As an introduction, this blog will discuss an artificial model that allows predicting with exactitude the sales of cable modems and technology equipment. For this objective, the model needs a group of input variables, such as the day, the telecommunication client, the country, and the season. It is like a neuron, but it is artificial intelligence. Needless to say, it is essential for the future growth of the enterprise.
Knowing these six learning behaviors can help you learn quicker with better retention, which will improve your learning agility.
How to get this?
You need a regression model to make an artificial neuronal. What is it? It is a tool that gives you the probability of determining information.
How does this model of regression work?
This tool provides an output variable from the correlation analysis of the input data collection. The objective is to convert a date’s table to a continuous function y = f(x) that predicts the value from every x.
There are two types of models. On the one hand, if there is only one input variable and only one output variable. This is a model of simple regression. On the other hand, if there are more input variables, this is a model of multiple regression. This second case has a more significant number of interactions to analyze.
The multiple regression model analyzes the correlation between the input variables with the output variables and all other input variables.
How do you know if it is a good regression model?
This is a more critical step because if you want to know how good the model is, you need to compare the real input data and the information predicted. This comparison can be made in different forms. To continue, it’s essential to know that we will explain only one way to make this. This is called MSE (Mean Square Error) and consists of the rest between the real value and the predicted value.
How to build the model?
At first, we are going to select a group of predicted variables that are potential candidates. For example:
- A quarter of the year (1, 2, 3, and 4)
- The month (January, February, March, April, May, June, July, August, September, October, November, December)
- The telecommunication client (Client 1, Client 2, … and Client N)
- The country (Country 1, Country 2, … and Country N)
- Season (Spring, Summer, Fall, Winter)
- Product (Product 1, Product 2, … Product N)
- Number of monthly sales (variable to predict)
After that, it’s necessary to analyze all the variables that you think are relevant to the prediction.
Who or what chooses the best regression model?
Machine learning allows us to propose neural networks that solve exactly this process of selecting the “best” model of regression, that is, the one that predicts the number of sales per month with the lowest possible error.
The input neurons to the network will be your predictor variables, and the final output neuron will be our variable to predict (sales per hour).
To sum up, artificial intelligence allows you to work faster and better. In fact, how the blog explained initially is essential for the future growth of enterprises worldwide. Moreover, this intelligence is intelligence because it can learn and become better.
Knowing these six learning behaviors can help you learn quicker with better retention, which will improve your learning agility.
