Skip to content

README.md

Latest commit

 

History

History
107 lines (75 loc) · 2.67 KB

README.md

File metadata and controls

107 lines (75 loc) · 2.67 KB

🏠 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.

📌 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.

📊 Dataset

  • California Housing Dataset (from scikit-learn)

Features include:

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

Target variable: Median House Price

🛠️ Tools & Libraries

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

🤖 Models Used

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

⚙️ 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

📊 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.

📉 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

🎯 Project Outcome

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

📂 Project Files

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

🚀 How to Run

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

📚 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

👤 Author

Sahil Bhatti AI/ML Internship Task

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