jonkwiatkowski,Jon Kwiatkowski,github

api-weather-analysis

Analyze data pulled from OpenWeather API and Google Places API to determine weather trends at varying distances from the equator. Use this information to find an ideal vacation location.

big-data

Perform the ETL process completely in the cloud and upload a DataFrame to an RDS instance. Then use PySpark and SQL to perform a statistical analysis of selected data.

biodiversity-dashboard

Use JavaScript and HTML to create a dashboard with several interactive visualizations.

climate-change-and-world-hunger

Created an interactive dashboard that analyzes world temperature and food supply over time. The dashboard contains a world food availability map, a country nutrition choropleth, and a food availability vs temperature graph.

deep-learning

Use machine learning and neural networks as well as the features in the provided dataset to create a binary classifier that can predict whether applicants will be successful if funded by a company.

jonkwiatkowski.github.io

This page contains various information about me including recent projects, education, and work experience.

leaflet

Use leaflet to to visualize a set of earthquake data.

matplotlib

Analyze data for a pharmaceutical company and use Matplotlib to create visualizations that present findings in a meaningful way.

pandas

Use pandas to analyze standardized test results in an effort to help a school district make strategic decisions regarding future budget allocation and priorities.

python-bank-poll-analysis

Use a python script to analyze a set of financial records for a bank as well as a set of poll data for a small town.

sql

Use SQL to perform data modeling, data engineering, and data analysis on a set of CSV files containing data from an employee research project conducted in the 1980s and 1990s.

sqlalchemy

Use Python and SQLAlchemy to perform basic climate analysis and data exploration of a climate database. Create a Flask app to display results.

supervised-machine-learning

Use given data to create machine learning models to classify the risk level of given loans.

tweet-sentiment-predictor

Trained and optimized four machine learning models to predict the sentiment of a tweet: Logistic Regression Classifier, Stochastic Gradient Descent, Multinomial Naive Bayes, and Light-Gradient Boosting. The completed website containing the predictor, word collage, project summary, and team page was deployed to Heroku.

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