Welcome to the Diabetes Prediction Project! This project focuses on developing a machine learning model to predict whether an individual has diabetes based on various health metrics and factors.
- Data Preprocessing: Clean and preprocess raw data to prepare it for modeling.
- Feature Selection: Identify relevant features that contribute significantly to diabetes prediction.
- Model Development: Train machine learning models using supervised learning algorithms such as logistic regression, decision trees, or neural networks.
- Model Evaluation: Assess the performance of the trained models using appropriate evaluation metrics like accuracy, precision, recall, and F1-score.
- Prediction Interface [RandomForest_Diabetes_predictor]: Provide an function for users to input their health data and obtain predictions on whether they have diabetes or not.
To set up the Diabetes Prediction Project, follow these steps:
- Clone the repository:
git clone https://github.com/Avyukth1C/diabetes-prediction.git - Unzip the files.
- Import the .ipynb into Google Colab.
- Import the prediction and patient datasets into your Google drive.[Change the file paths in the code respectively]
- Train the machine learning models using the prepared dataset.
Once the project is set up, you can:
- Input relevant health metrics such as glucose levels, HbA1c_level, BMI, etc., into the prediction .[Edit the values in the Excel sheet to give the values you desire]
- Obtain predictions on the likelihood of developing diabetes based on the provided input.
- Explore feature importance to understand which factors contribute most to the prediction.
- Evaluate the model's performance using various evaluation metrics and fine-tune the model if necessary.
If you encounter any issues or have questions about the Diabetes Prediction Project, please feel free to create an issue.
This project is licensed under the MIT License.