• Project:
  Customer Churn Prediction
• Category:
  Data Science & Analytics
• Sub-Category:
  Data Science Fundamentals

• Cost:
  ₹ 15000
• Date:
  28/Jan/2026
• Status:
  Deployed

Project Details:

Project: Customer Churn Prediction

Customer Churn Prediction is a data-driven project focused on identifying customers who are likely to stop using a product or service. Churn is a critical challenge for businesses, as retaining existing customers is often more cost-effective than acquiring new customers. This project applies data science techniques to analyze customer behavior, uncover meaningful patterns, and predict churn in advance so that organizations can take proactive retention measures.

Project Overview

In this project, learners work with historical customer data that includes demographics, usage behavior, subscription details, and service interactions. The primary objective is to predict whether a customer is likely to churn or remain loyal. The project emphasizes transforming raw data into actionable insights using fundamental data science concepts.

Key Objectives

• Understand the concept of customer churn and its impact on business growth
• Clean, preprocess, and structure real-world datasets
• Perform exploratory data analysis to identify trends and churn indicators
• Build and evaluate a predictive churn model
• Interpret model results to support data-driven decision-making

Skills Covered

• Data cleaning and preprocessing techniques
• Exploratory Data Analysis (EDA)
• Data visualization and insight generation
• Feature engineering and selection
• Introduction to machine learning models such as Logistic Regression and Decision Trees
• Model evaluation using accuracy, precision, recall, and confusion matrix

Tools & Technologies Used

• Python for data analysis and modeling
• Pandas and NumPy for data manipulation
• Matplotlib and Seaborn for data visualization
• Scikit-learn for building and evaluating machine learning models
• Jupyter Notebook for interactive data exploration

Real-World Use Case

Customer churn prediction is widely used in industries such as telecommunications, banking, SaaS platforms, and subscription-based services. By identifying customers at risk of leaving, businesses can design targeted retention strategies such as personalized offers, service improvements, and proactive customer engagement.

Learning Outcomes

By completing this project, learners gain practical experience in solving a real business problem using data science. They develop a strong foundation in analytics, improve their ability to interpret data-driven insights, and build confidence to work on more advanced data science and machine learning projects.