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Machine Learning and Deep learning classification has become an important topic in the area of Telecom Churn Prediction. Researchers have come out with very efficient experiments for Churn Prediction and have given a new direction to the telecommunication Industry to save their customers. Companies are eagerly developing the models for predicting churn and putting their efforts to save the potential churners. Therefore, for a better churn prediction model, finding the factors of churn is very important. This study is aiming to find the factors of user’s churn by evaluating their past service usage details. For this purpose, study is taking the advantage of feature importance, feature normalisation, feature correlation and feature extraction. After feature selection and extraction this study performing seven different experiments on the dataset to bring out the best results and compared the techniques. First Experiment includes a hybrid model of Decision tree and Logistic Regression, second experiment include PCA with Logistic Regression and Logit Boost, third experiment using a Deep Learning Technique that is CNN-VAE (Convolutional Neural Network with Variational Autoencoder), Fourth, fifth, sixth and seventh experiments was done on Logistic Regression, Logit Boost, XGBoost and Random Forest respectively. First four experiments are hybrid models and rest are using standalone techniques. The Orange dataset was used in this technique which has 3333 subscriber’s entries and 21 features. On the other hand, these experiments are compared with already existing models that have been developed in literature studies. The performance was evaluated using Accuracy, Precision, Recall rate, F-measure, Confusion Matrix, Marco Average and Weighted Average. This study proved to get better results as compared to old models. Random Forest outperformed in this study by achieving 95% Accuracy and all other experiments also produced very good results. The study states the importance of data mining techniques for a churn prediction model and proposes a very good comparison model where all machine Learning Standalone techniques, Deep Learning Technique and hybrid models with Feature Extraction tasks are being used and compared on the same dataset to evaluate the techniques performance better.

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