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This project is part of the Coursera Advanced Data Science with IBM Specialization The following project aims to predict the AirBnB property prices in Singapore based on the data on 27 Feb 2020.

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Architectural Decisions Document.pdf
Architectural Decisions Document.pdf
 
 
Computer_Vision_using_VGG_Non_Augementation.ipynb
Computer_Vision_using_VGG_Non_Augementation.ipynb
 
 
Data_ETL_and_Feature_Engineering.ipynb
Data_ETL_and_Feature_Engineering.ipynb
 
 
Data_Exploration.ipynb
Data_Exploration.ipynb
 
 
Data_ModelDevelopment.ipynb
Data_ModelDevelopment.ipynb
 
 
Data_Model_Training.ipynb
Data_Model_Training.ipynb
 
 
Prediction_of_Airbnb_Prices_in_Singapore.ipynb
Prediction_of_Airbnb_Prices_in_Singapore.ipynb
 
 
Prediction_of_Airbnb_Prices_in_Singapore_Final.ipynb
Prediction_of_Airbnb_Prices_in_Singapore_Final.ipynb
 
 
README.md
README.md
 
 
listing_model.csv
listing_model.csv
 
 
listings.csv
listings.csv
 
 
mrt_lrt_data.csv
mrt_lrt_data.csv
 
 

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Advanced-Data-Science-Capstone-Project

This project is part of the Coursera Advanced Data Science with IBM Specialization The following project aims to predict the AirBnB property prices in Singapore based on the data on 27 Feb 2020. The presentation can be access via this link

The following predictor models were used in this project

  • Support Vector Regression
  • Random Forest Regressor
  • XG Boost
  • Light Gradient Boosting Model (Light GBM)
  • Stacked Model (Ordinary Least Squares Regressor + XG Boost + Light GBM)
  • Artificial Neural Network (ANN) using TensorFlow Library

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This project is part of the Coursera Advanced Data Science with IBM Specialization The following project aims to predict the AirBnB property prices in Singapore based on the data on 27 Feb 2020.

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