https://github.com/dimitriskatos/usa_price_houses_predictions

U.S.A. house prediction
https://github.com/dimitriskatos/usa_price_houses_predictions

eda gradient-boosting-regressor logistic-regression visualization

Last synced: 17 days ago
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U.S.A. house prediction

• Host: GitHub
• URL: https://github.com/dimitriskatos/usa_price_houses_predictions
• Owner: DimitrisKatos
• Created: 2024-06-18T16:46:21.000Z (7 months ago)
• Default Branch: main
• Last Pushed: 2024-06-19T03:05:02.000Z (7 months ago)
• Last Synced: 2024-06-19T22:24:02.828Z (7 months ago)
• Topics: eda, gradient-boosting-regressor, logistic-regression, visualization
• Language: Jupyter Notebook
• Homepage: https://www.kaggle.com/code/dimitriskatos/usa-price-house
• Size: 909 KB
• Stars: 2
• Watchers: 1
• Forks: 0
• Open Issues: 0
• Metadata Files:
  • Readme: README.md

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README

        
# USA_price_houses_predictions

## You can see this notebook and all my work in th following link on Kaggle ![https://www.kaggle.com/code/dimitriskatos/usa-price-house]

In this notebook we try to predict the price of the houses in different cities of U.S.A.

- At the beginning we clean the data

- We dive into the dataset and finding hidden patterns and inshights by doing E.D.A. We make many visualization to understand the features.

- We apply Feature engineering and the we apply Multiple Linear Regression to determine which of this feature are signigicant for the model. 

- The we try some machine learning algorithms such as Regularization Regresion, Decission Trees, Random Forest regressor and Gradient Boosting Regressor.