Welcome to the Diabetes Prediction project! This implementation utilizes the Decision Tree Classifier to predict the likelihood of diabetes based on the Pima dataset. The model.ipynb file includes both the implementation of the classifier and insightful data visualizations.
- model.ipynb: This Jupyter Notebook contains the implementation of the Decision Tree Classifier for predicting diabetes using the Pima dataset. The notebook covers data preprocessing, model training, and insightful visualizations using histograms to depict the relationship between diabetes positivity and each Pima entity.
- Pima Dataset: The dataset used for training and testing the Decision Tree Classifier.
The data visualization section in model.ipynb includes histograms that provide a clear representation of the relationship between diabetes positivity and each of the Pima entities. These visualizations aid in understanding the distribution of features and their impact on the prediction model.
1.Clone this repository:
git clone https://github.com/AHBRIJESH/Decesion_Tree_based_Diabetic_Predictor.git
cd Decesion_Tree_based_Diabetic_Predictor.git- Open and run the cells in
model.ipynbusing Jupyter Notebook or any compatible environment. - Follow the step-by-step instructions for data preprocessing, model training, and data visualization.
- Gain insights into the dataset and explore the visualizations depicting the relationship between diabetes and Pima entities.
- /data: Contains the Pima dataset for training and testing the model.
- /images: Holds relevant images or plots generated during the data visualization process.
- model.ipynb: The main Jupyter Notebook for the Decision Tree Classifier implementation.
- README.md: The project's documentation.
Feel free to contribute by opening issues, providing suggestions, or submitting pull requests. Let's collaborate to enhance the accuracy and interpretability of the Diabetes Prediction model!
Happy coding! 🩺📊