https://github.com/phenomsg/house-prices---advanced-regression-techniques

Predict house prices in Ames, Iowa, using 79 features, evaluated by RMSE of log-transformed predicted and actual prices.
https://github.com/phenomsg/house-prices---advanced-regression-techniques

python3 pytorch pytorch-implmention regression-analysis

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Predict house prices in Ames, Iowa, using 79 features, evaluated by RMSE of log-transformed predicted and actual prices.

• Host: GitHub
• URL: https://github.com/phenomsg/house-prices---advanced-regression-techniques
• Owner: PhenomSG
• License: mit
• Created: 2024-09-09T13:56:46.000Z (9 months ago)
• Default Branch: main
• Last Pushed: 2024-09-12T18:41:52.000Z (9 months ago)
• Last Synced: 2024-10-06T02:06:01.508Z (8 months ago)
• Topics: python3, pytorch, pytorch-implmention, regression-analysis
• Language: Jupyter Notebook
• Homepage:
• Size: 179 KB
• Stars: 0
• Watchers: 1
• Forks: 0
• Open Issues: 0
• Metadata Files:
  • Readme: README.md
  • License: LICENSE

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README

        
# House Prices - Advanced Regression Techniques

This project focuses on predicting house sale prices using a comprehensive dataset of residential homes in Ames, Iowa. The dataset contains 79 explanatory variables that describe various aspects of the homes, such as size, location, quality, and condition.

![Main Image](house.png)

## Project Overview

The goal of this project is to predict the sales price for each house based on its features. The predictions will be evaluated using Root-Mean-Squared-Error (RMSE) between the logarithm of the predicted and actual sale prices. This approach ensures that errors in predicting both expensive and affordable houses are treated equally.

## Dataset

The dataset includes various features about each house, including:

- **Lot size and area**

- **Number of bedrooms and bathrooms**

- **Year built and remodeled**

- **Garage type and size**

- **Quality of materials**

- **Neighborhood and location features**

These features allow us to build a model that can capture the various factors influencing house prices.

## Evaluation Metric

The model's performance is measured by RMSE between the logarithmic values of the predicted and actual sale prices. This ensures a balanced evaluation for houses of different price ranges.

## Acknowledgments

The Ames Housing dataset was compiled by Dean De Cock and is widely used in data science education as an alternative to the Boston Housing dataset.