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Rule of thumb: Which AI / ML algorithms to apply

Written by Ajit Jaokar.

Firstly, there are three broad categories of algorithms:

  • Supervised learning: You know how to classify the input data and the type of behavior you want to predict, but you need the algorithm to calculate it for you on new data
  • Unsupervised learning: You do not know how to classify the data, and you want the algorithm to find patterns and classify the data for you
  • Reinforcement learning: An algorithm which learns by trial and error by interacting with the environment. You use it when you don’t have a lot of training data; you cannot clearly define the ideal end state; or the only way to learn about the environment is to interact with it

So, let us consider which algorithms can apply to business problems.

1. Customer services and supply chain

  • Understand product-sales drivers such as competition prices, distribution, advertisement, etc linear regression
  • Optimize price points and estimate product-price elasticities linear regression
  • Classify customers based on how likely they are to repay a loan logistic regression
  • Predict client churn Linear/quadratic discriminant analysis
  • Predict a sales lead’s likelihood of closing Linear/quadratic discriminant analysis
  • Detect a company logo in social media to better understand joint marketing opportunities (eg, pairing of brands in one product): Convolutional neural networks
  • Understand customer brand perception and usage through images : Convolutional neural networks

To read the full article featuring other applications, including in healthcare and trading, follow this link. For other articles by Ajit Joakar, visit this webpage. Details about these algorithms can be found here

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