Customer Churn Prediction Model Using Machine Learning

Overview

Developed a machine learning model to predict customer churn based on behavioral and transactional data. The goal was to identify customers likely to leave and provide actionable insights.

Dataset

Structured dataset with customer demographics and usage patterns

Included features such as tenure, activity, and engagement metrics

What Was Done

Data cleaning and preprocessing (handled missing values & duplicates)

Exploratory Data Analysis (EDA) to identify key patterns

Feature selection to improve model performance

Built classification models (Logistic Regression, Random Forest)

Tuned hyperparameters for better accuracy

Results

Achieved high prediction accuracy

Identified key factors influencing churn

Delivered a model ready for real-world use

Tools & Technologies

Python, Pandas, NumPy, Scikit-learn, Matplotlib