What is Common with Machine learning and Economics?
Well, the short and clear answer is that machine learning and economics are based on data. We have two approaches: traditional, which is econometrics, and modern, which is machine learning. There is a lot of overlap between the two. Econometrics is basically statistics that are ready to answer economic questions. Machine learning has a similar purpose in economics, but with the use of large amounts of data. Also, machine learning in economics is not based on the same model as economics.
How Machine learning and Economics come together?
When an economist analyzes economic data, he tries to find the relationship between two dynamic and seemingly irrelevant conditions. For example, a real estate economist can compile immovable data to determine the size, location, or other factors that determine the value of a home that people are willing to pay.
A Machine Learning will not only help the economist to determine the relationship between the consumer and external factors but also to estimate the value of the house and what to expect from potential buyers.
Machine learning will not directly affect economic research but will help economists with research data and predictions. Machine learning is primarily useful in gathering new data resources. For example, economists have already been able to convert satellite data into economic growth estimates, as well as to measure neighborhood income levels in Boston and New York via Google Street View, Yelp, and Twitter.
Thanks to the Machine learning economics, current, and future products will meet market expectations and get better. Why do you say that? Machine Learning can not only help improve the quality of products and services but also help deliver more personalized products and a variety of them to consumers. In addition, new companies entering the market have been able to measure customer demand for some amazing precision products.
In economics, machine learning can analyze the tons of Data needed to make the right business decisions about introducing new products to the market or replacing existing ones. Even now every serious company does a lot of surveys and studies before making small changes in the product.
Forecasts and Predictions:
When it comes to predictions, standard econometric models give “overfit” patterns so the result can be misleading. Machine learning algorithms are very accurate and lack human feedback and decisions. In traditional econometrics, the more complex the model you are building on, the greater the difference and the less bias. So, you might expect a prediction error, sometimes small, sometimes big, but it always exists.
In general, this is what machine learning of economics is all about. To help increase productivity, improve productivity, and help predict the future by making reliable predictions about the economy, market, society, politics, or technology. But for a change, these predictions can be really reliable.
Current predictions are mostly based on one’s thinking, whether it is an individual or a company. This is not a reliable source. Future forecasts will be based on big data. The machine learning algorithm will analyze tens of thousands of gigabytes of data to find the most probable result or trend. It will no longer be based on “reading the tea leaves” so we can expect it to be quite accurate.
You may also like to read: 9 Common Machine Learning Problems