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Marina Mukhranski's Projects

d3 icon d3

Bring data to life with SVG, Canvas and HTML. :bar_chart::chart_with_upwards_trend::tada:

etl_project icon etl_project

This project implements an ETL pipeline to extract, transform, and load crowdfunding data. Data is extracted and transformed into CSV files, used to generate an ERD and table schema, and then loaded into a Postgres database.

home_sales icon home_sales

SparkSQL is used to analyze and process home sales data. The goal is to determine key metrics about the data, such as average prices for different types of homes and the relationship between price and view ratings. SparkSQL is used to create temporary tables, partition the data, and cache and uncache tables to improve performance.

pandas_city_schools_analysis icon pandas_city_schools_analysis

This project involves analyzing school and standardized test data to identify trends in school performance using Pandas and Jupyter Notebook. It aims to provide a comprehensive overview of school performance, including district and school summaries, top and bottom performing schools, and performance by grade level, spending range, and school type.

python_api icon python_api

This project aims to analyze and visualize weather data from over 500 cities using Python. It utilizes the OpenWeatherMap API and Geoapify API to create scatter plots and maps.

same-sex-marriage-in-european-countries icon same-sex-marriage-in-european-countries

The problem I was trying to solve was to fill a gap in information on how non-resident same-sex couples can get married in the countries where same-sex marriage is legal. There was no single source of information, I decided to utilize crowdsourcing to gather data and make a database of reviews. The other features are moderation page to accept or reject posted reviews, interactive map with links to resources.

seattle_traffic_analysis_project icon seattle_traffic_analysis_project

This project aims to analyze foot traffic and bike traffic on the Burke-Gilman Trail north of NE 70th St in Seattle. The goals are to: Analyze trends in bike traffic in Seattle over time. Identify peak traffic times and days. Understand seasonal patterns in foot and bike traffic. Develop interactive visualizations that show these relationships.

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