Customer Churn Prediction

Done ✅-> EDA✅ , Data Validation✅ , Model Training✅(ML Algorithms✅, Neural Netwrok✅), Mlflow Tracking✅

Demo

customerChurnPredictionProject_AdobeExpress_AdobeExpress.mp4

Data Insights:

• Numerical Data Points are equally distributed.

• Almost equally distribution of each class in each feature.

• With respect to churn (equal contribution of all classes)

Installation

1. Clone the repository and navigate:

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

2. Create virtaul environment and activate it.

virtalenv churnvenv 
churnvenv/Scipts/activate.ps1

3. Install the required dependencies:

pip install -r requirements.txt

4. run main.py for data validation , data transformation, model training and mlflow tracking.

python run main.py

5. Run the the streamlit app:

streamlit run app.py

MLflow

• MLflow for local web server

mlflow ui

• run this in environment

export MLFLOW_TRACKING_URI=https://dagshub.com/GyanPrakashKushwaha/Customer-Churn-Prediction.mlflow
export MLFLOW_TRACKING_USERNAME=GyanPrakashKushwaha 
export MLFLOW_TRACKING_PASSWORD=53950624aa84e08b2bd1dfb3c0778ff66c4e7d05

• Tracking URL

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

I TRIED MY BEST! 😓

• For model performance Improvement(Data manipulation) normalized the features using log normal distribution but the performance didn't increase and then tried Generated Data using SMOTE and then trained model in the large data but still the accuracy remained same.

• For model performance Improvement (Model training) Used complex Algorithms - GradientBoostingClassifier , XGBoostClassifier , CatBoostClassifier , AdaBoostClassifier , RandomForestClassifier to easy algorithm like Logistic Regession and Also trained Deep Neural Network with different weight Initializers , activation function ,input nodes and optimizer but models performance not Improved .

• neural netwrok architecture

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

• Machine learning models and best parameters

{'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

TODO

• read data from mondoDB
• deploy the model in AWS