Predictive Analysis on Telecom Customer churn: A Case Study on AIRTEL

To analyze historical data of AIRTEL telecom customers to identify patterns and trends leading to churn.

To develop predictive models using advanced analytics techniques to forecast customer churn.

To evaluate the effectiveness of the predictive models in identifying at-risk customers and reducing churn rates.

To provide actionable recommendations to AIRTEL based on the predictive analysis results.

Collect and clean historical data on AIRTEL telecom customers, including demographic information, usage patterns, and churn status.

Conduct exploratory data analysis to identify key factors influencing customer churn.

Build and validate predictive models using machine learning algorithms such as logistic regression, decision trees, and random forests.

Evaluate the performance of the predictive models using metrics like accuracy, precision, recall, and F1 score.

Present the findings and recommendations in a comprehensive report outlining the predictive analysis process and results.