Chee Hong Lam's Projects
Case study solutions for #8WeekSQLChallenge. All solutions were coded in PostgreSQL.
Analysis of A/B test results by an e-commerce company. Application of hypothesis testing to determining whether the company should implement the new web page it developed to increase users' conversion rate.
Customer segmentation using abc analysis.
Automatically generate meta descriptions using deep learning.
Cash book for recording all cash receipts and payments, including bank deposits and withdrawals.
Using cohort analysis to measure customer retention.
Credit card detection fraud using fastai.
Predictive modeling of customer response behavior in direct marketing.
Using machine learning methods to predict demand for bike sharing.
Employee attrition prediction using ktrain.
Setting a free delivery threshold.
Implemented an item-based collaborative filtering recommender system for a given user using Pearson’s R.
Customer segmentation using k-means clustering.
Affinity analysis for market basket recommendation. Implemented using the FP-Growth algorithm.
Exploratory and statistical analysis with marketing data.
Calculate CLV using the BG/NBD and Gamma-Gamma models.
Yearly budget worksheet for planning your personal budget. Includes categories for a single person budgeting needs. Ideal for monthly and yearly budget and expense tracking.
Predicting Uber and Lyft fares using ktrain.
Build a fastText product classification model that can predict a normalized category name for a product, given an unstructured textual representation.
Profanity detection using fastText.
Customer segmentation using rfm analysis.
Sentiment classification of shoppers' reviews using machine learning techniques.
Build a deep learning spam detection system for sms using ktrain.
Customer churn prediction in telecom using machine learning.
Emotion analysis of English tweets using machine learning approach.
Classifying the unstructured customer support tickets using pytorch lightning flash.
Build a multi-headed model that’s capable of detecting different types of of toxicity like threats, obscenity, insults, and identity-based hate.