Finance & Economics Datasets as the Resource
Data are required for machine learning to function. In the absence of data, models cannot be trained and no insights can be obtained. Fortunately, there are several places where you may find free datasets for machine learning synthesis.
The more data you have during training, the better, yet data by itself is not enough. Equally important is making sure the datasets are of good caliber and relevant to the task at hand. Make sure the datasets aren’t too big to start with. You should probably spend some time cleaning up the data if it has more rows or columns than the project requires.
It’s not surprising that the banking industry has embraced machine learning so enthusiastically. Finance and economics, in contrast to other areas where data may be more difficult to come by, provide a wealth of knowledge that is ideal for AI models that wish to forecast future events based on past performance results.
Financial, economic, and other datasets are accessible through Quandl which has two different formats for the data:
1. A time-series (date/time stamp)
2. Tables of numerical/sorted types, with strings available for those who require them.
This is an excellent resource for financial and economic data, encompassing everything from stock prices to commodities, and you can download either a JSON or CSV file depending on your desire.
The World Library is a priceless tool for anyone trying to understand global trends, and this data bank covers everything from population demographics to the most important metrics that matter in development work. You can access it whenever suits you because it’s free and open to everyone.
The ideal source for conducting extensive analysis is open data from the World Bank. To help you understand how nations throughout the world are doing on many fronts, the information it provides includes population demographics, macroeconomic data, and vital measures of development.