Applied preprocessing methods on IMDb movie reviews and utilized Naïve Bayes model to execute NLP to determine positive and negative movie reviews using PythonOptimized model and ranked top 3 out of 15 teams with highest text classification accuracy of 80%

· Built Multinomial Logistic Regression models on employee data to predict retirement age and their annuity preferences using Python

· Applied univariate feature selection on employee data to utilize influential characteristics in the model and achieved an A/E ratio of nearly 100%

· Facilitated company with forecasting disbursements, making pricing assumptions and cost-savings

· Generated business insights by creating interactive visualizations and dashboard via Tableau

Analyzed Airbnbs top customer score listings to find the main factors influencing customer reviewsCompared the performance of applying various data preprocessing approaches and machine learning models such as Naïve Bayes, Decision Tree, KNN, and Logistic Regression using Python and WEKAAchieved highest accuracy of 92.16% and based on findings, provided advice to Airbnb hosts on how to improve their listings and enhance customer experience

Established Principal Component Regression Analysis (PCA) model based on MRT traffic data to analyze reasons for low utilization and uneven distribution between MRT stations using R, SAS, and MATLABLed a team to deliver outstanding results and comprehensive research to win an Honorable Mention Award in Student Research Competition