🏠 House Price Prediction using Model Validation & Hyperparameter Tuning

This project demonstrates how to improve machine learning models using validation techniques and hyperparameter tuning to reduce overfitting and improve generalization.

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📌 Overview

This project focuses on improving a house price prediction model by applying key machine learning techniques such as overfitting detection, cross-validation, and hyperparameter tuning.

The goal is to build a model that not only performs well on training data but also generalizes effectively to unseen data.

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📊 Dataset

• California Housing Dataset (from scikit-learn)

Features include:

• Median Income
• House Age
• Average Rooms
• Population
• Occupancy
• Latitude & Longitude

Target variable: Median House Price

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🛠️ Tools & Libraries

• Python
• Jupyter Notebook
• Pandas
• NumPy
• Matplotlib
• Scikit-learn

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🤖 Models Used

• Linear Regression (Baseline)
• Ridge Regression (Regularized)
• Decision Tree Regressor (Overfitting Analysis)
• Tuned Decision Tree (Final Model)

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⚙️ Techniques Applied

• Data Preprocessing & Feature Scaling
• Train-Test Split (80:20)
• Overfitting Detection (Train vs Test Performance)
• 5-Fold Cross Validation
• Hyperparameter Tuning using GridSearchCV

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📊 Final Results

Model	RMSE	R² Score
Linear Regression	0.7456	0.5758
Ridge Regression	0.7456	0.5758
Tuned Decision Tree	0.6389	0.6885

👉 The Tuned Decision Tree achieved the best performance by reducing overfitting and improving generalization.

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📉 Insights

• Decision Tree initially overfitted the data (very low training error, high test error)
• Cross-validation provided more reliable model evaluation
• Hyperparameter tuning significantly improved model performance

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🎯 Project Outcome

The Tuned Decision Tree model significantly improved performance by reducing overfitting and achieved the best balance between accuracy and generalization.

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📂 Project Files

• 📓 Notebook: Task3_Model_Validation.ipynb
• 📄 Report: Task3_Model_Validation_Report.pdf

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🚀 How to Run

1. Clone the repository
2. Open the notebook in Jupyter Notebook
3. Run all cells to reproduce results

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📚 Key Learnings

• Importance of detecting and controlling overfitting
• Role of cross-validation in reliable evaluation
• Impact of hyperparameter tuning on model performance
• Model selection should focus on generalization, not just accuracy

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👤 Author

Sahil Bhatti AI/ML Internship Task

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⭐ This project was completed as part of an AI/ML Internship Task.