loss: 0.3488 - val_accuracy: 0.8550\n",
      "Epoch 12/100\n",
      "250/250 [==============================] - 1s 5ms/step - loss: 0.3247 - accuracy: 0.8670 - val_loss: 0.3451 - val_accuracy: 0.8525\n",
      "Epoch 13/100\n",
      "250/250 [==============================] - 1s 5ms/step - loss: 0.3262 - accuracy: 0.8660 - val_loss: 0.3529 - val_accuracy: 0.8610\n",
      "Epoch 14/100\n",
      "250/250 [==============================] - 1s 5ms/step - loss: 0.3244 - accuracy: 0.8662 - val_loss: 0.3607 - val_accuracy: 0.8600\n"
     ]
    }
   ],
   "source": [
    "### Train the model\n",
    "history=model.fit(\n",
    "    X_train,y_train,validation_data=(X_test,y_test),epochs=100,\n",
    "    callbacks=[tensorflow_callback,early_stopping_callback]\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "e:\\projects\\Churn classification\\venv\\Lib\\site-packages\\keras\\src\\engine\\training.py:3103: UserWarning: You are saving your model as an HDF5 file via `model.save()`. This file format is considered legacy. We recommend using instead the native Keras format, e.g. `model.save('my_model.keras')`.\n",
      "  saving_api.save_model(\n"
     ]
    }
   ],
   "source": [
    "model.save('model.h5')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "## Load Tensorboard Extension\n",
    "%load_ext tensorboard"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "\n",
       "      <iframe id=\"tensorboard-frame-6658cf6876f11679\" width=\"100%\" height=\"800\" frameborder=\"0\">\n",
       "      </iframe>\n",
       "      <script>\n",
       "        (function() {\n",
       "          const frame = document.getElementById(\"tensorboard-frame-6658cf6876f11679\");\n",
       "          const url = new URL(\"/\", window.location);\n",
       "          const port = 6006;\n",
       "          if (port) {\n",
       "            url.port = port;\n",
       "          }\n",
       "          frame.src = url;\n",
       "        })();\n",
       "      </script>\n",
       "    "
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "%tensorboard --logdir logs/fit"
   ]
  }
 ],
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   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
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