gyanprakashkushwaha / customer-churn-prediction Goto Github PK

git clone https://github.com/GyanPrakashkushwaha/Customer-Churn-Prediction.git customer-churn-prediction ; cd customer-churn-prediction

virtalenv churnvenv 
churnvenv/Scipts/activate.ps1

pip install -r requirements.txt

python run main.py

streamlit run app.py

mlflow ui

https://dagshub.com/GyanPrakashKushwaha/Customer-Churn-Prediction.mlflow

from keras.layers import BatchNormalization, Dense
from keras.losses import binary_crossentropy
from tensorflow import keras
from keras.callbacks import LearningRateScheduler , EarlyStopping
from keras.activations import relu , sigmoid
from keras import Sequential
from keras.initializers import he_normal

model = Sequential()

model = Sequential()

model.add(layer=Dense(units=512,activation=relu,kernel_initializer=he_normal))
model.add(layer=Dense(units=332,activation=relu,kernel_initializer=he_normal))
model.add(BatchNormalization())
model.add(Dense(units=128,activation=relu,kernel_initializer=he_normal))
model.add(Dense(units=64,activation=relu,kernel_initializer=he_normal))
model.add(Dense(units=1,activation=sigmoid,name='output_layer'))

def lr_schedule(epoch, lr):
    if epoch < 1:
        return lr
    else:
        return lr * np.exp(-0.1)

lr_scheduler = LearningRateScheduler(lr_schedule)

early_stopping = EarlyStopping(
    monitor="accuracy",
    min_delta=0.00001,
    patience=5,
    verbose=1,
    mode="auto",
    baseline=None,
    restore_best_weights=False
)

optimizer = keras.optimizers.RMSprop(learning_rate=0.0005)

model.compile(optimizer=optimizer, 
               loss=binary_crossentropy, 
                 metrics=['accuracy']) 

history = model.fit(X_train, y_train, validation_data=(X_test, y_test), epochs=20,
                    batch_size=64, 
                      callbacks=[lr_scheduler, early_stopping]) 

## output
Epoch 1/20
1256/1256 [==============================] - 9s 5ms/step - loss: 0.7005 - accuracy: 0.5001 - val_loss: 0.7269 - val_accuracy: 0.5018 - lr: 5.0000e-04
Epoch 2/20
1256/1256 [==============================] - 7s 6ms/step - loss: 0.6952 - accuracy: 0.5014 - val_loss: 0.6939 - val_accuracy: 0.5006 - lr: 4.5242e-04
Epoch 3/20
1256/1256 [==============================] - 7s 6ms/step - loss: 0.6945 - accuracy: 0.4992 - val_loss: 0.6992 - val_accuracy: 0.5003 - lr: 4.0937e-04
Epoch 4/20
1256/1256 [==============================] - 7s 5ms/step - loss: 0.6938 - accuracy: 0.5042 - val_loss: 0.6933 - val_accuracy: 0.5040 - lr: 3.7041e-04
Epoch 5/20
1256/1256 [==============================] - 7s 5ms/step - loss: 0.6938 - accuracy: 0.5027 - val_loss: 0.6936 - val_accuracy: 0.5017 - lr: 3.3516e-04
Epoch 6/20
1256/1256 [==============================] - 7s 5ms/step - loss: 0.6935 - accuracy: 0.5010 - val_loss: 0.6947 - val_accuracy: 0.4987 - lr: 3.0327e-04
Epoch 7/20
1256/1256 [==============================] - 6s 5ms/step - loss: 0.6934 - accuracy: 0.5019 - val_loss: 0.6933 - val_accuracy: 0.5001 - lr: 2.7441e-04
Epoch 8/20
1256/1256 [==============================] - 6s 5ms/step - loss: 0.6935 - accuracy: 0.4967 - val_loss: 0.6933 - val_accuracy: 0.4959 - lr: 2.4829e-04
Epoch 9/20
1256/1256 [==============================] - 6s 5ms/step - loss: 0.6933 - accuracy: 0.5012 - val_loss: 0.6932 - val_accuracy: 0.4956 - lr: 2.2466e-04
Epoch 9: early stopping

{'Gradient Boosting Classifier': {'subsample': 0.7,
  'n_estimators': 64,
  'max_features': 'log2',
  'loss': 'exponential',
  'learning_rate': 0.1,
  'criterion': 'friedman_mse'},
 'XGBoost Classifier': {'subsample': 0.6,
  'n_estimators': 64,
  'min_child_weight': 1,
  'max_depth': 7,
  'learning_rate': 0.1},
 'CatBoost Classifier': {'loss_function': 'CrossEntropy',
  'learning_rate': 0.1,
  'iterations': 100,
  'eval_metric': 'Logloss',
  'depth': 8},
 'AdaBoost Classifier': {'n_estimators': 16,
  'learning_rate': 0.01,
  'algorithm': 'SAMME.R'},
 'Random Forest Classifier': {'n_estimators': 256,
  'min_samples_split': 10,
  'min_samples_leaf': 2,
  'max_features': 'sqrt',
  'max_depth': 40,
  'criterion': 'entropy'}}

## output
                    model	        accuracy
0	Gradient Boosting Classifier	0.501867
1	XGBoost Classifier	            0.498333
2	CatBoost Classifier	            0.499667
3	AdaBoost Classifier	            0.503067
4	Random Forest Classifier	    0.498000