I have a couple of datasets that I scraped myself with Python. One is a list of countries with a few figures on how easy they are to work with from a business stand point (days items are in customs, strength of the USD in that country, etc). The other a list of those same countries with a row for each product they have available.
My goal is to find which country is the best target for my company to target based on the was of working with the country and the breadth of products they have available. I’d like to also be able to flip the script and see which products are the most common across the countries.
I’m not sure the best method to go about this. I’ve been researching various analytics methods f or the past few days and nothing really seems to fit. If anyone has any suggestions, in all ears.
First thing to do after cleaning the data is exploratory data analysis and data visualization. From charts and graphics we can have more insights of data as compared to fancy machine learning models.
try interactive data visualization using plotly package.
I would like to suggest you, FIRST: Business Analytic : e.g., https://study.deakin.edu.au/course/business-analytics/?gclid=EAIaIQobChMIk-zqxu2o5wIVhDUrCh0AnAMOEAAYASAAEgIqV_D_BwE&ef_id=EAIaIQobChMIk-zqxu2o5wIVhDUrCh0AnAMOEAAYASAAEgIqV_D_BwE:G:s&s_kwcid=AL!855!3!190658236032!b!!g!!%2Bbusiness%20%2Banalytics%20%2Bcourse
https://www.amazon.com.au/Python-Business-Intelligence-Cookbook-Dempsey-ebook/dp/B0166Y6YMG
Genetic Algorithm, e.g., https://towardsdatascience.com/genetic-algorithm-implementation-in-python-5ab67bb124a6
and https://github.com/DEAP/deap
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