Customer Churn Prediction

Project Overview

This project aims to predict customer churn using machine learning techniques. By analyzing customer behavior and transaction history, we can identify customers who are likely to churn and take preventive actions.

Dataset

The dataset used contains customer information, such as demographics, usage patterns, and historical transactions. [Include details about where the dataset can be found or if it's included in the repository.]

Methodology

1. Data Preprocessing: Cleaned and processed the data for analysis.
2. Exploratory Data Analysis (EDA): Visualized and understood the data distribution and patterns.
3. Feature Engineering: Created new features that could help improve the model's performance.
4. Modeling: Used machine learning algorithms like Logistic Regression, Decision Trees, and Random Forest to predict churn.
5. Evaluation: Evaluated the model's performance using accuracy, precision, recall, and AUC-ROC scores.

How to Run the Project

1. Clone the repository:

   git clone https://github.com/your-username/customer-churn-prediction.git