Diabetes Prediction Model Using SVM with Streamlit Deployment

Introduction

This repository contains the implementation of a machine learning model to predict the likelihood of diabetes based on patient data. The model uses Support Vector Machine (SVM) and achieves an accuracy of 72%. The application is deployed using Streamlit, allowing users to input medical data and receive real-time predictions regarding their diabetes risk.

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Objective

The goals of this project are:

1. Build a machine learning model to predict diabetes using medical data.
2. Deploy the model using Streamlit, enabling user interaction via a web interface.

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Dataset

The dataset used is the Pima Indians Diabetes Dataset. It includes the following features:

• Pregnancies: Number of pregnancies
• Glucose: Plasma glucose concentration
• Blood Pressure: Diastolic blood pressure
• Skin Thickness: Triceps skin fold thickness
• Insulin: 2-Hour serum insulin level
• BMI: Body mass index
• Diabetes Pedigree Function: Diabetes risk based on family history
• Age: Patient's age
• Outcome: (1 = Diabetic, 0 = Not Diabetic)

You can download the dataset from diabetes.csv

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Methodology

1. Data Preprocessing

• Handling missing or anomalous values in the dataset (e.g., replacing 0s in certain fields).
• Feature scaling using StandardScaler to normalize data.
• Splitting data into training and testing sets.

2. Model Building

• Using Support Vector Machine (SVM) with an RBF kernel to classify patients as diabetic or non-diabetic.
• Training the model on 80% of the data and testing on 20%.
• The model achieves an accuracy of 72% on the test data.

3. Model Evaluation

• The model's performance is evaluated based on accuracy, precision, recall, and F1-score.
• Accuracy is 72%, and further evaluations like confusion matrix provide insight into false positives and false negatives.

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Streamlit Deployment

The model is deployed using Streamlit to create a web-based interface where users can input relevant medical information and receive predictions in real-time. The steps for deployment are:

1. Building the Web Interface: A simple UI built using Streamlit allows users to enter data such as glucose levels, BMI, and age.
2. Real-Time Predictions: Once the data is input, the app provides a prediction on whether the user is likely to have diabetes.
3. Deploying the App: The app can be deployed on platforms like Heroku or Streamlit Sharing for public access.

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Installation and Usage

Requirements

• Python 3.x
• Streamlit
• Scikit-learn
• Pandas
• Numpy