Skip to content

Latest commit

 

History

History
65 lines (39 loc) · 3.28 KB

File metadata and controls

65 lines (39 loc) · 3.28 KB

House Price Prediction Using Linear Regression

Overview

Buying a home is one of the biggest investments most people make. But what drives the price of a house? This project aims to uncover the hidden patterns behind housing prices by analyzing various features that influence the sale price of homes. Using data analysis and predictive modeling, we provide insights into what potential buyers should focus on when evaluating a property.


Key Findings

1. Size Matters, But It’s Not Everything

The size of the house (measured by GrLivArea) is strongly correlated with sale price. Larger homes tend to sell for higher prices. However, there’s a limit—homes beyond a certain size (about 4,000 sq ft) don’t always command higher prices at the same rate.

  • Tip for Buyers: Bigger isn’t always better. After a certain size, the price increase slows down. Look at quality and location as well.

2. Quality Over Quantity

OverallQual, or overall quality of materials and finish, has a very strong correlation with sale price. In many cases, a high-quality smaller home may be priced similarly to a larger home with lower quality.

  • Tip for Buyers: Prioritize quality over square footage. A well-built home holds its value better over time.

3. Renovations Boost Value

While the year a house was built has some impact, the year it was remodeled has a much stronger effect on price. Homes with modern renovations are more attractive and priced higher.

  • Tip for Buyers: Renovated homes tend to command higher prices. Look for homes with updated kitchens, bathrooms, or other important areas.

4. Outdoor Features Count

Homes with well-maintained outdoor features like porches, decks, and garages tend to sell for more. Outdoor living spaces are becoming more valued, even if they’re not the first thing people consider.

  • Tip for Buyers: Don’t underestimate the value of a good outdoor space. It can significantly impact resale value.

Visualizing the Data

1. Distribution of Sale Prices

The distribution of sale prices shows that most homes fall between $100,000 and $300,000. A small percentage of homes sell for significantly higher prices, creating a slight skew towards the right.

Distribution of Sale Prices


2. Correlation of Key Features

The heatmap below illustrates the correlation between various features and the sale price. Features like GrLivArea and OverallQual show the strongest positive correlations with sale price.

Correlation Matrix of Numeric Features


Conclusion

This analysis highlights the importance of both size and quality in determining a house's value. However, buyers should focus on more than just square footage. Quality, recent renovations, and outdoor features play an equally significant role in determining price.

Whether you’re buying your first home or investing in a new property, these insights can help you make more informed decisions.


Tools Used

  • Python for data analysis
  • pandas, NumPy for data manipulation
  • scikit-learn for model building
  • matplotlib, seaborn for data visualization