🏡 House Prices Exploratory Data Analysis (EDA)

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📊 Overview

Discover the hidden patterns behind what drives house prices!
This project performs an in-depth exploratory data analysis (EDA) on the Kaggle House Prices - Advanced Regression Techniques dataset using Python and popular data science libraries.

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📁 Dataset

• Rows: 1,460   |   Columns: 81
• Source: Kaggle Competition
• Target Variable: SalePrice

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🚀 Quick Start

1. git clone https://github.com/Rivu5555/House-Regression.git cd house-prices-eda

2. Install dependencies: pip install -r requirements.txt

3. Run the Notebook: jupyter notebook house_prices_eda.ipynb

   If the data file is not present, download it here and place it in the input/ folder.

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🧐 EDA Highlights

• Target Distribution:
  

• Missing Value Analysis:
  Bar plots to visualize missing data columns.

• Key Feature Relationships:

• GarageArea vs SalePrice

• OverallQual vs SalePrice

• SaleType impact

• Visual Insights:

• Histograms, KDE plots, Boxplots

• Scatter plots for feature relationships

• Major Findings:

• Higher OverallQual and GarageArea often predict higher SalePrice

• Certain SaleTypes are linked to price outliers

• Notable missing data in some categorical features

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📦 Requirements

• Python 3.8+

• pandas

• numpy

• matplotlib

• seaborn

• 🤝 Contribution

Pull requests are welcome! For major changes, open an issue first to discuss what you would like to change.

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📚 References

• Kaggle Competition Page
• Seaborn EDA Reference

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Happy Analyzing! 🏠📈