Portfolio
Hello world, here are my humble beginnings of a portfolio:
🚗 ebay - A deeper analysis of a well know car dataset. I've managed to find a few more things among the rows.🌐 maps - Quick and easy intro to geopandas.💻 coders_survey - Analysis of coders survey. A battle of mean, median and mode functions.🎣 tricks - Mix of short and easy tricks or hacks.👾 : Star Wars - This one is all about the style... I've even plotted a death-star !🔡 : scraping data - Using BeautifulSoup to scrape budget data from Wikipedia pages.🎥 Fandango - Extended version of Fandango ratings analysis. Includes scraping movies budget data from wiki.🚙 ML car prices - Introduction to ML with k-nearest neighbors algorithm, I've extended the project with testing out multiple random seeds, checking many column combinations and different dataframe versions (based on cleaning techniques).🏠 : ML house prices - Building a linear regression model to predict house prices, using feature selection techniques, outlier removal and feature engineering.🚕 ML NYC taxi trips - An ongoing project involving importing large datasets of NYC taxi trips, merging them with other data, all while trying to fit under strickt memory limitations.
languages: Python
libraries: Pandas, Numpy, Matplotlib, Geopandas, Seaborn, Wikipedia, BeautifulSoup, scikit-learn, Dask
Adam Kubalica
