With 21 predictor variables, the task is to predict whether a particular customer will switch to another telecom provider or not, known as churning or not churning, respectively.
| Variable Name | Meaning |
|---|---|
| CustomerID | The unique ID of each customer |
| Gender | The gender of a person |
| SeniorCitizen | Whether a customer can be classified as a senior citizen |
| Partner | If a customer is married/in a live-in relationship |
| Dependents | If a customer has dependents (children/retired parents) |
| Tenure | The time for which a customer has been using the service |
| PhoneService | Whether a customer has a landline phone service along with internet service |
| MultipleLines | Whether a customer has multiple lines of internet connectivity |
| InternetService | The type of internet services chosen by the customer |
| OnlineSecurity | Specifies if a customer has online security |
| OnlineBackup | Specifies if a customer has online backup |
| DeviceProtection | Specifies if a customer has opted for device protection |
| TechSupport | Whether a customer has opted for tech support or not |
| StreamingTV | Whether a customer has an option of TV streaming |
| StreamingMovies | Whether a customer has an option of Movie streaming |
| Contract | The type of contract a customer has chosen |
| PaperlessBilling | Whether a customer has opted for paperless billing |
| PaymentMethod | Specifies the method by which bills are paid |
| MonthlyCharges | Specifies the money paid by a customer each month |
| TotalCharges | The total money paid by the customer to the company |
| Churn | Target variable indicating if a customer has churned or not |
Extensive exploratory data analysis (EDA), data scaling, and modeling were performed.
- Logistic Regression
- Decision Tree
- Random Forest
- Logistic Regression: Achieved 80% accuracy.
- Decision Tree and Random Forest helped identify tenure as the most significant predictor influencing churn.
- warnings
- pandas
- sklearn
- matplotlib
- seaborn
- statsmodel