This project focuses on predicting the risk of diabetes using linear regression analysis. By leveraging historical medical data such as blood pressure, BMI, age, and other relevant factors, the aim is to develop a model that can effectively forecast the likelihood of individuals developing diabetes.
Diabetes is a prevalent health condition with significant impacts on individuals' lives. Early detection and intervention are crucial for managing the disease effectively. This project aims to provide a tool for early prediction of diabetes risk based on various medical and lifestyle factors.
- Utilizes linear regression analysis for predictive modeling.
- Incorporates historical medical data for training the model.
- Offers the potential for early detection of diabetes risk.
- Provides insights into the importance of different factors influencing diabetes development.
- Data Collection: Gather a comprehensive dataset containing relevant medical and lifestyle features.
- Data Preprocessing: Clean and prepare the dataset, handling missing values and normalizing features.
- Model Training: Train the linear regression model using the prepared dataset.
- Model Evaluation: Assess the model's performance using appropriate metrics such as MSE or R².
- Python 3.x
- Data processing and analysis libraries (Pandas, NumPy, Matplotlib, Seaborn)
- Machine learning libraries (Scikit-learn)
- Jupyter Notebook (optional, for visualization and experimentation)
Contributions to this project are welcome! If you have ideas for improvement, feature requests, or bug reports, please feel free to open an issue or submit a pull request.