📊 Diabetes Prediction Using SVM

🚀 Overview

This project implements a Support Vector Machine (SVM) classifier to predict whether a person is diabetic based on medical data. The model is trained using scikit-learn, with StandardScaler for feature scaling to improve accuracy.

📌 Features

• Uses Support Vector Machine (SVM) for classification
• Standardizes input features with StandardScaler
• Takes user input for real-time diabetes prediction
• Outputs whether the person is diabetic or not diabetic

🔧 Installation & Setup

1️⃣ Clone the Repository

git clone https://github.com/Strange0000/diabetes-prediction-svm.git
cd diabetes-prediction-svm

2️⃣ Install Dependencies

pip install -r requirements.txt

📊 Dataset

This model is trained using the Pima Indians Diabetes Dataset, which consists of the following features:

• Pregnancies
• Glucose
• BloodPressure
• SkinThickness
• Insulin
• BMI
• DiabetesPedigreeFunction
• Age

🚀 Usage

Run the Prediction Script

python predict.py

Example Input & Output

input_data = (5,166,72,19,175,25.8,0.587,51)
# Output: The person is diabetic

📌 Model Workflow

1. Load the dataset and preprocess it.
2. Train an SVM Classifier with a linear kernel.
3. Standardize input features using StandardScaler.
4. Predict diabetes based on new user input.

📈 Example Code

import numpy as np
import pandas as pd
from sklearn.preprocessing import StandardScaler
from sklearn import svm

# Load dataset (ensure you have trained the model)
df = pd.read_csv('diabetes.csv')
scaler = StandardScaler()
X = scaler.fit_transform(df.drop('Outcome', axis=1))

# Train SVM Model
clf = svm.SVC(kernel='linear')
clf.fit(X, df['Outcome'])

# Sample Prediction
input_data = np.array([[5,166,72,19,175,25.8,0.587,51]])
scaled_data = scaler.transform(input_data)
result = clf.predict(scaled_data)
print('Diabetic' if result[0] == 1 else 'Not Diabetic')

🛠️ Technologies Used

• Python 🐍
• Scikit-Learn 🤖
• NumPy 🔢
• Pandas 📊
• Matplotlib 📈

🤝 Contributing

Feel free to fork this project and submit pull requests!

📜 License

This project is licensed under the MIT License.

⭐ Show Your Support

If you like this project, give it a ⭐ on GitHub!