Cloud Hosting

Using the flexible CRISP-DM phases, data teams can move back and forth between stages as needed. Also, software technologies can do some of these tasks or support them.

The data scientist or data miner starts by identifying project objectives and scope. They collaborate with business stakeholders to identify certain information.

They then use this information to define data mining goals and identify the resources required for knowledge discovery.

Once they understand the business problem, data scientists begin preliminary analysis of the data. They gather data sets from various sources, obtain access rights, and prepare a data description report. The report includes the data types, quantity, and hardware and software requirements for data processing. Once the business has approved their plan, they begin exploring and verifying the data. They manipulate the data using basic statistical techniques, assess the data quality, and choose a final data set for the next stage.

Data miners spend the most time on this phase because data mining software requires high-quality data. Business processes collect and store data for reasons other than mining, and data miners must refine it before using it for modeling. Data preparation involves the following processes.

For example, handle missing data, data errors, default values, and data corrections.

For example, combine two disparate data sets to get the final target data set.

For example, convert data types or configure data for the specific mining technology being used.

Data miners input the prepared data into the data mining software and study the results. To do this, they can choose from multiple data mining techniques and tools. They must also write tests to assess the quality of data mining results. To model the data, data scientists can:

After creating the models, data miners start measuring them against the original business goals. They share the results with business analysts and collect feedback. The model might answer the original question well or show new and previously unknown patterns. Data miners can change the model, adjust the business goal, or revisit the data, depending on the business feedback. Continual evaluation, feedback, and modification are part of the knowledge discovery process.

During deployment, other stakeholders use the working model to generate business intelligence. The data scientist plans the deployment process, which includes teaching others about the model functions, continually monitoring, and maintaining the data mining application. Business analysts use the application to create reports for management, share results with customers, and improve business processes.

Follow this link:

What is Data Mining? Data Mining Explained - AWS