Calvin Lim's Projects
Analysed dataset and developed multivariate regression models for house sales price prediction to determine property’s fair value and recommend ways to potentially maximise house sales prices for the city of Ames, Iowa, United States.
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My portfolio website: https://ca1vin1im.github.io
CICFlowmeter-V4.0 (formerly known as ISCXFlowMeter) is a network traffic Bi-flow generator and analyzer for anomaly detection that has been used in many Cybersecurity datsets such as Android Adware-General Malware dataset (CICAAGM2017), IPS/IDS dataset (CICIDS2017), Android Malware dataset (CICAndMal2017) and Distributed Denial of Service (CICDDoS2019).
Computer Vision (CV) deep learning (DL) capstone project to re-label videos, convert to images, crop faces, and develop EfficientNetB7-based Convolutional Neural Network (CNN) model, trained with dataset focused on Asian content and ethnicities, that correctly identifies deep fakes videos (excluding the audio component).
[CVPR 2020] A Large-Scale Dataset for Real-World Face Forgery Detection
deepfake dataset collected on the web for deepfake detection
Github of the FaceForensics dataset
Analysed trends in both the ACT and SAT participation rates, supplemented with extensive secondary research, to uncover and recommend potential strategies for improving 2020 ACT participation rates.
Notebooks and code for the book "Introduction to Machine Learning with Python"
A social networking service scraper in Python
Natural Language Processing (NLP) project to scrape 2 subreddits r/DotA2 and r/leagueoflegends using pushshift.io Reddit API, pre-process data, analyse most frequent words, and develop models for posts classification to 2 separate threads.
Created new features influencing mosquito breeding patterns, distilled insights regarding spread of West Nile Virus (WNV) as well as their mosquito vectors, and developed WNV outbreak prediction models for the City of Chicago, Illinois, United States.
Implementation of paper - YOLOv7: Trainable bag-of-freebies sets new state-of-the-art for real-time object detectors