The following steps must be followed in order to develop an ML model: The first step is to gather the dataset for your model. 80% of this data will be used in the training and the rest of the 20% will be used in testing and model validation. Then, you need to select a suitable algorithm for your model. The algorithm selection totally depends on the problem type and the data set. Next comes the training of the model. It includes running the model against various inputs and re-adjusting it according to the results. This process is repeated until the most accurate results are achieved. After training the model, it is tested against new data sets and is improved accordingly to produce accurate results.