There are countless algorithms we can used to mathematically predict an outcome to a business challenge.  However, the most widely used algorithms will fall into four categories: classification, continuous, clustering and recommendation.

Let’s use a real life example to illustrate how we choose the right algorithm to solve the right problem. For illustration purposes we are making a number of assumptions to keep things simple for the non-analyst.

Let’s say that a realtor is trying to answer the following questions:

1. Will a couple buy a house? Here we are looking for a categorical answer of Yes or No. For this we would use some kind of Classification algorithm, which could include: Logistic Regression, Decision Trees or Convolutional Neural Network
2. How much will they pay of the house? For this question we would use Continuous estimation as we trying to determine the value in a sequence. Is this case, one would likely use a Linear Regression algorithm.
3. Where will the buy the house? Clustering would be the best approach to determine where they are likely to buy a house. K-means and Affinity
4. If they buy a house, what else will they buy? Recommender System Algorithms are commonly used to determine next best offer or next best action. The most commonly used Recommender algorithm is Collaborative Filtering: either user-to-user or item-to-item.