Think back to your time at school, you had a lot of bad times but i’m sure that, with time and distance, you are proud of how you handled the situations.
And from those that you were not able to manage in the best way, you surely learned.
Data mining – regression example
clustering or segmentation
there are situations where it is necessary to segment or group a set of elements, such as customers, based on their attributes.
In this way, commercial strategies can be defined based on the segment in which a particular client is located.
Their attributes can be either personal (such as age) or based on their relationship with the organization (such as volume of purchases or type of products consumed).
Examples of this type of algorithms australia phone number list are kmeans or hierarchical clustering .
The objective in this case is to obtain clusters where the elements of the same cluster have the minimum possible distance and the maximum distance from elements from which they are different.
Data mining – clustering example
classification
classification algorithms usually work by training the model beforehand, which is why we are talking about supervised algorithms.
Examples of this type of algorithms would be support vector machines ( svm ) or decision trees (such as, for example, the rpart module of the ro code, multi-tree classification random forest).
Classification algorithms allow us to classify
new entries into a certain group based on the spam data previously trained model.
Below we can see the classification using. Decision trees of three sales representatives from a company. Based on the kilometers traveled. Visits, income and. Margin and the comparison of successes with the test data.
sometimes we try to find the existence of associations between elements
For example, in a supermarket it is possible to observe which products are purchased together with others, seeking define a quantity to relocate the products to favour an increase in sales by taking advantage of the leverage that some products or groups of them offer in relation to “similar” ones.
Examples of these algorithms are apriori and eclat .