This repository contains a machine learning project aimed at building and evaluating predictive models using various algorithms. The project leverages Python libraries such as Pandas, Scikit-learn, Matplotlib, and XGBoost.
The goal of this project is to preprocess data, train multiple machine learning models, and evaluate their performance using various metrics. The project includes the following components:
- Data preprocessing and feature engineering
- Training multiple machine learning models
- Evaluating models using performance metrics
- Saving and loading trained models for deployment
- Preprocessing: Handles scaling, encoding, and splitting of datasets.
- Models:
- Logistic Regression
- Decision Trees
- Random Forests
- Support Vector Machines (SVM)
- Naive Bayes
- XGBoost
- AdaBoost
- Perceptron
- Evaluation:
- Accuracy, Precision, Recall, F1-Score
- Classification Reports
- Confusion Matrices
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Clone the repository:
git clone "https://github.com/satwikgarg2022461/ML_Project_Stress_prediction.git" cd your-repository
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Install the required dependencies:
pip install -r requirements.txt
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Place your dataset in the appropriate directory (e.g.,
data/). -
Modify the notebook or script to point to your dataset path.
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Run the Jupyter Notebook:
jupyter notebook main.ipynb
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Follow the steps in the notebook to preprocess data, train models, and evaluate results.
main.ipynb: The main notebook containing the project code.data/: Directory for input datasets.models/: Directory for saving trained models.outputs/: Directory for storing evaluation results.requirements.txt: File specifying project dependencies.
- Python 3.7+
- pandas
- numpy
- matplotlib
- seaborn
- scikit-learn
- xgboost
- pickle
Install dependencies using:
pip install -r requirements.txtFeel free to submit issues or pull requests. Contributions are welcome!
This project is licensed under the MIT License. See the LICENSE file for details.
- Scikit-learn Documentation
- XGBoost Documentation
- Python community for their amazing tools and libraries.