Diabetes Prediction Project

Overview

This project uses machine learning to predict diabetes based on health metrics. It employs the Support Vector Machine (SVM) with a linear kernel for classification.

Dataset

The dataset used is diabetes.csv, containing the health information of individuals. The target variable is Outcome, where:

• 0: Non-diabetic
• 1: Diabetic

Features

• Pregnancies: Number of times pregnant
• Glucose: Plasma glucose concentration in a 2-hour oral glucose tolerance test
• BloodPressure: Diastolic blood pressure (mm Hg)
• SkinThickness: Triceps skin fold thickness (mm)
• Insulin: 2-Hour serum insulin (mu U/ml)
• BMI: Body mass index (weight in kg/(height in m)^2)
• DiabetesPedigreeFunction: Diabetes pedigree function
• Age: Age in years

Files

• diabetes.csv: Dataset file containing health metrics
• diabetes_prediction.ipynb: Jupyter notebook with detailed analysis and code
• requirements.txt: List of Python dependencies

Installation

To run this project locally, follow these steps:

1. Clone the repository:

   git clone https://github.com/your-username/diabetes-prediction.git
   cd diabetes-prediction
   

2. Installing dependencies:

   pip install -r requirements.txt
   

Workflow

1. Data Loading and Preprocessing:

   • Load the dataset (diabetes.csv).
   • Separate features and target variable (Outcome).

2. Feature Standardization:

   • Standardize the features using StandardScaler to ensure each feature contributes equally to the model.

3. Model Training:

   • Split the dataset into training and testing sets (80%-20%).
   • Train an SVM model with a linear kernel on the training data.

4. Model Evaluation:

   • Evaluate the model on both training and testing data using the accuracy score.

5. Prediction:

   • Perform predictions on new input data to determine if the person is diabetic or not.

Example Prediction

# Example prediction for new input data
input_sample = np.array([5, 166, 72, 19, 175, 22.7, 0.6, 51]).reshape(1, -1)
input_df = pd.DataFrame(input_sample, columns=X.columns)  # Assuming X has feature names
input_standardized = scaler.transform(input_df)
prediction = clf.predict(input_standardized)

if prediction[0] == 0:
    print("Prediction: Person is not diabetic")
else:
    print("Prediction: Person is diabetic")