Big Data in Finance Final Project, audited at NTUT.
• Determined the purpose of data analysis and picked the required variables.
• Implemented data processing including data cleaning and data clustering.
• Built the model by using the FP-growth algorithm in RapidMiner.
Market Analysis for Umbilical Cord and Stem Cell Storage in TaiwanWayne Wei
Outlined:
1. Operation Process
2. Analysis for Economic and Environment aspect
3. Market and Customer research
4. Benchmark: Baby Bank- A SWOT Analysis
WSDM - KK box's churn prediction challenge YunRouLai
Data Structures and Algorithms Final Project
• Analyzed and predicted whether customers will churn based on KKBox's dataset.
• Finished data cleaning and data reduction before data analysis.
• Employed different machine learning models to predict the outcome, including XGBoost, Random Forest, and, lastly, compared them.
We analyzed hundreds of millions of data from Chunghwa Post for decades and found out pain points of the delivery by the visualization tool in Power BI. In the end, we discovered the reason for the waste of manpower was that many people were not at home when postmen are in delivery. In order to reduce costs, we found some ways out.
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Market Analysis for Umbilical Cord and Stem Cell Storage in TaiwanWayne Wei
Outlined:
1. Operation Process
2. Analysis for Economic and Environment aspect
3. Market and Customer research
4. Benchmark: Baby Bank- A SWOT Analysis
WSDM - KK box's churn prediction challenge YunRouLai
Data Structures and Algorithms Final Project
• Analyzed and predicted whether customers will churn based on KKBox's dataset.
• Finished data cleaning and data reduction before data analysis.
• Employed different machine learning models to predict the outcome, including XGBoost, Random Forest, and, lastly, compared them.
We analyzed hundreds of millions of data from Chunghwa Post for decades and found out pain points of the delivery by the visualization tool in Power BI. In the end, we discovered the reason for the waste of manpower was that many people were not at home when postmen are in delivery. In order to reduce costs, we found some ways out.
Machine Learning Final Project.
• Predicted the fare amount (inclusive of tolls) for a taxi ride in New York City given the pickup and dropoff locations.
• Finished data cleaning and data reduction before data analysis.
• Employed different machine learning models to predict the outcome, including XGBoost, AdaBoost and, lastly, compared them.
Quantity forecast of bus and minibus on national road no. 5YunRouLai
Linear Programming Final Project, data from TISV, Traffic Information Service.
• Reptiled the data from Traffic Information Service, M03A, the time interval is 2016/6/14-2018/6/14.
• Implemented data consolidation and data clustering. Further, picked the outliers by data visualization.
• Found out the other three variables which are matched dependent variable's data format.
• Employed different Linear Regression models to predict the outcome, and looked over whether it is the collinear condition by calculating variance inflation faction.
• Predicted all the variables through ARIMA.
• Printed the outcome by putting predictands of variables into the regression model.
Financial Computer Programming Final Project, negative examples of losing money.
• Generated strategies for algorithmic trading.
• Wrote models in R for stock selection.
• Compared to the profits of the subjects.
• Utilized data backtesting to make sure the program code is right.
Analysis of how family background have an effect on self confidence of children.YunRouLai
Big Data Analytics and Applications Final Project, finding out how variables influence the dependent variable.
• Data based on the 2010 Basic questionnaire at NTU, a total of 1030 people.
• Implemented data cleaning and added the feature field on excel.
• Imported the final data set in python and maked exploratory data analysis with the Pandas package and the Matplotlib package.
• Plotted cross-tabulation and produced a predictive model by using Logistic regression.
