🏠 Kaggle House Prices Prediction

A machine learning solution for the Kaggle House Prices: Advanced Regression Techniques competition, predicting residential home prices in Ames, Iowa.

📋 Description

This repository contains a comprehensive solution for predicting house prices using regression techniques. The implementation leverages multiple machine learning models in an ensemble approach to achieve high prediction accuracy.

The project addresses the Kaggle House Prices competition, which challenges participants to predict the sale price of homes in Ames, Iowa based on 79 explanatory variables covering almost every aspect of residential properties.

🔍 Features

• Extensive Data Analysis: Includes correlation analysis, outlier detection, and feature importance evaluation
• Data Preprocessing: Handles missing values, categorical encoding, and feature engineering
• Model Ensemble: Combines multiple regression models for improved prediction accuracy:
  • Lasso Regression
  • Ridge Regression
  • Gradient Boosting Regressor
  • Elastic Net
• Model Stacking: Implements a stacked model approach with cross-validation
• Feature Engineering: Creates new features to improve model performance
• Outlier Handling: Identifies and addresses outliers in the dataset

🛠️ Setup Guide

Prerequisites

• Python 3.x
• Required libraries:
  • pandas
  • numpy
  • scikit-learn
  • matplotlib
  • seaborn
  • scipy

Installation

1. Clone this repository
2. Install the required dependencies:

   pip install pandas numpy scikit-learn matplotlib seaborn scipy

📊 Usage

1. Ensure the dataset files (train.csv and test.csv) are in the repository root directory
2. Run the main script:

   python housesPredictPrices.py

3. The script will generate predictions in test_set_prediction.csv

🧮 Implementation Details

Data Preprocessing

• Missing Value Handling: Implements sophisticated strategies for handling missing values based on domain knowledge
• Feature Transformation: Applies log transformation to skewed numerical features
• Categorical Encoding: Converts categorical variables to numerical using ordinal encoding and one-hot encoding

Model Architecture

The solution employs a stacked ensemble approach:

1. Base Models:

   • Lasso Regression with alpha tuning
   • Ridge Regression
   • Gradient Boosting Regressor
   • Elastic Net

2. Stacking Layer:

   • Uses a Lasso model to combine predictions from base models
   • Implements k-fold cross-validation (k=20) for robust evaluation

Feature Engineering

• Creates a TotalSF feature combining basement, first floor, and second floor square footage
• Develops a Total_Home_Quality feature from quality and condition ratings
• Bins year-related features to capture temporal patterns
• Generates squared terms for important numerical features

📈 Results

The implementation achieves competitive results on the Kaggle leaderboard by:

• Effectively handling the complex relationships between features
• Addressing outliers and skewed distributions
• Leveraging the strengths of multiple regression algorithms
• Using cross-validation to ensure model robustness

📄 License

This project is licensed under the MIT License - see the LICENSE file for details.

🔗 Resources

• Kaggle Competition Page
• Ames Housing Dataset Documentation