Customer churn occurs when customers stop using a company’s products or services, leading to revenue loss and impacting long-term business growth. Understanding why customers churn is essential for developing effective retention strategies and improving customer satisfaction.
This project focuses on analyzing churn data to uncover key patterns and insights that can help businesses predict and reduce churn rates. By applying data analytics and machine learning techniques, the goal is to identify customers who are likely to leave and provide actionable recommendations to retain them.
🎯 Objectives
Understand the major factors contributing to customer churn.
Build predictive models to classify customers as likely to churn or stay.
Visualize churn trends and customer behavior using data-driven insights.
Provide recommendations to reduce churn and improve customer loyalty.
🧩 Features
Data Exploration & Cleaning: Handle missing values, outliers, and prepare data for modeling.
Exploratory Data Analysis (EDA): Identify patterns and visualize relationships between churn and customer attributes.
Feature Engineering: Create meaningful variables that enhance model performance.
Predictive Modeling: Implement classification models (e.g., Logistic Regression, Random Forest, XGBoost) to predict churn.
Model Evaluation: Assess model accuracy, precision, recall, F1-score, and ROC-AUC.
Actionable Insights: Translate model results into practical business recommendations.
📊 Technologies Used
Programming Language: Python
Live Link: https://customer-churn-analysis-predication-one.vercel.app/
Libraries: Pandas, NumPy, Matplotlib, Seaborn, Scikit-learn
Tools: Jupyter Notebook, Google Colab
Techniques: Data Cleaning, Feature Engineering, Predictive Modeling, Data Visualization