Pinned Repositories
codewars-Python
coursera-Databases-and-SQL-for-Data-Science-with-Python
coursera-Programming-for-Everybody
coursera-Python-Data-Structures
coursera-Using-Python-to-Access-Web-Data
CrowdEye
GDSC-ai-stock
leetcode-Python
ML-Forest-Fire-Prediction-with-Regression-and-Classification
Using both Regression and Classification to make a good prediction to an extreme imbalance dataset. Improve the R square from 0.01 to 0.73 for Regression and the Accuracy of Classification can up to almost 90%.
ML-Mathematical-and-Statistical-foundation-of-Shrinkage-method
In-depth exploration of the mathematical and statistical principles behind the shrinkage method. Using Lasso and Stepwise feature selection to select and classify an artificially expanded variable data set.
scfengv's Repositories
scfengv/codewars-Python
scfengv/coursera-Databases-and-SQL-for-Data-Science-with-Python
scfengv/coursera-Programming-for-Everybody
scfengv/coursera-Python-Data-Structures
scfengv/coursera-Using-Python-to-Access-Web-Data
scfengv/CrowdEye
scfengv/GDSC-ai-stock
scfengv/leetcode-Python
scfengv/ML-Forest-Fire-Prediction-with-Regression-and-Classification
Using both Regression and Classification to make a good prediction to an extreme imbalance dataset. Improve the R square from 0.01 to 0.73 for Regression and the Accuracy of Classification can up to almost 90%.
scfengv/ML-Mathematical-and-Statistical-foundation-of-Shrinkage-method
In-depth exploration of the mathematical and statistical principles behind the shrinkage method. Using Lasso and Stepwise feature selection to select and classify an artificially expanded variable data set.
scfengv/ML-Wine-Type-and-Quality-Classification
In wine type classification, seven distinct supervised learning classification models were evaluated. XGBoost emerged as the frontrunner with an impressive accuracy rate of 99.46%. However, when it came to the classification of wine quality, our approach diverged from established literature, presenting alternative perspectives and viewpoints.
scfengv/NLP-Sentiment-Classifier
Utilize and explore the mathematical foundation of Generative and Discriminative algorithms to build a Natural Language sentiment classifier with Twitter samples dataset.
scfengv/NLP_DL-Topic-Modeling-for-TVL-livestream-comments
這是一份和企業甲級排球聯賽（以下簡稱企排）合作的研究，旨在為企排 YouTube 直播留言建立一個主題模型以量化分析觀眾的討論話題，進而達到了解各主題隨時間的熱度 分佈及掌握觀眾的注意力，以利後續開發更多的商業用途。本文所使用的所有資料均源自於 企排 18 年所有直播場次的留言資料，資料分別透過五個預處理方式以評估模型表現。分類模型由三個分類器所構成，分別用來分類主要主題（閒聊、比賽、加油、轉播）、次要主題（將比賽細分為球員、球隊、裁判、教練、戰術）以及情緒分析，三者的量化評估分數 Area Under ROC Curve 高達 99.55 / 99.73 / 99.99，單句留言平均計算時間（計算於 Nvidia T4 GPU）為 0.044 seconds / sentence。
scfengv/Random-Walk-distribution
scfengv/scfengv
Hi there, check out some works I have done in my spare time
scfengv/Ship-Analysis
scfengv/Stock-Valuation
This project serves as a 5-year DCF model to evaluate companies' target prices, using Selenium to fetch stock information mainly from Yahoo Finance & Stock Analysis. The information includes everything needed to calculate DCF.