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Project 1 - Predicting student exam performance to identify students who perform better and those that perform worse. This would allow funds to be allocated to the schools and school districts that need them the most.

Project 2 - Predicting Titanic passenger survival based off multiple variables. Ultimately, used a confusion matrix to summarize the success rate of the predictions of who survived and those who did not.

Project 3 - Predicting when the Los Angeles Dodgers should run a promotion and what promotion they should run to maximize attendance.

Project 4 - Visualizing worldwide airplane data to prove that flying is the safest mode of transportation, even though people, including myself, might be scared to fly. This hopefully will help in alleviating concerns.

Project 5 - Predicting who NBA teams should draft. This could help teams decide which players to draft to most help their team and avoid drafting players who could turn into busts. Ultimately, no real useful conclusions were drawn, but I was very happy with the work I put in, how quickly I failed, and the ethical issues taken into consideration.

Project 6 - Combining NBA data from flat files, a website, and an api to showcase my skillset of wrangling data from different sources and combining them into one source to conduct data analysis and EDA on.

Project 7 - Taking a chapter of Pride and Prejudice to create a dictionary of all the words. This involves utilizing text data, removing punctuation and capitalizations, in order to count the number of times each word is used in the chapter.

Project 8 - Text classifier where I took words and removed the punctuation and capitalizations in order to classify the words by part of speech.

Project 9 - Sentiment analysis where I took customer feedback in text data and categorized the comments as either positive or negative. This could help companies determine what is working well for them and those items that customers complain about.

Project 10 - Predicting Olympic Medalist performance. This was another example of failing quickly. The model for predicting who would not medal was quite good, but that is most likely the nature of and byproduct of what the Olympics are, there are not too many medalists. Nonetheless, I am proud of the work of cleansing and predicting, along with the ethical considerations.