Project Title:Customer Churn Prediction Using Machine Learning

 Objective:

To develop a machine learning model that predicts whether a customer is likely to leave (churn) a business or service based on their behavior and profile data.

Project Summary:

Customer churn refers to the loss of customers over time. This project focuses on building a predictive model using machine learning to identify customers at risk of leaving a company (e.g., a telecom or subscription service). The dataset usually includes features such as customer demographics, service usage patterns, contract type, payment method, and tenure. By analyzing this data, the model predicts whether a customer will churn, helping businesses take proactive actions to retain valuable customers.

Key Components:

Dataset: Customer data with features like age, gender, services used, contract length, billing info, and churn label

Algorithms: Logistic Regression, Decision Tree, Random Forest, Support Vector Machine (SVM), XGBoost

Tools & Libraries: Python, Scikit-learn, Pandas, NumPy, Matplotlib, Seaborn

Technologies: Machine Learning, Classification, Predictive Analytics

Features:

Data preprocessing: handling missing values, encoding categorical data

Feature selection and engineering for better model performance

Training different classification models to predict churn

Model evaluation using accuracy, precision, recall, F1-score, and ROC-AUC

Visualizations: churn rates, feature importance, confusion matrix

Applications:

Telecom and internet service providers

Subscription-based businesses (e.g., streaming platforms, SaaS)

Banking and insurance industries

E-commerce and retail loyalty management systems

Outcome:

This project helps students learn how to handle real-world business problems using data-driven approaches. It provides practical experience in working with classification models and interpreting results for actionable insights. The solution can help companies reduce churn and improve customer retention strategies.