This repository contains the code and resources for a customer churn data analysis project conducted entirely using Power BI. The project aims to analyze customer churn data, derive insights, and provide recommendations for improving customer retention strategies.

Customer churn, or customer attrition, is a critical metric for businesses, especially those in subscription-based models or with recurring revenue streams. Analyzing churn data can provide valuable insights into customer behavior, enabling businesses to take proactive measures to reduce churn rates and improve customer satisfaction.

In this project, we leverage Power BI's robust data visualization and analytics capabilities to perform the following tasks:

• Data preprocessing and cleaning
• Exploratory data analysis (EDA)
• Churn rate calculation and trend analysis
• Feature engineering
• Predictive modeling
• Visualization of insights and recommendations

1. Data Collection: Gathered customer data from various sources, including CRM systems and transaction logs.

2. Data Preparation: Cleaned and preprocessed the data to ensure its quality and suitability for Power BI.

3. Exploratory Data Analysis (EDA): Utilized Power BI's visualization capabilities to explore the dataset, identify patterns, and gain initial insights.

4. Churn Analysis: Defined churn criteria and analyzed churn rates, trends, and associated factors using Power BI visualizations.

5. Predictive Modeling: Applied predictive modeling techniques within Power BI to forecast and understand potential future churn.

6. Visualization and Reporting: Created compelling and informative Power BI reports and dashboards to communicate findings and recommendations.