https://github.com/prashver/house-prices-prediction

Utilizing the House Prices Dataset , this project predicts home prices through a Jupyter notebook-based data science pipeline. It includes exploratory data analysis, cleaning, feature engineering, and modeling. The project explores diverse aspects of residential homes to understand price influences beyond traditional factors.
https://github.com/prashver/house-prices-prediction

machine-learning matplotlib numpy pandas regression-models scikit-learn seaborn

Last synced: about 12 hours ago
JSON representation

Utilizing the House Prices Dataset , this project predicts home prices through a Jupyter notebook-based data science pipeline. It includes exploratory data analysis, cleaning, feature engineering, and modeling. The project explores diverse aspects of residential homes to understand price influences beyond traditional factors.

• Host: GitHub
• URL: https://github.com/prashver/house-prices-prediction
• Owner: prashver
• Created: 2022-05-14T21:27:05.000Z (over 2 years ago)
• Default Branch: master
• Last Pushed: 2022-11-30T20:04:12.000Z (about 2 years ago)
• Last Synced: 2024-11-14T17:12:04.390Z (2 months ago)
• Topics: machine-learning, matplotlib, numpy, pandas, regression-models, scikit-learn, seaborn
• Language: Jupyter Notebook
• Homepage:
• Size: 937 KB
• Stars: 2
• Watchers: 2
• Forks: 0
• Open Issues: 0
• Metadata Files:
  • Readme: README.md

Awesome Lists containing this project

README

        
# House-Prices-Prediction

Ask a home buyer to describe their dream house, and they probably won't begin with the height of the basement ceiling or the proximity to an east-west railroad. But this playground competition's dataset proves that much more influences price negotiations than the number of bedrooms or a white-picket fence.

With 79 explanatory variables describing (almost) every aspect of residential homes in Ames, Iowa, this competition challenges you to predict the final price of each home.

In a form of a jupyter notebook, my solution goes through the basic steps of a data science pipeline:

- Exploratory data analysis with visualizations

- Data cleaning

- Feature engineering

- Modeling

### Dataset used: [House Prices Dataset](https://www.kaggle.com/competitions/house-prices-advanced-regression-techniques/overview)