A collaborative project utilizing ensemble models for Binary classification with Bank churn . 🚀💎
This project focuses on Binary classification of Bank customers churn using a combination of various machine learning models. Machine Learning Models included are:-
- DecisionTreeClassifier
- QuadraticDiscriminantAnalysis
- KNeighborsClassifier
- LogisticRegression
- RidgeClassifier
- Perceptron
- SGDClassifier
- AdaBoostClassifier
- RandomForestClassifier
- HistGradientBoostingClassifier
- GradientBoostingClassifier
- ExtraTreesClassifier
- GaussianNB
- lgb (LightGBM)
- xgb (XGBoost)
- CatBoostClassifier
- 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