Customer churn prediction is a critical task for businesses, particularly in the banking sector. Identifying customers who are likely to leave allows for proactive retention strategies, potentially saving significant revenue. In this blog post, I'll walk you through my project on predicting customer churn using the XGBoost algorithm, covering everything from data preprocessing to model evaluation.

📋 Table of Contents

• Introduction
• Project Overview
• Installation
• Usage
• Model Comparison
• Model Training
• Understanding XGBoost
• Results
• Conclusion

📌 Introduction

Customer churn is when customers stop using a company's products or services. Predicting churn helps businesses take proactive measures to retain customers, thus improving long-term profitability. In this project, I used the XGBoost algorithm, known for its efficiency and performance, to build a model for predicting customer churn in a bank.

Check out the project for better understanding GitHub repository

💡 Project Overview

The goal of this project is to build a machine learning model that predicts whether a customer will churn based on various features such as credit score, age, gender, balance, and more. I compared multiple algorithms, including Logistic Regression, Random Forest, KNN, and Naive Bayes, but ultimately chose XGBoost for its superior performance.

⚙️ Installation

To run this project, ensure you have Python installed. Clone the repository and install the required packages using the following command:

pip install -r requirements.txt

To run the flask app

python app.py

🚀 Usage

1. Clone the repository.
2. Place the dataset Churn_Modelling.csv in the project directory.
3. Run the xgb.py script to train the model.
4. Use app.py to serve the model and make predictions via a web interface.

🔍 Model Comparison

In the 'ChurnPrediction.ipynb' notebook, I compared the performance of five different machine learning algorithms:

1. Logistic Regression
2. XGBoost
3. Random Forest
4. K-Nearest Neighbors (KNN)
5. Naive Bayes

This comparison helps in understanding which model performs best for our churn prediction task.

🏋️ Model Training

The model training process involves several key steps:

1. Data Loading: Load the customer data from the CSV file.
2. Data Preprocessing: Encode categorical variables, drop unnecessary columns, and split the data into features and target variables.
3. Data Balancing: Use SMOTE (Synthetic Minority Over-sampling Technique) to handle class imbalance.
4. Model Training: Train an XGBoost classifier on the balanced training data.
5. Model Evaluation: Evaluate the model using classification metrics.

🧠 Understanding XGBoost

XGBoost (Extreme Gradient Boosting)

XGBoost is a scalable and efficient implementation of gradient boosted decision trees. Here's a brief overview of how it works:

• Decision Trees: XGBoost builds an ensemble of decision trees, where each tree is trained to correct the errors of the previous ones.
• Gradient Boosting: Uses gradient descent to minimize the loss function by adjusting weights. New trees are added sequentially, correcting errors from existing trees.
• Regularization: Includes regularization terms to control overfitting and improve generalization.
• Parallel Processing: Leverages parallel processing for faster computation.

📈 Results

The model's performance is evaluated using metrics such as precision, recall, F1-score, and accuracy. Below are the classification reports for both the training and test datasets.

Classification Report

🏁 Conclusion

In this project, I demonstrated how to predict customer churn using the XGBoost algorithm. By comparing various models and fine-tuning the chosen algorithm, I achieved a high-performance model capable of accurately predicting customer churn. This project highlights the importance of data preprocessing, handling class imbalance, and choosing the right algorithm for the task.

Feel free to check out the GitHub repository for the complete code and dataset. Happy coding!