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The project predicts the final price of each home in the Kaggle dataset by using different Machine Learning algorithms, Preprocessing and Parametrisation for this Kaggle Competition: https://www.kaggle.com/c/house-prices-advanced-regression-techniques

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robin-bista/HousingPricePrediction

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HousingPricePrediction

Code Folder

This folder contains the python code used to predict the prices of house with various models: Gradient Boosting Regressor(GBR), XGBoost(XGB), Random Forest(RF), K-Nearest Neighbour(KNN), AdaBoost(ADA), and Lasso. This project was completed in 2 phases. Checkpoint1.py was first phase and final_project.py is the complete project.

Doc Folder

Contains documentation and findings. All the above mentioned models are compared and the most accurate model was selected.

Images Folder

Contains the images and screenshots that were used during the documentation.

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The project predicts the final price of each home in the Kaggle dataset by using different Machine Learning algorithms, Preprocessing and Parametrisation for this Kaggle Competition: https://www.kaggle.com/c/house-prices-advanced-regression-techniques

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