Welcome to the Telco Churn Prediction and Analysis project repository! In this project, we conducted an in-depth analysis of Telco customer churn, employing logistic regression for prediction and Power BI for data visualization. This integrated approach allowed us to identify the key drivers of churn and derive actionable insights to enhance customer retention strategies.

Churn prediction is a crucial task for businesses, especially in the telecom industry. Understanding and minimizing customer churn is vital for maintaining revenue and ensuring customer satisfaction. In this project, we utilized various techniques to analyze and predict churn:

• Logistic Regression: We used machine learning techniques, specifically logistic regression, to predict customer churn. This model helped us identify which customers were more likely to churn based on historical data.

• Power BI Visualization: Power BI was employed to create interactive and insightful data visualizations. These visualizations enabled us to explore the data, identify trends, and communicate the results effectively.

To further enhance the visualization and exploration of our Telco Churn Analysis results, we have created an interactive Power BI dashboard. You can access and interact with the dashboard by opening the provided Power BI file:

• Telco_Churn_Dashboard.pbix: This Power BI file contains a comprehensive dashboard with various visualizations and insights derived from our analysis.

Our project involved a range of skills, including:

• Power BI: Proficient use of Power BI for data visualization and reporting.
• Machine Learning: Application of machine learning techniques, specifically logistic regression, for churn prediction.
• Business Analysis: Thorough analysis of business data to derive actionable insights.
• Python (Programming Language): Utilization of Python for data preprocessing and analysis.