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Definition of Reputation Credits
Laurence Lin
Enthusiastic data scientist, willing to bring up AI solutions that would solve human life problems. Highly cooperative teamwork and curious about data insight information. Familiar with Python and SQL language, experienced of image processing and time series forecasting. Understand basic web development and RESTful API with Flask or FastAPI framework.
Taichung city，Taiwan
Cell Phone: 0916-067399
Github: https://github.com/laurence-lin
LinkedIn:https://www.linkedin.com/in/laurence-lin-836412133/
Medium: https://lawrence123.medium.com/
Working Experience
Machine learning engineer (2020/09/01 ~ current)
Hamastar Technology Corporation
Build data science application platform for data science developers and end users
Develop data science solutions for web layout defect detection, save 70% of time for developers to examine the product
Skills
Projects
Retail Store Location Selection by Popularity Ranking: Collect geographic features from Foursquare API and Google Place API, use EDA for data analysis and preprocessing, build predictive model to rank the store popularity, and found the main characteristics that effects most of the retail store's popularity. Achieve 0.83 in NDCG@k metric comparable with previous research.
Methods: EDA, Google Place API, Foursquare API, SVR, Linear Regression, Neural Network
link: https://github.com/laurence-lin/Retail-Store-Location-Ranking
Credit Card Fraud Detection: Predict the probability for credit card data fraud, apply feature engineering for data cleaning and transformation, use cross validation and ROC_AUC as evaluation for model performance.
Methods: EDA, LightGBM, Cross Validation, Logistic Regression
link: https://github.com/laurence-lin/Kaggle_competition/blob/master/FraudDetect/FraudDetection.ipynb
Projects
Retail Store Location Selection by Popularity Ranking: Collect geographic features from Foursquare API and Google Place API, use EDA for data analysis and preprocessing, build predictive model to rank the store popularity, and found the main characteristics that effects most of the retail store's popularity. Achieve 0.83 in NDCG@k metric comparable with previous research.
Methods: EDA, Google Place API, Foursquare API, SVR, Linear Regression, Neural Network
link: https://github.com/laurence-lin/Retail-Store-Location-Ranking
Credit Card Fraud Detection: Predict the probability for credit card data fraud, apply feature engineering for data cleaning and transformation, use cross validation and ROC_AUC as evaluation for model performance.
Methods: EDA, LightGBM, Cross Validation, Logistic Regression
link: https://github.com/laurence-lin/Kaggle_competition/blob/master/FraudDetect/FraudDetection.ipynb
Education
IBM Data Science Professional(Certified)
Coursera, June 2020 ~ August 2020
Google Data Analytics(Certified)
Coursera, April 2021 ~ June 2021
National Cheng Kung University / Engineering Science / Bachelor's degree
國立成功大學工程科學系
National Cheng Kung University / Engineering Science / Master degree
國立成功大學工程科學所
Master thesis: PM 2.5 forecasting using LSTM model
Laurence Lin
Enthusiastic data scientist, willing to bring up AI solutions that would solve human life problems. Highly cooperative teamwork and curious about data insight information. Familiar with Python and SQL language, experienced of image processing and time series forecasting. Understand basic web development and RESTful API with Flask or FastAPI framework.
Taichung city，Taiwan
Cell Phone: 0916-067399
Github: https://github.com/laurence-lin
LinkedIn:https://www.linkedin.com/in/laurence-lin-836412133/
Medium: https://lawrence123.medium.com/
Working Experience
Machine learning engineer (2020/09/01 ~ current)
Hamastar Technology Corporation
Build data science application platform for data science developers and end users
Develop data science solutions for web layout defect detection, save 70% of time for developers to examine the product
Skills
Projects
Retail Store Location Selection by Popularity Ranking: Collect geographic features from Foursquare API and Google Place API, use EDA for data analysis and preprocessing, build predictive model to rank the store popularity, and found the main characteristics that effects most of the retail store's popularity. Achieve 0.83 in NDCG@k metric comparable with previous research.
Methods: EDA, Google Place API, Foursquare API, SVR, Linear Regression, Neural Network
link: https://github.com/laurence-lin/Retail-Store-Location-Ranking
Credit Card Fraud Detection: Predict the probability for credit card data fraud, apply feature engineering for data cleaning and transformation, use cross validation and ROC_AUC as evaluation for model performance.
Methods: EDA, LightGBM, Cross Validation, Logistic Regression
link: https://github.com/laurence-lin/Kaggle_competition/blob/master/FraudDetect/FraudDetection.ipynb
Projects
Retail Store Location Selection by Popularity Ranking: Collect geographic features from Foursquare API and Google Place API, use EDA for data analysis and preprocessing, build predictive model to rank the store popularity, and found the main characteristics that effects most of the retail store's popularity. Achieve 0.83 in NDCG@k metric comparable with previous research.
Methods: EDA, Google Place API, Foursquare API, SVR, Linear Regression, Neural Network
link: https://github.com/laurence-lin/Retail-Store-Location-Ranking
Credit Card Fraud Detection: Predict the probability for credit card data fraud, apply feature engineering for data cleaning and transformation, use cross validation and ROC_AUC as evaluation for model performance.
Methods: EDA, LightGBM, Cross Validation, Logistic Regression
link: https://github.com/laurence-lin/Kaggle_competition/blob/master/FraudDetect/FraudDetection.ipynb
Education
IBM Data Science Professional(Certified)
Coursera, June 2020 ~ August 2020
Google Data Analytics(Certified)
Coursera, April 2021 ~ June 2021
National Cheng Kung University / Engineering Science / Bachelor's degree
國立成功大學工程科學系
National Cheng Kung University / Engineering Science / Master degree
國立成功大學工程科學所
Master thesis: PM 2.5 forecasting using LSTM model