The 7 Key Steps To Build Your Machine Learning Model

Given the problem you want to solve, you will have to investigate and obtain data that you will use to feed your machine. The quality and quantity of information you get are very important since it will directly impact how well or badly your model will work. You may have the information in an existing database or you must create it from scratch. If it is a small project you can create a spreadsheet that will later be easily exported as a CSV file. It is also common to use the web scraping technique to automatically collect information from various sources such as APIs.

This is a good time to visualize your data and check if there are correlations between the different characteristics that we obtained. It will be necessary to make a selection of characteristics since the ones you choose will directly impact the execution times and the results. You can also reduce dimensions by applying PCA if necessary.

Additionally, you must balance the amount of data we have for each result -class- so that it is significant as the learning may be biased towards a type of response and when your model tries to generalize knowledge it will fail.

You must also separate the data into two groups: one for training and the other for model evaluation which can be divided approximately in a ratio of 80/20 but it can vary depending on the case and the volume of data we have.

At this stage, you can also pre-process your data by normalizing, eliminating duplicates, and making error corrections.

There are several models that you can choose according to the objective that you might have: you will use algorithms of classification, prediction, linear regression, clustering, i.e. k-meansor K-Nearest Neighbor, Deep Learning, i.e Neural Networks, Bayesian, etc.

There are various models to be used depending on the data you are going to process such as images, sound, text, and numerical values. In the following table, we will see some models and their applications that you can apply in your projects:

You will need to train the datasets to run smoothly and see an incremental improvement in the prediction rate.