Introduction This project looks at the mergers and acquisitions of 30 publicly traded companies and attempts to determine the stock price at closing. M&As are incredibly difficult to assess, and while the company's instrinsic value and fundamentals play a significant role in predicting whether a merger will be "successful", public sentiment from Wall Street investors is another commonly referenced topic. Brainstorming for this project prompted two notable observations; data on M&As are often incomplete and highly inconsistent given the confidentiality behind these deals, and determining an appropriate dependent variable y for analysis presents a significant challenge (would most likely require an additional project on its own). The success of a merger could be measured various ways, but often times the unpredictability of management makes all the more challenging. Culture, reorganization, and leadership shake-up are all attributes that play an important role in the success of an M&A but are difficult to quantify. Although I do build and run a model in this proejct, the complexity around this subject urged me to focus primarily on data gathering and manipulation. Since one would most likely need to compose a dataframe with the attributes necessary to run an a useful analysis on Mergers and Acquisition, I believe this is a valuable first step. For a more balanced notebook between EDA, data manipulation, and models, I have a project that focuses on COVID19's impact on Post-Secondary Education below titled COVID19 Effects on Post-Secondary Education https://github.com/clozgil The Process My objective was to build a dataframe with useful attribtues from scratch. I found that three reports per company would have sufficient information to get started. Acquistion data (any and all information on the company's M&A) Financial ratios (data to determine the company's fundamentals) Stock information (data to gain insight into Wall Street sentiment) Since downloading, importanting, and cleaning each one of those files for each of the 30 companies would be cumbersome, I looped on all the data files using the OS module, simulteanously cleaning and merging each one of the files. However, for the purpose of this presentation, I will feature each one of my data cleaning techniques for one company - Apple. Data Sources For reference only. All necessary data for this project can be found in the data dictionary Acquisition data: https://www.capitaliq.com/CIQDotNet/my/dashboard.aspx * Financial ratios: https://www-mergentonline-com.pitt.idm.oclc.org/companyfinancials.php?pagetype=ratios&compnumber=46247&period=Quarters&range=50&Submit=Refresh&csrf_token_mol=3680683535 * Stock info: https://www-mergentonline-com.pitt.idm.oclc.org/equitypricing.php?pagetype=report&compnumber=46247 * (*) = Account required. University of Pittsburgh account used for access