Predicting apartment, house price using XGBoost, MLP methods.
MLP structure:
XGBoost structure:
Variables: -City, country, district -Dedicated area -Contract year and month -Transaction amount -Floor level -Year of construction -Whithin 600m of -Starbucks -Subway Station -Schools and daycare centers -Supermarkets, big stores -Hospital -Park Some of Data Correlation graphs:
Transaction volume, Year of construction:
Transaction amount, Floor level:
Transaction amount, Subway station:
By these graphs we can know how much these variables can affect transaction volume(price of the apartment)
RMSE value of the test batch(epoch=1000, learning rate=0.001 mini-batch=64):
To run you have to install: streamlit==1.10.0 sklearn==0.0 matplotlib==3.5.1 numpy==1.22.3 pandas==1.4.2 xgboost==1.6.1 openpyxl==3.0.9