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  1. Universal Service Administrative Company (USAC) E-rate Program Analysis. Tech used: Sodapy, Socrata, Pandas, Numpy, Matplotlib, Seaborn, Plotly Dash.

    Jupyter Notebook

  2. Why DC Metro ridership is declining? Analyzing public transport trends. Various regression models comparison. Tech used: Python Pandas, Numpy, Scipy, Seaborn, Matplotlib.

    Jupyter Notebook 1 2

  3. Forked from VanessaVanG/GW-Project3

    Media bias analysis. Building the predictive model of mass media language using NLP (20 million articles). Tech used: MongoDB, Flask, D3.js, Pickle, Scikit-learn, Plotly, Pandas, Numpy.

    Jupyter Notebook

  4. Visualizing Geo Data of all Earthquakes from the past 7 days. Tech used: Leaflet.js, HTML, CSS, PHP


  5. mars Public

    Flask application with latest Mark data. Tech used: Flask, MongoDB, Splinter, Selenium, Pandas, BeautifulSoup, HTML/CSS, Heroku.


  6. SQL playground using Sakila DB. Query building exercise.

20 contributions in the last year

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Contribution activity

January 2022

yegorkryukov has no activity yet for this period.

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