Customer-Churn-Prediction

Overview

Customer churn, also known as customer attrition, occurs when a customer stops using a company's product or service. In today's highly competitive business environment, predicting and preventing customer churn is essential for maximizing revenue and maintaining a loyal customer base.

This project aims to develop a machine learning model that predicts whether a customer will churn based on demographic, account, and service-related data. By identifying at-risk customers, businesses can proactively implement retention strategies.

Problem Statement

The objective of this project is to build a classification model that predicts customer churn using:

• Demographic data (e.g., gender, senior citizen status, tenure)
• Service usage details (e.g., internet service, phone service, online security) By leveraging machine learning, we aim to help businesses:
• Identify customers likely to churn.
• Design targeted retention strategies.
• Minimize revenue loss.

Dataset Information

Dataset Name: Customer_data:https://docs.google.com/spreadsheets/d/1rnBO9F9xdSUY-WpeOJilMxMRZT-hwwWq6O98OHreY0k/edit?gid=1602415961#gid=1602415961

• The dataset contains customer-related features such as demographic details, service subscriptions, and account tenure.
• The target variable is Churn, which indicates whether a customer has left the service or not.

Deliverables

Data Preprocessing - Analyze the dataset, handle missing values, perform feature engineering, and prepare data for modeling.

Machine Learning Model - Train classification models, tune hyperparameters, and evaluate performance using metrics like accuracy, precision, recall, and F1-score.

Tools & Technologies Used

• Programming Language: Python
• Libraries & Frameworks:
  • pandas (Data Manipulation)
  • numpy (Numerical Computation)
  • matplotlib & seaborn (Data Visualization)
  • scikit-learn (Machine Learning)
  • imbalanced-learn (Handling Imbalanced Data)
• **Development Environment: ** Jupyter Notebook

Impact of Analysis

• Helps businesses identify at-risk customers and take proactive retention measures.
• Reduces revenue loss by minimizing customer churn.
• Provides data-driven insights for better decision-making and personalized customer strategies.