https://github.com/yashksaini-coder/binary-classification-churn-prediction
Bank Churn Binary Classification Project | Prediction of the bank churn implementing the Ensemble model building technique.
https://github.com/yashksaini-coder/binary-classification-churn-prediction
classification data-science ensemble-learning machine-learning machine-learning-algorithms supervised-learning
Last synced: 2 months ago
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Bank Churn Binary Classification Project | Prediction of the bank churn implementing the Ensemble model building technique.
- Host: GitHub
- URL: https://github.com/yashksaini-coder/binary-classification-churn-prediction
- Owner: yashksaini-coder
- License: mit
- Created: 2024-02-02T17:31:11.000Z (over 1 year ago)
- Default Branch: main
- Last Pushed: 2024-03-13T15:01:08.000Z (over 1 year ago)
- Last Synced: 2025-03-25T11:01:33.079Z (3 months ago)
- Topics: classification, data-science, ensemble-learning, machine-learning, machine-learning-algorithms, supervised-learning
- Language: Jupyter Notebook
- Homepage:
- Size: 18.6 MB
- Stars: 16
- Watchers: 1
- Forks: 0
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
- License: LICENSE
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README
# Binary Classification with a Bank Churn Dataset
> A collaborative project utilizing ensemble models for Binary classification with Bank churn . 🚀💎
---
## Table of Contents
- [Description](#description)
- [Models](#models)
- [Installation](#installation)
- [Usage](#usage)
- [Contributing](#contributing)
- [License](#license)
---
## Description
This project focuses on Binary classification of Bank customers churn using a combination of various machine learning models.
Machine Learning Models included are:-
1. DecisionTreeClassifier
2. QuadraticDiscriminantAnalysis
3. KNeighborsClassifier
4. LogisticRegression
5. RidgeClassifier
6. Perceptron
7. SGDClassifier
8. AdaBoostClassifier
9. RandomForestClassifier
10. HistGradientBoostingClassifier
11. GradientBoostingClassifier
12. ExtraTreesClassifier
13. GaussianNB
14. lgb (LightGBM)
15. xgb (XGBoost)
16. CatBoostClassifier
17. MLPClassifier (Multi-layer Perceptron)
Ensemble Techniques:-
- **VotingClassifier:-** Used for combining multiple machine learning models.
---
## Installation
```bash
# Clone the repository
git clone https://github.com/yashksaini-coder/Binary-Classification-with-a-Bank-Churn-Dataset
# Navigate to the project directory
cd Binary-Classification-with-a-Bank-Churn-Dataset
# Install dependencies
pip install -r requirements.txt
```
# Usage
To run the prediction models, follow these steps:
- Open the Jupyter Notebook or Python script.
- Run the cells or execute the script.
- Input the relevant features for prediction.
- Obtain the predicted Mohs hardness.
# Contributing
- [Fork the project](https://github.com/yashksaini-coder/Binary-Classification-with-a-Bank-Churn-Dataset/fork)
- Create your feature branch (git checkout -b feature/AmazingFeature)
- Commit your changes (git commit -am 'Add some AmazingFeature')
- Push to the branch (git push origin feature/AmazingFeature)
Open a pull request
# Contact
[Medium](https://medium.com/@yashksaini) [LinkedIn](https://www.linkedin.com/in/yashksaini/)