Customer Churn Prediction Using Machine Learning | End-to-End Python Project

Learn how to build a Customer Churn Prediction Project using Machine Learning in Python! 🚀
In this full end-to-end tutorial, we’ll predict customer churn using real-world bank data and explore how data-driven insights can help businesses retain their valuable customers.

This Customer Churn Prediction Machine Learning Project is perfect for data science, AI, and ML enthusiasts who want to build a real-world project for their portfolio and understand how predictive modeling works in business applications.

GitHub Code Link for this repository: https://github.com/nightfury217836/Cu...

We’ll cover everything — from data preprocessing, feature engineering, and EDA (Exploratory Data Analysis) to model training, evaluation, and visualization — all explained step-by-step using Python.

🧠 What You’ll Learn:

✅ How to preprocess and clean structured data
✅ Perform in-depth Exploratory Data Analysis (EDA) to find patterns
✅ Engineer meaningful features like Balance, Tenure, and Product Usage
✅ Build and compare ML models — Logistic Regression, Random Forest, XGBoost
✅ Evaluate performance using Accuracy, Precision, Recall, F1-Score, and ROC-AUC
✅ Visualize churn trends and customer behavior using Seaborn & Matplotlib
✅ Understand how businesses can use churn prediction for customer retention strategies

🧩 Tools & Libraries Used:

Python | Pandas | NumPy | Matplotlib | Seaborn | Scikit-learn | XGBoost

💼 Project Type:

Machine Learning | Data Science | Business Analytics | Predictive Modeling | Customer Retention | Churn Analysis | Python Project

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📢 Share this project with your friends who are learning Machine Learning, Data Analytics, and Python!