# Diabetes Prediction using Decision Tree Regressor

This project implements a diabetes prediction model using the diabetes dataset from scikit-learn and a Decision Tree Regressor.

The dataset used in this project is the diabetes dataset from scikit-learn. It contains ten baseline variables, age, sex, body mass index, average blood pressure, and six blood serum measurements for 442 diabetes patients. The target variable is a quantitative measure of disease progression one year after baseline.

We used a Decision Tree Regressor from scikit-learn to predict diabetes progression. Decision trees are versatile machine learning algorithms that can be used for both classification and regression tasks.

The main steps of the implementation include:

1. Loading the diabetes dataset
2. Preprocessing the data
3. Splitting the data into training and testing sets
4. Creating and training the Decision Tree Regressor model
5. Making predictions on the test set
6. Evaluating the model's performance

This heatmap shows the correlation between different features in the dataset.

[Insert your feature importance visualization here]

This chart displays the importance of each feature in the Decision Tree Regressor model.

• Python 3.x
• scikit-learn
• numpy
• pandas
• matplotlib
• seaborn

1. Clone this repository
2. Install the required dependencies
3. Run the main script: python diabetes_prediction.py

• Try other regression algorithms and compare their performance
• Perform hyperparameter tuning to optimize the model
• Collect more data to improve prediction accuracy

Feel free to fork this project and submit pull requests with any improvements or suggestions.