A collection of samples from my independent and collaboartive projects.

Main Tools

• Python (NumPy, polars, Pandas, scikit-learn, Seaborn, Matplotlib, Plotly, Pymongo, NLTK, Gensim, Textblob)
• R (plumbr, plotly, ggplot2, dplyr, tidyr, caret, gganimate, rpart, data.table, reticulate, e1071)
• SQL (e.g., BigQuery, SQLite)

Feel free to contact me via LinkedIn
Current Resume

• NYC Tree Census Project (R and Python)
  • My main contributions:
    • Data collection and exploratory data analyses
    • Data cleaning and kNN data imputation
    • K-means Clustering (Unsupervised) and elbow method evaluation
    • Classification models (Supervised)
      • Logistic Regression, Naive Bayes, Decision Tree, Neural Network
      • Model evaluation using LIME
• COVID-19 Research Articles - NLP Project (R and Python)
  • My main contributions:
    • Exploratory data analyses
    • Data cleaning and text preprocessing
    • Text classification algorithms: LDA and sentiment analysis code
    • Interactive plotly visuals
  • Resulting publication (co-first author):
    • Dornick, C., Kumar, A., Seidenberger, S. Seidle, E., & Mukherjee, P. (2021) Analysis of Patterns and Trends in COVID-19 Research, Procedia Computer Science, 185, 302-310. https://doi.org/10.1016/j.procs.2021.05.032

• Network Data Visualization (Python and Gephi)
• Student conversion and retention: Interactive visuals and animated GIF (R)*
• NLP - Survey Data (Python)*
  • Text Cleaning
  • LDA (Unsupervised)
  • Sentiment Analysis
  • Word Frequencies by k-pairings (k=1,2,3)
• Dashboard Usage Analysis (R)*
  • Market Basket Analysis (Unsupervised)
  • Visualizations (parallel coordinates plots, frequency plots, network graphics)

*Real data are intentially excluded