The modelling process is a crucial step in a data science process and for that, we use Machine Learning. We feed our model the right set of data and train it with appropriate algorithms. The following steps are taken into consideration while modelling a process:
1. The very first step is Feature Engineering. This step takes the previously collected information into consideration, determines the essential features for the model and combines them to form new and more evolved features.
2, This step must be performed with caution as too many features could end by deteriorating our model rather than evolving it.
3. Then we determine the spot-checking algorithms. These algorithms are the ones on which the model needs to be trained after acquiring new features.
4. Out of them, we pick the best performing algorithms and tune them to even enhance their abilities. To compare and find the best model, we consider the metric of different algorithms.