obesity prediction project:
The Obesity Prediction project aims to develop predictive models to assess the likelihood of an individual developing obesity based on various factors such as demographics, lifestyle, genetics, and medical history. By leveraging machine learning techniques and analyzing large datasets, the project seeks to identify patterns and risk factors associated with obesity.
- Data Collection: Utilize diverse datasets containing information on demographics, lifestyle factors, genetic markers, and medical history related to obesity.
- Data Preprocessing: Clean, preprocess, and transform the raw data to make it suitable for model training.
- Model Development: Implement machine learning algorithms such as logistic regression, random forests, and neural networks to build predictive models.
- Evaluation: Evaluate model performance using appropriate metrics such as accuracy, precision, recall, and F1-score.
- Deployment: Deploy the trained models in real-world scenarios to assess the obesity risk of individuals and provide personalized recommendations for prevention and intervention.
- Clone the Repository: Clone this repository to your local machine using
git clone https://github.com/rajinmail/obesity-prediction.git. - Install Dependencies: Install the required dependencies using
pip install -r requirements.txt. - Data Preparation: Prepare the dataset for training by running the data preprocessing scripts provided in the
preprocessingdirectory. - Model Training: Train the predictive models using the processed data by executing the training scripts in the
modelsdirectory. - Evaluation: Evaluate the trained models' performance using the evaluation scripts in the
evaluationdirectory. - Deployment: Deploy the models in your preferred environment to make obesity predictions for individuals.
Contributions to improve the project, add new features, or enhance model performance are welcome! Please follow the contribution guidelines outlined in the CONTRIBUTING.md file.
This project is licensed under the MIT License. See the LICENSE file for details.
- Acknowledge any individuals, organizations, or datasets that contributed to the project.
- Mention any references or research papers that inspired the project.
Feel free to customize this template based on the specifics of your project and its requirements. The README file serves as a crucial document for users and contributors to understand the project's objectives, features, and usage instructions.