1. LOGISTIC REGRESSION
Posted: Mon Apr 21, 2025 6:49 am
Logistic Regression is a supervised machine learning model. That is, it uses algorithms capable of learning from labeled data in order to find, on their own, the answers within that set of information.
Logistic Regression seeks to analyze the hungary mobile database between two numerical variables. In this way, it is possible to obtain useful results for predictive analysis and improve sales, pricing, supply and demand planning , among several other processes.
2. CLUSTERING
Clustering, in turn, is a type of unsupervised machine learning. Therefore, the data does not have labels, and the goal is to make the machines find information on their own that was not explicit.
In Clustering, data is grouped by a specific category. The machine does the work of identifying patterns and grouping the content according to this criterion.
Logistic Regression seeks to analyze the hungary mobile database between two numerical variables. In this way, it is possible to obtain useful results for predictive analysis and improve sales, pricing, supply and demand planning , among several other processes.
2. CLUSTERING
Clustering, in turn, is a type of unsupervised machine learning. Therefore, the data does not have labels, and the goal is to make the machines find information on their own that was not explicit.
In Clustering, data is grouped by a specific category. The machine does the work of identifying patterns and grouping the content according to this criterion.