Data Science Immersive
London, England, United Kingdom
Completed a 12-week intensive, all-encompassing data science course with General Assembly. Capstone project:Can we actually predict market changes by analysing Reddit’s /r/wallstreetbets?- Tested the hypothesis that the subreddit /r/wallstreetbets is actually impacting market and index movements by scraping Reddit and market data for my given dates. - Key outcomes: I managed to build models that accurately classify positive and negative changes with the S&P 500 based on user sentiment in Reddit discussions.- Resampled all comments by day, marged our dataframe with market data from Yahoo Finance along the date index, ran classification models (LogisticRegression, RandomForest, DecisionTreeClassifier and KNeighborClassifer) on our data and analysed the ROC, AUC and Classification Reports to gauge the strength of my model. Ames Housing Data:- Ran regression and classification models on the Ames housing dataset (available on Kaggle) to predict housing prices as part of our individual course project (timeframe: 2 weeks). - Examined and analysed the residuals based on my best models to break down the differences in housing prices, explained by fixed characteristics versus unfixed characteristics as well as any ‘abnormal’ sales masquerading as outliers. Web Scraping Data Science Job Postings:- Project designed to scrape indeed.com to analyse what features predict salaries in the United States.- Ran classification models (LinearRegression, RandomForest, DecisionTree, BaggingClassifier and KNearestNeighbors) on the data to analyse the feature importance within my dataset and create the ROC and AUC curves for the models, to understand how well our model can predict the correct classes as well as how often.