This resource is designed primarily for beginner to intermediate data scientists or analysts who are interested in identifying and applying machine learning algorithms to address the problems of their interest.
A typical question asked by a beginner, when facing a wide variety of machine learning algorithms, is “which algorithm should I use?” The answer to the question varies depending on many factors, including:
- The size, quality, and nature of data.
- The available computational time.
- The urgency of the task.
- What you want to do with the data.
Even an experienced data scientist cannot tell which algorithm will perform the best before trying different algorithms. We are not advocating a one-and-done approach, but we do hope to provide some guidance on which algorithms to try first depending on some clear factors.
It completely depends on the dataset we have. If the data is discrete we use SVM. If the dataset is continuous we use linear regression.
So there is no specific way that lets us know which ML algorithm to use, it all depends on the exploratory data analysis (EDA).
EDA is like “interviewing” the dataset; As part of our interview we do the following:
- Classify our variables as continuous, categorical, and so forth.
- Summarize our variables using descriptive statistics.
- Visualize our variables using charts.
Based on the above observations select one best-fit algorithm for a particular dataset.