This repo includes all projects completed by me. To be continued...
Contents
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Energy_Score_Prediction.ipynb (In case of unable to load the .ipynb, the corresponding nbviewer page: Link)-------Use the energy data to build a model that can predict the Energy Star Score of a building and interpret the results to find the factors which influence the score.
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Kaggle competition (Santander Customer Transaction Prediction) --------Use gradient boosting, XGBoosting, lightGBM to builder a classifier, to identify who'll make a transaction. Got 89.98% accuracy, and ranked as top 22% in the competition. (Code will be organized and published soon...)
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Lending club loan data analysis-----------Predict whether or not loan will be default using the history data.
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Text classification using CNN -----Sentiment analysis on movie review dataset
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Movie recommendation system ----------RS, searching and ranking