Customer Churn Analysis & Prediction — Python

🔧 Tools Used

• Python
• Pandas, NumPy
• Matplotlib, Seaborn
• Scikit-learn

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📌 Project Overview

This project analyzes customer churn data to identify key factors influencing customer attrition and builds a machine learning model to predict churn.

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📂 Dataset

• Telecom customer churn dataset with customer demographics, services, and billing information.

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🛠️ Key Steps Performed

• Data cleaning and preprocessing (type conversion, missing value handling)
• Exploratory Data Analysis (EDA) using visualizations
• Feature encoding and scaling
• Built a Logistic Regression model for churn prediction
• Evaluated model performance using accuracy, precision, recall, and F1-score

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📈 Model Performance

• Logistic Regression Accuracy: ~78%
• Strong performance in identifying non-churn customers

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🎯 Outcome

Identified important churn drivers such as contract type, tenure, and monthly charges, providing actionable insights for customer retention strategies.

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📁 Files Included

• Customer_Churn_Analysis.ipynb — Jupyter Notebook
• WA_Fn-UseC_-Telco-Customer-Churn.csv — Dataset

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Note This project is part of my Data Analytics portfolio, showcasing Python-based data analysis and machine learning skills.