#data project

*This project was never completed. It was a group Project for my Data Visualizations class, but my teammate and I were not able to see eye to eye. This is the work I contributed to the project. *

For almost a century, economists have tried to theorize the stock market trend. They could not find any relevant, predictable pattern and concluded that the stock market follows a random walk pattern. The 2013 Nobel Prized Fama developed the most famous theory in the 1970s. This theory is called the efficient market hypothesis (EMH), which asserts that information is fully incorporated into the price. In other words, stocks do not display any relevant information that can be used for predictions. However, some investors have made a fortune from the stock market, contradicting Fama’s theory.

In this project, we would like to verify the validity of the EMH. Under such theory, financial ratios could not be used for prediction. Yet, many financial advisors recommend the use of some. In this project, we will use the most widely used financial ratio to see if they impact the market trend.

To answer our questions, we will use a data set collected from Kaggle. The data set contains 4,000 listed stocks with their financial ratios. In total, they have 200 financial ratios, which is all the financial ratios that are out there. The time span of the data ranges from 2014 to 2018. It can be accessed using the following link:

Research shows that ICUs can put people with chronic illnesses in worse conditions than when they entered. Though it may not be immediately apparent, some studies have shown that the ICU can have a negative effect on patients later. We want to see if this applies to the different conditions in this dataset.

We want to answer the question: Does gender impact ICUs admission rates and the likelihood of re-admittance for individuals with different Chronic Illnesses?

The goal of this data analysis is to gather a deeper understanding of why Donald J. Trump won the 2016 elections. From this data, we hope to see if any inferences can be made to help candidates prepare for the 2020 elections. Our analysis proposes that there are 3 factors that led to Donald J. Trump winning the election which includes : Publicity, Hate Crime, & Unemployment rates (which we are calling environmental factors).

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