In order to understand your data, you first make some basic descriptives and plots. You can repeat this step if you have made new variables.
If you have analyzed your data, you can construct new features. These are a few examples. After feature engineering, it is wise to again check some basic statistics (such as the correlation matrix).
If you have performed your descriptive analysis, you want to remove redundant rows based on your scope (if necessary) and have a look at your missing values.
If you have your dataset ready, you can start analyzing. This is the basic flow to test a linear regression model, but you can easily extend this to (1) different models and (2) classification problems. It is logical to go back in your flow based on the test results you have. You can add / remove variables and run your flow again.
Loading in your data from your csv files.
To use this workflow in KNIME, download it from the below URL and open it in KNIME:
Deploy, schedule, execute, and monitor your KNIME workflows locally, in the cloud or on-premises – with our brand new NodePit Runner.
