René's URL Explorer Experiment


Title: Khin Yadanar Lin | Portfolio Website

Open Graph Title: Khin Yadanar Lin | Portfolio Website

X Title: Khin Yadanar Lin | Portfolio Website

Description: Data Science and Analytics Professional | London

Open Graph Description: Data Science and Analytics Professional | London

X Description: Data Science and Analytics Professional | London

Mail addresses
khinydnlin@gmail.com

Opengraph URL: https://khinydnlin.github.io/

Generator: Astro v4.0.2

direct link

Domain: khinydnlin.github.io

titleKhin Yadanar Lin | Portfolio Website
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twitter:urlhttps://khinydnlin.github.io/
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Links:

Khttps://khinydnlin.github.io/
Let's connect!https://www.linkedin.com/in/khinyadanarlin/
View projects on Github https://github.com/khinydnlin
Topic Modelling & Sentiment Analysis - Data Science Subreddit I crawled over 7,000 top posts and comments from the 'Data Science' subreddit using the Reddit API. I then applied text preprocessing and TF-IDF vectorization before training an LDA model to extract five key discussion themes. I also employed a lexicon-based sentiment analyser to guage the sentiment across different flairs. Python PRAW NLTK Tf-idf Vectorizer LDA Vader https://github.com/khinydnlin/topic_modelling_ds_subreddit/blob/main/README.md
False Fire Alarms Detection - London Fire Brigade In the UK, 42% of emergency incidents reported to rescue teams are false fire alarms, costing approximately £1 billion annually to address. In this project, I analysed spatial data using geopandas, and built a classifier to detect false alarms using techniques like Logistic Regression, KNN, Naive Bayes, and Random Forest. Python geopandas sklearn Classification Supervised Machine Learning https://github.com/khinydnlin/false_fire_alarms_detection/blob/main/README.md
Car Auction Price Predictions In this freelance project, I developed a ML pipeline to predict used car prices for a Myanmar-based car rental platform. I identified key factors influencing resale values through feature importance analysis and employed techniques such as Ridge Regression and Ensemble Methods. The model is demonstrated via a Flask web app. Python Statsmodels sklearn Flask Supervised Machine Learning https://github.com/khinydnlin/car_auction_price_predictions/blob/main/README.md
Inquiry Volume Forecasting - ARIMA Time Series Forecasting This project uses real-world business data from an anonymised company to forecast daily chat inquiry volumes on Facebook Messenger through ARIMA modelling. Specifically, I explored dynamic regression with ARIMA error terms method, to allow the inclusion of additional predictors such as digital ads spend and campaign data R ARIMA Time Series Forecasting Dynamic Regression https://github.com/khinydnlin/chat_inquiry_volume_forecasting/blob/main/README.md
View more projectshttps://khinydnlin.github.io/projects
What is Causal Inference? A beginner's guide to causal inference methods: randomized controlled trials, difference-in-differences, synthetic control, and A/B testing https://medium.com/towards-data-science/what-is-causal-inference-48c57d848242
Optimisation: Unpacking Queueing Theory in its Simplest Terms Have you ever found yourself waiting in line at a supermarket, a restaurant, or a bank, wishing your turn would come just a bit faster? https://medium.com/towards-data-science/optimisation-unpacking-queueing-theory-in-its-simplest-terms-484ad80be56c
Website Template https://astrofy-template.netlify.app/
Manuel Ernestohttps://manuelernestog.github.io
https://khinydnlin.github.io/
Homehttps://khinydnlin.github.io/
Projectshttps://khinydnlin.github.io/projects
CVhttps://khinydnlin.github.io/cv
https://calendly.com/khinydnlin-nxqk/30min
https://github.com/khinydnlin
https://medium.com/@khinydnlin_66752
https://www.linkedin.com/in/khinyadanarlin/

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