🏠 House Price Prediction – Advanced Regression Techniques

Welcome to my implementation of the House Prices - Advanced Regression Techniques. This project leverages data preprocessing, feature engineering, and advanced machine learning techniques to accurately predict housing prices.

🚀 Project Overview

This notebook solves a supervised regression problem using the Ames Housing Dataset, which contains 79 explanatory variables describing (almost) every aspect of residential homes in Ames, Iowa.

The goal is to predict the SalePrice of each house in the test set.

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📦 Dataset Details

• train.csv – Training dataset with features and SalePrice (target)
• test.csv – Test dataset without SalePrice
• data_description.txt – Full metadata for all 79 variables
• submission.csv – Final prediction file

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🔧 Workflow Summary

✅ 1. Data Loading

• Loaded train.csv and test.csv using pandas

✅ 2. Preprocessing

• Combined train and test for unified cleaning
• Imputed missing values:
  • Mode for categorical
  • Median for numerical

✅ 3. Label Encoding

• Applied LabelEncoder to all object-type columns

✅ 4. Feature Engineering

Created new features that improved predictive power:

• TotalSF = Total livable square footage
• TotalBath = Combined bathrooms (full + half)
• Age = Years since built
• RemodAge = Years since last remodel
• HasGarage, HasPool = Binary flags

✅ 5. Modeling

• Used XGBoost Regressor (XGBRegressor)
• Applied log-transformation to the target (SalePrice) for normalization
• Evaluated with cross-validation RMSE

✅ 6. Submission

• Generated final predictions and saved to submission.csv

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📈 Model Performance

• Model Used: XGBoost
• Cross-Validation RMSE: 0.1270
• Target Transform: log1p + expm1 for SalePrice

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📁 Files Included

File	Description
House_Price_Prediction.ipynb	Full Colab notebook
train.csv / test.csv	Dataset files
submission.csv	Final prediction file

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🧠 Key Learnings

• Real-world handling of missing data
• Importance of feature engineering in boosting model performance
• How to combine preprocessing and modeling pipelines effectively
• Power of XGBoost in tabular regression tasks