This project involves the prediction of diabetes progression using Ridge Regression in Jupyter Notebook. The dataset contains features such as glucose level, blood pressure, body mass index, and more. Through this analysis, we aim to build a regression model that accurately predicts the progression of diabetes based on the given input features.
The diabetes dataset used for this analysis includes various features related to diabetes patients, such as glucose level, blood pressure, body mass index, and the target variable: diabetes progression.
Before running the code, make sure you have the following dependencies installed:
- Python (3.x)
- Jupyter Notebook
- Pandas
- NumPy
- Matplotlib
- scikit-learn
To get started, follow the steps below:
- Clone the repository:
git clone https://github.com/shaadclt/Diabetes-Progression-Prediction-RidgeRegression.git- Change into the project directory:
cd Diabetes-Progression-Prediction-RidgeRegression-
Install the required dependencies:
-
Run Jupyter Notebook:
jupyter notebook-
Open the
Diabetes Progression Prediction.ipynbnotebook in Jupyter. -
Run the notebook cells to load the dataset, perform data preprocessing, train the Ridge Regression model, and evaluate its performance.
The notebook provides a step-by-step guide to predict diabetes progression using Ridge Regression. The analysis includes the following tasks:
- Loading and understanding the dataset
- Data cleaning and preprocessing
- Feature selection and transformation
- Splitting the dataset into training and testing sets
- Training the Ridge Regression model
- Evaluating the model's performance using metrics such as mean squared error and R-squared score
- Making predictions on new data points
After training the model and evaluating its performance, you will gain insights into how well the Ridge Regression model predicts diabetes progression based on the given input features. The notebook includes performance metrics and visualizations to assess the accuracy of the model. Feel free to refer to the notebook for detailed results and interpretations.
You can customize the analysis to suit your specific requirements. For example, you can experiment with different feature engineering techniques, try different regression algorithms, or incorporate additional features from the dataset to improve the model's accuracy.
This project is licensed under the MIT License. See the LICENSE file for more information.
- This analysis is inspired by the need to predict diabetes progression, which can be useful for healthcare professionals in understanding disease progression and planning appropriate interventions.
Contributions are welcome! If you find any issues or have suggestions for improvements, please open an issue or submit a pull request.