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rachelsng/README.md

Hi there, I'm Rachel Sng

  • Thank you for visiting my Github portfolio! I am interested in the field of data science, data analytics and machine learning.
  • I am currently a data scientist!
  • I will be constantly updating my portfolio with projects that I have done on this learning journey. Open to any feedback and improvements! 😄

Portfolio

Project Name Repo
[Python, SQL] Detecting Brands Committing Ratings Fraud on Amazon with Network Analytics 🔗
[Python] Improving Starbucks Offer Targeting by Predicting Offer Success 🔗
[Python] Train Travel Demand Prediction 🔗
[Python] Multi-armed Bandits to Learn Best Website Layout 🔗
[SQL] Web App to Visualize Maritime Energy Efficiency Statistics 🔗

Packages Used

I am familiar with the following packages through various hands-on projects.

  • Python:
    • Data Preprocessing: pandas, numpy
    • Data Visualisation: matplotlib, seaborn
    • Causal Inference: statsmodels, linearmodels
    • Machine Learning: sklearn, xgboost, lightgbm
    • Deep Learning: keras/tensorflow, pytorch
    • NLP: nltk, texthero, fuzzywuzzy, BERT
    • Big Data: pyspark
  • R:
    • Data Preprocessing: dplyr, tidyr
    • Data Visualisation: ggplot2
    • Graph: igraph

Popular repositories

  1. Amazon-Fraud-Detection-with-Network-Analytics Amazon-Fraud-Detection-with-Network-Analytics Public

    [Python, SQL] Using network analytics (Python igraph package) to improve detection of brands engaging in online ratings fraud.

    Jupyter Notebook 1

  2. Train-Travel-Demand-Modelling-in-Python Train-Travel-Demand-Modelling-in-Python Public

    [Python] Building a demand model and correcting for reverse causation with 2-stage least squares regression (OLS in statsmodels, IV2SLS in linearmodels)

    HTML

  3. rachelsng rachelsng Public

  4. Improving-Starbucks-Offer-Targeting-with-Success-Prediction Improving-Starbucks-Offer-Targeting-with-Success-Prediction Public

    [Python] Predicting offer redemption success and expected spend to decide which promotions customers should be served to maximise overall return.

    Jupyter Notebook 1

  5. Multiarmed-Bandits-Website-Tuning Multiarmed-Bandits-Website-Tuning Public

    [Python] 4 multi-armed bandit algorithms are implemented to determine which one can most effectively determine the best website configuration that maximise signups.

    Jupyter Notebook

  6. WallStreetBets-Sentiment WallStreetBets-Sentiment Public

    Jupyter Notebook