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

📖 stoas

public document at doc.anagora.org/recommended-algorithms-by-usage
video call at meet.jit.si/recommended-algorithms-by-usage

⥱ context

To see links, go up to full node [[recommended-algorithms-by-usage]].