The purpose of this project would be to analyze acquisitions that Alphabet, Google's parent company, has made since 2015. This would allow companies seeking to be acquired by a large company like Alphaebet to estimate hwat their potential valuation would be.
The dataset can be found here: Mergers and Acquisitions Dataset
data_dict = {'date': 'acquisition date', 'acquired company': 'the merger company', 'acquiring company': 'specific Alphabet subsidiary that acquired/merged with the acquired company', 'business': 'industry of the acquired company', 'country': 'country the acquired company is located in', 'price': 'valuation price'}
data_dict
{'date': 'acquisition date',
'acquired company': 'the merger company',
'acquiring company': 'specific Alphabet subsidiary that acquired/merged with the acquired company',
'business': 'industry of the acquired company',
'country': 'country the acquired company is located in',
'price': 'valuation price'}
How exactly the data would be organized is still up for considering, but the basic premise is that the date of acquistion, the industry the acquired company is in, the country acquired company is based out of and how much the acquistion was valued out would allow us to perform regression machine learning to estimate the valuuation of another company based on its country, the date of the acquistion, and the industry it's in.