Data Mining Process | Data Science | What is Data Mining ? | Data Mining Process Basic
Data preparation.
00:01
This stage involves collecting and cleaning the data, removing duplicates, and handling missing values.
00:09
The data is then transformed and formatted into a suitable form for analysis.
00:14
Data exploration.
00:16
This stage involves exploring the data to identify patterns, relationships and anomalies.
00:23
The goal is to gain a better understanding of the data and to identify potential areas of interest for analysis.
00:31
Model building This stage involves building a model or algorithm to analyze the data.
00:38
This could involve using techniques such as decision trees, regression analysis, clustering or neural networks.
00:46
The goal is to develop a model that accurately represents the data and can be used to make predictions or identify patterns.
00:55
Model evaluation.
00:57
This stage involves evaluating the performance of the model.
01:01
This could involve testing the model on new data, comparing the model to other models, or analyzing the accuracy of the predictions.
01:10
Model deployment.
01:12
This stage involves deploying the model to be used in real world applications.
01:17
This could involve integrating the model into a larger system or making it available to end users through a web-based interface or other application.
01:28
These stages are not necessarily sequential and may be iterative, with the output of one stage feeding into the next.
01:36
Data mining is an iterative process that requires careful planning and execution to ensure that the results are accurate, useful, and actionable.
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