🔥 Customer Churn Analysis

📋 Project Overview

This project aims to predict customer churn — identifying customers who are likely to leave a company — using machine learning techniques. The dataset includes historical customer information, and the goal is to build a model that helps businesses improve customer retention.

⚙️ Features

• Data cleaning and preprocessing
• Feature engineering and selection
• Scaling of features using StandardScaler
• Model building using Random Forest Classifier
• Model evaluation with accuracy, precision, recall, and AUC-ROC

🛠️ Technologies Used

• Python
• Pandas
• NumPy
• Scikit-learn
• Matplotlib / Seaborn (for visualization)

▶️ How to Run

1. Clone the repository
   git clone https://github.com/yourusername/customer-churn-analysis.git
2. Install required libraries
   pip install -r requirements.txt
3. Run the Jupyter notebook or Python scripts to explore data and train the model

📊 Results

• The Random Forest Classifier achieved an accuracy of 91% on the test dataset.
• Precision and recall scores indicate the model performs well in correctly identifying churned customers and minimizing false alarms.
• The AUC-ROC score was 0.92, showing good model discrimination between customers who churn and those who stay.
• Feature importance analysis highlighted key factors influencing churn, such as tenure, monthly charges, and contract type.
• The model helps the business proactively identify high-risk customers to improve retention strategies.