Bank-Churn-Classification

Overview

This repository contains code for predicting customer churn in a bank using machine learning techniques. Customer churn refers to the phenomenon where customers stop doing business with a company. Predicting churn is crucial for businesses, especially in industries with high competition, such as banking, as it allows proactive measures to retain customers.

Dataset

The dataset used in this project is a fictional dataset representing customers of a bank. It contains various features such as customer demographics, account information, transaction history, etc. The target variable is 'Churn', indicating whether a customer has churned or not.

Installation

1. Clone the repository:

   git clone https://github.com/pranavvyawahare25/bank-churn-classification.git
   

2. Install dependencies:

   import numpy as np
   import pandas as pd
   import matplotlib.pyplot as plt
   import seaborn as sns 
   import warnings
   
   

Model Performance

• Include details about the model's performance metrics, such as accuracy, precision, recall, F1-score, etc.

  Model-Name Accuracy AUC F1-Score

0. DecisionTreeClassifier 80.006463 70.431959 53.069739
1. RandomForestClassifier 85.845570 87.133568 61.315964
2. GradientBoostingClassifier 86.481792 88.792851 62.489492
3. ExtraTreesClassifier 85.310335 86.395071 60.263345
4. AdaBoostClassifier 86.075822 87.944320 61.360834
5. LogisticRegression 83.258266 81.294726 48.601724
6. XGBClassifier 86.352528 88.588906 63.255207

Contributing

Contributions to improve the code or add new features are welcome. Please follow the standard GitHub workflow:

1. Fork the repository.
2. Create a new branch (git checkout -b feature/new-feature).
3. Make your changes.
4. Commit your changes (git commit -am 'Add new feature').
5. Push to the branch (git push origin feature/new-feature).
6. Create a new Pull Request.

License

This project is licensed under the MIT License - see the LICENSE.md file for details.