This project predicts diabetes disease progression using machine learning regression techniques. The objective was to build an end-to-end machine learning workflow, including data preprocessing, exploratory data analysis, feature scaling, model training, evaluation, and interpretation of results.
The dataset used is the Diabetes dataset from sklearn.datasets. It contains baseline medical measurements collected from diabetes patients, with the goal of predicting disease progression one year after baseline.
The target variable is:
- Disease progression score
- Loaded and explored the Diabetes dataset
- Performed exploratory data analysis (EDA)
- Checked for missing values and data quality
- Visualized feature relationships
- Split the data into training and testing sets
- Applied feature scaling using StandardScaler
- Trained a Linear Regression model
- Evaluated model performance using regression metrics
- Interpreted the model results
- Python
- Pandas
- NumPy
- Matplotlib
- Scikit-learn
- Jupyter Notebook
The model was evaluated using:
- Mean Absolute Error (MAE)
- Root Mean Squared Error (RMSE)
- R² Score
These metrics were used to assess how accurately the model predicts diabetes disease progression on unseen data.
- Data preprocessing and feature scaling
- Exploratory Data Analysis (EDA)
- Regression modeling
- Model evaluation and interpretation
- Building reproducible machine learning workflows
This project demonstrates the complete workflow for developing a regression model to predict diabetes disease progression. Although predicting disease outcomes is challenging due to the complexity of medical data, the project highlights the importance of proper preprocessing, feature scaling, model evaluation, and result interpretation. It provides a solid foundation for applying machine learning techniques to healthcare-related prediction problems.# Diabetes-disease-progression-prediction-