Customer Churn Prediction Using Machine Learning | End-to-End Python Project скачать в хорошем качестве

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 🔔 Don’t Forget To: 👍 Like | 💬 Comment | 🔔 Subscribe for more AI, ML, and Data Science Projects: @SouvikChai 📢 Share this project with your friends who are learning Machine Learning, Data Analytics, and Python!