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We create a model using the gradient boosting algorithm to cut down on the noise and improve performance. This work was done during an informal project under Prof. Yaganti while studying at BITS.
This repository is a partial fulfilment of the requirements for the module of MSIN0114: Business Analytics Consulting Project/Dissertation for UCL School of Management.
Develop a classification model that can accurately diagnose the presence of kidney disease in a person based on their medical test results. The model will then identify which factors are the most influential in determining a person's chances of developing kidney disease.
Feature selection is widely used in nearly all data science pipelines. Hence I have created functions that do a form of backward stepwise selection based on the XGBoost classifier feature importance and a set of other input values with the goal to return the number of features to keep in regard to a prefered AUC-score.
Explore the dynamics of US home prices over two decades using a robust Random Forest Regressor model. Achieving a 99.87% R2 score, uncover key factors influencing real estate trends