What are the high-level steps as part of the model development?

Model is the heart of machine learning. The model is nothing but the combination of the numerical values and the respective data parameters.

The high-level steps as part of the model development.

Step 1: Data Preparation

This is the critical step as part of the model development. As part of this step, we need to identify what is the problem statement. On a high-level, the machine learning problem statements are divided into two categories.

a. Classification Problem Statements

b. Regression Problem Statements

Step 2: Data Analysis

In this step, data analysis happens on the prepared data set. The following steps are considered as part of this step.

a. What are all the useful parameters for the prediction analysis?

b. Remove the data set parameters that are not useful in the data set.

c. Group the prediction parameters

d. Determine the parameters that are categorical or numeric

Step 3: Model development

In this step, develop the regression or the classification models.

Step 4: Model Analysis

In this step, analyze the generated model performance.

Step 5: Model selection

This is the final step, select the model and export it.