Reading here and there.... from DATALLIGENCE

1. Defining the Problem: Analyze business requirements, define the scope of the problem, define the metrics by which the model will be evaluated, and define specific objectives for the data mining project.

2. Preparing Data: Remove/handle bad data, find correlations in the data, identify sources of data that are the most accurate, and determining which columns are the most appropriate for use in analysis.

3. Exploring the Data: Calculate the minimum and maximum values, calculate mean and standard deviations, and look at the distribution of the data.

4. Building Models: Specify the input columns, the attribute that you are predicting, and parameters that tell the algorithm how to process the data.

5. Exploring & Validating Models: Use the models to create predictions, which you can then use to make business decisions, create content queries to retrieve statistics, rules, or formulas from the model, embed data mining functionality directly into an application, update the models after review and analysis or update the models dynamically, as more data comes into the organization.

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