📊 Customer Churn Prediction with XGBoost

This project aims to predict customer churn using machine learning techniques, specifically XGBoost. The goal is to identify customers likely to leave a service, allowing for proactive retention strategies.

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🧱 Project Overview

The project follows a standard end-to-end machine learning pipeline:

1. Data Cleaning – Handled missing values, removed inconsistencies.
2. Exploratory Data Analysis (EDA) – Explored distributions, correlations, and key patterns.
3. Feature Engineering – Converted categorical variables, normalized numerical features.
4. Class Imbalance Handling – Applied techniques like SMOTE to improve recall on minority class.
5. Modeling – Trained and tuned an XGBoost classifier for best performance.
6. Evaluation – Assessed using accuracy, precision, recall, F1-score, ROC-AUC, and confusion matrix.

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🔧 Tech Stack

• Language: Python 3.x
• Libraries: Pandas, NumPy, Matplotlib, Seaborn, Scikit-learn, XGBoost, imbalanced-learn

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📂 Project Structure

Directory/File	Description
data/	Cleaned dataset
notebooks/	EDA and modeling Jupyter notebooks
models/	XGBoost model
README.md	Project overview and instructions

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