AboNady / House_Prediction

A Kaggle's competition - The main task is to predict the prices of the houses.

House Prediction

    Most of the work was data preprocessing, filtring and cleaning. I tried 3 different regression algorithms, Linear Regression, RandomForest amd XGBoost. and the score and the Mean Square Error were the following:

Tech Stack

• Python: Version 3.10

• Scikit-Learn: Version 1.1.2

• Pandas: Version 1.4.3

Details

• Feature engineering was the best thing I learned in this project; I dropped the nan columns because they were misleading for the classification algorithm. Then I replaced the nan values with the coulmn's mean.

• I splitted the dataset to 70% Train and 30% Test.

• I tried 3 different regression algorithms and the results was as following:

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Linear Reg 0.8233656237553196 RMSE : 0.048385

Random Forest 0.8443752818314662 RMSE : 0.045416

XGB Reg 0.8502656831659069 RMSE : 0.044549

Contributing

Contributions are what makes the open source community such an amazing place to learn, inspire, and create. Any contributions you make are greatly appreciated.

If you have a suggestion that would make this better, please fork the repo and create a pull request. You can also simply open an issue with the tag "enhancement". Do not forget to give the project a star! Thanks again!

License

Distributed under the MIT License. See LICENSE.txt for more information.

References

• The Kaggle competition

Contacts

• Via Email : [e-mail]
• Via LinkidIn : https://www.linkedin.com/in/abonady/