Telecomm-Churn-Using-Multiple-Models
Telecom customer churn prediction
In this problem i have used multiple Machine Learning models and and also done hyperparameter tuning for prediction which are mentioned below.
- 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 :
- Demographics (age, gender etc.)
- Services availed (internet packs purchased, special offers etc)
- Expenses (amount of recharge done per month etc.) Based on all this past information, you want to build a model which will predict whether a particular customer will churn or not. 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. It is 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 is three csv files