hk_ipo_prediction
Predict first day performance of Hong Kong IPO stocks: A pipeline example of machine learning projects.
Procedures:
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Crawl, Parse and Clean Hong Kong IPO data from AAStocks.com using selenium webdriver and phantomjs (around 400 data points).
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Use pandas for data cleaning and feature engineering, including feature selection and handling big values, missing values and categorical values (one hot encoding)
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Use xgboost for regression model to predict first day performance. Generated feature importance plot is very interesting.