Diabetic Prediction Model Project

Welcome to the Diabetic Prediction Model project! This project aims to develop a predictive system for diabetes using machine learning techniques. The implementation utilizes the power of Python and popular libraries such as pandas, matplotlib, numpy, and seaborn.

Project Structure

1.Data Exploration and Analysis:

Utilized pandas for exploring and understanding the dataset. Analyzed key statistics and characteristics of the data.

2.Data Visualization:

Leveraged matplotlib and seaborn to create visualizations for better insights. Included pair plots, heatmaps, and count plots to observe patterns and relationships.

3.Handling Outliers:

Identified outliers through data visualization. Applied necessary techniques to handle outliers and maintain data integrity.

4.Data Standardization:

Ensured data standardization for consistent model performance. Employed preprocessing techniques to scale the features appropriately.

5.Train and Test Split:

Segregated the dataset into training and testing sets. Used a strategic split to ensure the model's ability to generalize.

6.Building the Predictive Model:

Implemented a machine learning classifier for diabetes prediction. Employed Support Vector Machine (SVM) as the model of choice.

7.Model Evaluation:

Utilized accuracy score as the metric system to evaluate model performance. Provided insights into the model's effectiveness in predicting diabetes.

Open and run the Jupyter Notebook diabetic_prediction_model.ipynb to explore the project. Explore and Contribute:

Feel free to explore the code, experiment with parameters, and contribute to the project. If you encounter any issues or have suggestions, please open an issue or submit a pull request.

Acknowledgments Special thanks to the InternPe community and contributors for their valuable insights and contributions.

Happy exploring and predicting! 🚀