https://github.com/mendez-luisjose/house-price-prediction-with-scikit-learn-streamlit-and-deployed-with-flask

House Price Prediction with Scikit Learn, Streamlit and Deployed with Flask
https://github.com/mendez-luisjose/house-price-prediction-with-scikit-learn-streamlit-and-deployed-with-flask

streamlit xgboost

Last synced: 22 days ago
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House Price Prediction with Scikit Learn, Streamlit and Deployed with Flask

• Host: GitHub
• URL: https://github.com/mendez-luisjose/house-price-prediction-with-scikit-learn-streamlit-and-deployed-with-flask
• Owner: mendez-luisjose
• Created: 2023-10-22T20:41:07.000Z (over 1 year ago)
• Default Branch: main
• Last Pushed: 2023-10-22T21:35:12.000Z (over 1 year ago)
• Last Synced: 2023-10-23T21:29:43.834Z (over 1 year ago)
• Topics: streamlit, xgboost
• Language: Jupyter Notebook
• Homepage:
• Size: 3.62 MB
• Stars: 1
• Watchers: 1
• Forks: 0
• Open Issues: 0
• Metadata Files:
  • Readme: README.md

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README

        
# House Price Prediction with Scikit Learn, Streamlit and Deployed with Flask

![](./assets/prev-1.gif)

## House Price Prediction with Scikit Learn, Numpy, Pandas, Streamlit and Deployed with Flask 

The Model was trained with Tabular House Prices Dataset and with the `XGBRegressor` Architecture. The Model predicts the Price of a given House, also the U.I. to select the parameters of the House was built with Streamlit and the API with Flask. 

## Run it Locally

Test it Locally by running the `app.py` file, built with `Streamlit`, and the `api.py` file with `Flask`. Remember first to run the `api.py` file, copy the http url and saved in the API variable of the `app.py` file, and uncomment the code lines.

## App made with Streamlit

```sh

streamlit run app.py

```

## Deployed with Flash

```sh

python3 api.py

```

![](./assets/prev-2.gif)

## Resources

- House Price Dataset: https://www.kaggle.com/datasets/shibumohapatra/house-price