Roadmap for Building Machine Learning Models

The roadmap for building machine learning models is straightforward and consists of five major steps, which are explained here:

• Data Pre-processing

  This is the first step in building a machine learning model. Data pre-processing refers to the transformation of data before feeding it into the model. It deals with the techniques that are used to convert unusable raw data into clean reliable data.

  Since data collection is often not performed in a controlled manner, raw data often contains outliers (for example, age = 120), nonsensical data combinations (for example, model: bicycle, type: 4-wheeler), missing values, scale problems, and so on. Because of this, raw data cannot be fed into a machine learning model because it might compromise the quality of the results. As such, this is the most important step in the process of data science.

• Model Learning

  After pre-processing the data and splitting it into train/test sets (more on this...