Diabetes Prediction System

Overview

This repository contains a Python-based system that predicts whether a person has diabetes or not. We utilize a Support Vector Machine (SVM) model for this purpose. The system processes relevant health features and classifies individuals as either "diabetic" or "non-diabetic."

Files

• notebook.ipynb: A Jupyter Notebook containing the code for data preprocessing, model training, and evaluation.
• diabetes.csv: The dataset used for training and testing the model.

Dependencies

Make sure you have the following Python libraries installed:

• pandas
• numpy
• scikit-learn

Usage

1. Load the Dataset: The dataset (diabetes.csv) contains labeled health features (e.g., glucose level, blood pressure, BMI).
2. Data Preprocessing:
   • Clean the data by handling missing values and scaling features.
   • Split the data into features (X) and target labels (Y).
3. Split the Data:
   • Divide the dataset into training and testing sets (e.g., 80% training, 20% testing).
4. Train the Model:
   • Use SVM to train the model on the preprocessed data.
5. Evaluate the Model:
   • Assess the model's performance using metrics such as accuracy, precision, recall, and F1-score.

Results

The model achieved an accuracy of approximately 80% on the training data and 75% on the test data. You can further fine-tune hyperparameters or explore other models to improve performance.

Future Enhancements

Consider the following improvements:

• Feature Selection: Experiment with different subsets of features.
• Hyperparameter Tuning: Optimize SVM hyperparameters.
• Ensemble Methods: Explore ensemble techniques (e.g., Random Forest, Gradient Boosting).