- 👨🏽🦱 Pronouns, He/Him
- 🤩 Favorite Language: Python 🐍
- 🔬 Currently learning: 🧠 Neural Networks and Portuguese 🇵🇹
- 🪐 Love of all things science, and always willing to collobrate
- 🤝 I’m looking to collaborate on projects that will change the world for the better
Binary classification prediction model using NASA exoplanet archive database to predict wether the exoplanets
discovered lie within their habitabale zones. Various features from the host star and exoplanet used to determine habitlity.
Libraries Utilized: Sckit-Learn, Pandas, Astropy, Numpy, Matpotlib, Seaborn, Scipy, Imblearn, bs4
Time series data set from NASA CO2 dataset that was recorded on weekly basis by
scientist recorded in ppm as global average.Utilizing ARIMA, ARMA, and SARIMA models to make future predictions if CO2 levels stay the same
Libraries Utilized: Sckit-Learn, Pandas, Numpy, Matplotlib, Seaborn, StatsModel
Using various features about the home, location, and proximity to certain favorable locations, a linear regression model was used to created the most accurate prediction of these house in Kiings County, WA, USA. Accuracy was improved using feature engineering, one-hot encoding, and feature selction.
Libraries Utilized: Sckit-Learn, Pandas, Numpy, Matplotlib, Seaborn
Exploration of data in Yelp's API to help businesses determine where to setup a business. In this specific
case, the option of opening a Yoga studio in either New York City or London. First immersive project for
the Flatiron School Data Science Immersive Program.
Libraries Utilized: Pandas, Numpy, Matplotlib, Seaborn
Credit: Rafael Ferreira
Last edited on: 02/09/2022


