Diabetes Prediction System

Overview

This project aims to build a system that predicts whether a person has diabetes or not using Machine Learning techniques. The system is developed in Python, utilizing the Support Vector Machine (SVM) model for prediction.

Technologies Used:

• Python
• Support Vector Machine (SVM)
• Machine Learning
• Scikit-learn
• Pandas
• Numpy

Project Structure

• diabetes-prediction.ipynb: The main Python script containing the implementation of the Support Vector Machine (SVM) model and data analysis.
• diabetes.csv: The dataset used for training and testing the model.
• README.md: This file, providing an overview of the project, technologies used, and other relevant information.

Results

The Support Vector Machine model developed in the project achieved an Accuracy-Score of 77.27272727272727%.

This metric indicates that the model performs well in predicting whether a person has diabetes or not based on the provided dataset. The high accuracy value suggests that the model is reliable and effective for this classification task.

What I Have Learned

Throughout the development of this project, I have gained several key insights and skills:

• Understanding of Machine Learning Workflow: From data preprocessing and feature selection to model training and evaluation.
• Support Vector Machine (SVM): Deepened my understanding of how SVM works, including kernel functions and hyperparameter tuning.
• Data Handling with Pandas: Enhanced my ability to manipulate and analyze data efficiently using the Pandas library.
• Model Evaluation: Learned how to evaluate model performance using metrics such as accuracy.
• Python Programming: Improved my Python programming skills, especially in the context of data science and machine learning.
• Problem-Solving: Developed problem-solving skills by tackling the challenges of building an accurate prediction model.