A collaborative project utilizing ensemble models for Binary classification with Bank churn . 🚀💎

• Description
• Models
• Installation
• Usage
• Contributing
• License

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.

# 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

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.

• Fork the project
• 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

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