Customer Churn Prediction & Analysis

Machine Learning project developed using Python and Jupyter Notebook to identify customers likely to churn, perform exploratory data analysis, feature engineering, visualization, and predictive modeling.

Project Overview

This project predicts customer churn using historical customer information. Multiple preprocessing techniques, exploratory analysis, visualization, and machine learning algorithms were used to generate business insights and improve customer retention strategies.

Project Highlights

📈 Telecom Dataset

🐍 Python Language

📓 Jupyter Notebook

📊 Customer Analytics

Technologies Used

Python Pandas NumPy Matplotlib Seaborn Scikit-Learn Jupyter Machine Learning

Workflow

Data Collection
Data Cleaning
Exploratory Data Analysis
Feature Engineering
Data Visualization
Model Training
Performance Evaluation
Business Insights

Key Features

Customer churn prediction
Interactive visual analysis
Missing value handling
Correlation analysis
Feature importance
Classification model evaluation

Project Gallery

Results

The project identifies customers with high churn probability and provides business insights that can help improve customer retention and reduce revenue loss.

GitHub Repository ← Back to Portfolio