# Recommended algorithms by usage

See the [[Main AI Page]] or go back to the [[Master Contents Page]]
Also see the [[Master of Philosophy - Main Page]]

|	Usage	|	Algorithm	|
|	-----------------------	|	-----------------------	|
|	Predict housing prices	|	Regression(supervised)	|
|	explore customer demographic data to identify patterns	|	Unsupervised learning	|
|	Understand product-sales drivers such as competition prices, distribution, advertisement, etc	|	Linear regression	|
|	Classify customers based on how likely they are to repay a loan	|	Logistic regression	|
|	Predict if a skin lesion is benign or malignant based on its characteristics (size, shape, color, etc)	|	Logistic regression	|
|	Predict client churn	|	Linear/quadratic discriminant analysis	|
|	Predict a sales lead’s likelihood of closing	|	Linear/quadratic discriminant analysis	|
|	Provide a decision framework for hiring new employees	|	Decision tree	|
|	Understand product attributes that make a product most likely to be purchased	|	Decision tree	|
|	eg, if an email contains theword “money,” then the probability of it being spam is high	|	Naive Bayes	|
|	Analyze sentiment to assess product perception in the market	|	Naive Bayes	|
|	Create classifiers to filter spam emails	|	Naive Bayes	|
|	Predict how many patients a hospital will need to serve in a time period	|	Support vector machine	|
|	Predict how likely someone is to click on an online ad	|	Support vector machine	|
|	Predict call volume in call centers for staffing decisions	|	Random forest	|
|	Predict power usage in an electrical- distribution grid	|	Random forest	|
|	Detect fraudulent activity in credit-card transactions.	|	AdaBoost	|
|	Simple, low-cost way to classify images (eg, recognize land usage from satellite images for climate-change models).	|	AdaBoost	|
|	Forecast product demand and inventory levels	|	Gradient-boosting trees	|
|	Predict the price of cars based on their characteristics (eg, age and mileage)	|	Gradient-boosting trees	|
|	Predict the probability that a patient joins a healthcare program	|	Simple neural network	|
|	Predict whether registered users will be willing or not to pay a particular price for a product	|	Simple neural network	|
|	Segment customers into groups by distinct charateristics (eg, age group)— for instance, to better assign marketing campaigns or prevent churn	|	K-means clustering	|
|	Segment customers to better assign marketing campaigns using less-distinct customer characteristics (eg, product preferences)	|	Gaussian mixture model	|
|	Segment employees based on likelihood of attrition	|	Gaussian mixture model	|
|	Cluster loyalty-card customers into progressively more microsegmented groups	|	Hierarchical clustering	|
|	Inform product usage/development by grouping customers mentioning keywords in social-media data	|	Hierarchical clustering	|
|	Recommend what movies consumers should view based on preferences of other customers with similar attributes	|	Recommender system	|
|	Recommend news articles a reader might want to read based on the article she or he is reading	|	Recommender system	|
|	Optimize the trading strategy for an options-trading portfolio	|	Reinforcement learning	|
|	Balance the load of electricity grids in varying demand cycles	|	Reinforcement learning	|
|	Stock and pick inventory using robots	|	Reinforcement learning	|
|	Optimize the driving behavior of self-driving cars	|	Reinforcement learning	|
|	Optimize pricing in real time for an online auction of a product with limited supply	|	Reinforcement learning	|
|	Diagnose health diseases from medical scans	|	Convolutional neural network	|
|	Detect a company logo in social media to better understand joint marketing opportunities (eg, pairing of brands in one product)	|	Convolutional neural network	|
|	Understand customer brand perception and usage through images	|	Convolutional neural network	|
|	Detect defective products on a production line through images	|	Convolutional neural network	|
|	When you are working with time-series data or sequences 	|	Recurrent neural network	|
|	Provide language translation	|	Recurrent neural network	|
|	Track visual changes to an area after a disaster to assess potential damage claims (in conjunction with CNNs)	|	Recurrent neural network	|
|	Assess the likelihood that a credit-card transaction is fraudulent	|	Recurrent neural network	|
|	Generate captions for images	|	Recurrent neural network	|
|	Power chatbots that can address more nuanced customer needs and inquiries	|	Recurrent neural network	|