Customer Churn Prediction Project Using Classification Techniques

Customer Churn Prediction Project Using Classification Techniques

Learn how to analyze real-world telecom customer data using Python to predict churn and identify high-risk users. This beginner-friendly handbook guides you through data cleaning, feature engineering, exploratory analysis, and building a Logistic Regression model, with deployment via Gradio in Google Colab.

Customer Churn Prediction Project

This handbook helps learners gain hands-on experience in data analysis by working with a real-world student performance dataset. It explains how to clean, transform, and visualize academic data to uncover attendance trends, study patterns, performance changes, and key factors influencing exam scores, all in a clear and beginner-friendly way using Python, Pandas, Matplotlib, and Seaborn.

Customer Churn Prediction Project for Beginners

This project is ideal for beginners who want to get started with data analysis and machine learning. It’s perfect for students, freshers, and anyone with basic Python knowledge who wants to understand how real-world telecom customer data can be cleaned, analyzed, and used to build a predictive churn model through practical, hands-on implementation

Prerequisites

This course is suitable for:

• Basic knowledge of Python programming
• Basic understanding of data analysis concepts (columns, rows, missing values)
• A Google account to access Google Colab
• Access to the sales dataset in CSV format
• Internet connection to upload the dataset and run the project in Colab