A data mining model is a virtual structure that represents data grouped for predictive analysis. It is created by applying an algorithm to data, but it is more than an algorithm or a metadata container: it is a set of data, statistics, and patterns that can be applied to new data to generate predictions and make inferences about relationships.
1.
Data collection
defeine problem : The first step is to collect the data that will be used
to create the model. This data can come from a variety of sources, such as
customer transactions, website logs, or social media data.
2.
Data preparation: The
next step is to prepare the data for analysis. This may involve cleaning the
data, removing outliers, and transforming the data into a format that is
compatible with the data mining algorithm.
3.
Model
selection: The third step is to select the data mining algorithm that will
be used to create the model. There are many different data mining algorithms
available, and the best algorithm for a particular problem will depend on the
nature of the data and the desired outcome.
4.
Model
training: The fourth step is to train the model. This involves feeding the
data to the data mining algorithm and allowing the algorithm to learn the
patterns in the data.
5.
Model
evaluation: The fifth step is to evaluate the model. This involves testing
the model on a holdout dataset and assessing its accuracy.
6.
Model
deployment: The final step is to deploy the model. This involves making
the model available to users so that they can use it to make predictions or
decisions.