🩺 Diabetes Prediction using Supervised Learning

🔍 Project Overview

This project applies supervised machine learning algorithms to predict whether a patient is diabetic based on diagnostic medical attributes. Using the Pima Indians Diabetes Dataset, we trained and evaluated multiple classification models to identify high-risk individuals and assist in early detection.

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📊 Dataset

• Source: Kaggle – Pima Indians Diabetes Database
• Features: Pregnancies, Glucose, Blood Pressure, Skin Thickness, Insulin, BMI, Diabetes Pedigree Function, Age
• Target: Outcome (0: Non-Diabetic, 1: Diabetic)

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🚀 Features Implemented

• ✅ Data cleaning & exploration
• ✅ Feature scaling with StandardScaler
• ✅ Model training: Logistic Regression, Random Forest, and SVM
• ✅ Model evaluation using:
  • Accuracy, Precision, Recall, F1-Score
  • Confusion Matrix & Classification Report
  • ROC Curve & AUC Score
• ✅ Single-patient prediction with real data simulation
• ✅ Clean, modular, and well-commented code

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🧠 Algorithms Used

Model	Description
Logistic Regression	Interpretable baseline classifier
Random Forest	Ensemble method for robust predictions
Support Vector Machine (SVM)	Effective for small-to-medium datasets with scaling

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📈 Performance

The Random Forest classifier showed the best performance with:

• Accuracy: ~85%
• ROC AUC Score: High discriminative power
• Balanced precision and recall, ideal for medical diagnosis

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🔮 Sample Prediction

sample = np.array([[6, 148, 72, 35, 0, 33.6, 0.627, 50]])
sample_scaled = scaler.transform(sample)
prediction = model.predict(sample_scaled)

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🛠️ Tech Stack

• Python (NumPy, Pandas, Scikit-Learn)
• Matplotlib & Seaborn for visualizations
• Jupyter Notebook / Google Colab

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💡Future Improvements

• Hyperparameter tuning using GridSearchCV
• Model deployment with Streamlit or Flask
• Cross-validation and imputation for missing values
• Advanced models like XGBoost or LightGBM

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