🏡 House Prices Exploratory Data Analysis (EDA)

---

📊 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.

---

📁 Dataset

• Rows: 1,460   |   Columns: 81
• Source: Kaggle competition (House Prices – Advanced Regression Techniques)
• Target variable: SalePrice

---

🚀 Quick Start

1. Clone the repository -git clone https://github.com/rivu-intel45/house-prices-regression.git -cd house-prices-regression

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

3. Run the notebook -jupyter notebook Open house-regression.ipynb and run all cells.

4. Add data (if needed)
   If train.csv is not present, download it from Kaggle
   and place it in an input/ folder (or update the path in the notebook).

---

🧐 EDA Highlights

• Target distribution
  Distribution and spread of SalePrice, including skewness and outliers.

• Missing value analysis
  Bar plots to visualize columns with the highest proportion of missing data.

• Key feature relationships

• GarageArea vs. SalePrice

• OverallQual vs. SalePrice

• Impact of SaleType on prices

• Visual insights

• Histograms, KDE plots, and boxplots for numeric features

• Scatter plots for important feature–price relationships

• Major findings

• Higher OverallQual and GarageArea are strongly associated with higher SalePrice.

• Some SaleType categories correspond to notable price outliers.

• Several categorical features contain substantial missing values that need careful handling.

---

📦 Requirements

• Python 3.8+
• pandas
• numpy
• matplotlib
• seaborn

---

🤝 Contributions

Pull requests are welcome.
For larger changes, please open an issue first to discuss what you would like to add or modify.

---

📚 References

• Kaggle competition page
• Seaborn documentation

---

Happy analyzing! 🏠📈