Banking Customer Churn Prediction Project | Machine Learning with Python in Jupyter Notebook

Welcome to this hands-on machine learning project tutorial! In this video, we will build a Banking Customer Churn Prediction Model using Python in Jupyter Notebook. This is a complete end-to-end machine learning project, guiding you through real-world data analysis, preprocessing, model building, and evaluation.

📌 What You’ll Learn:
What is customer churn and why it matters in the banking sector

Data exploration and visualization techniques

Feature engineering and preprocessing best practices

How to apply Label Encoding & One-Hot Encoding

Splitting data into training and testing sets

Implementing ML models like Logistic Regression, Random Forest, and more

Evaluating models using accuracy, precision, recall, and F1-score

Making predictions and deriving actionable insights

📁 Tools & Libraries Used:
Python

Pandas

NumPy

Matplotlib & Seaborn

Scikit-learn (sklearn)

Jupyter Notebook

🎯 Whether you’re preparing for interviews, building portfolio projects, or diving deeper into applied machine learning — this video will help you understand how to solve real-world problems using machine learning techniques.

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