🧠 Support Vector Regression (SVR) on Tips Dataset

This project demonstrates how to use Support Vector Regression (SVR) to predict restaurant total bills using the well-known Tips dataset. It includes preprocessing (label + one-hot encoding), model training, evaluation, and hyperparameter tuning using GridSearchCV.

---

📚 Dataset Overview

The Tips dataset from Seaborn includes information about meals in a restaurant and the corresponding tips. It contains both numerical and categorical variables.

Features:

• total_bill: Total cost of the meal (target)
• tip: Tip amount
• sex: Gender of the customer (Male/Female)
• smoker: Whether the customer smokes (Yes/No)
• day: Day of the week
• time: Time of day (Lunch/Dinner)
• size: Number of people in the party

---

🧪 Libraries Used

• pandas
• numpy
• seaborn
• scikit-learn (SVR, GridSearchCV, LabelEncoder, OneHotEncoder, ColumnTransformer)
• matplotlib or seaborn (for optional visualizations)

🧼 Data Preprocessing

🔄 Feature Encoding

• Label Encoding: Applied to binary categorical features (sex, smoker, time)
• One-Hot Encoding: Applied to day using ColumnTransformer with drop='first' to avoid multicollinearity

📊 Feature and Target Separation

• X = ['tip', 'sex', 'smoker', 'day', 'time', 'size']
• y = total_bill

✂️ Train-Test Split

• Performed before encoding to avoid data leakage
• test_size=0.2, random_state=42

---

🤖 Model: Support Vector Regression (SVR)

✅ Training

• Model: SVR()
• Fitted on encoded training data
• Evaluated on test set

📈 Metrics

Initial SVR Results:

• R-squared Score: 0.5502
• Mean Absolute Error (MAE): 4.41

---

🔍 Hyperparameter Tuning

Using GridSearchCV

✅ Conclusion

This project showcases the application of SVR on real-world-like data, along with:

• Proper feature engineering (label + one-hot encoding)
• Avoiding data leakage
• Hyperparameter tuning for optimization While performance could still be improved with more complex models or feature engineering, this provides a strong foundation for regression modeling with scikit-learn.

📎 Author

Mai3Prabhu