Telecomm-Churn-Using-Multiple-Models

Telecom customer churn prediction

In this project, I used multiple Machine Learning models and did hyperparameter tuning for prediction.

• LOGISTIC REGRESSION
• Hyperparameter Optimization in logistic regression
• Decision Tree Classifier
• Random Forest Classifier
• XGBoost
• Hyperparameter Tuning of XGBoost

Problem Statement :

"You have a telecom firm which has collected data of all its customers" The main types of attributes are :

1. Demographics (age, gender etc.)
2. Services availed (internet packs purchased, special offers etc)
3. Expenses (amount of recharge done per month etc.) Based on all this past information, you want to build a model to predict whether a particular customer will churn. So the variable of interest, i.e. the target variable here is ‘Churn’ which will tell us whether or not a particular customer has churned. A binary variable 1 means that the customer has churned and 0 means the customer has not churned. With 21 predictor variables, we need to predict whether a particular customer will switch to another telecom provider or not.

DATA:-

Data is available in three CSV files