To identify the Machine Learning algorithm for our problem, we should follow the below steps:
Step 1: Problem Classification: Classification of the problem depends on the classification of input and output:
Classifying the input: Classification of the input depends on whether we have data labeled (supervised learning) or unlabeled (unsupervised learning), or whether we have to create a model that interacts with the environment and improves itself (reinforcement learning).
Classifying the output: If we want the output of our model as a class, then we need to use some classification techniques.
If it is giving the output as a number, then we must use regression techniques and, if the output is a different cluster of inputs, then we should use clustering techniques.
Step 2: Checking the algorithms in hand: After classifying the problem, we have to look for the available algorithms that can be deployed for solving the classified problem.
Step 3: Implementing the algorithms: If there are multiple algorithms available, then we will implement each one of them, one by one. Finally, we would select the algorithm that gives the best performance.