1 
Liner regression
Highly interuptable . need to have past relationship identified
for the output variable
helps predicate future output variables

understand sales drivers such as price, ad's or distribution
optimzation of price points

2 
Logistic regression
used for classification tasks, meaning the output variable is binary (eg, only black or white)
rather than continuous (eg, an infinite list of potential colors)

Predict if a skin lesion is benign or malignant based on its characteristics (size, shape, color, etc) 
3 
Linear/quadratic discriminant analysis
a logistic regression to deal with nonlinear problems—those in which changes to
the value of input variables do not result in proportional changes to the output variables

Predict client churn
Predict a sales lead’s likelihood of closing

4 
Decision tree
Highly interpretable classification or regression model that splits datafeature values into branches at decision nodes
(eg, if a feature is a color, each possible color becomes a new branch) until a final decision output is made

a decision framework for hiring new employees
Understand product attributes that make a product most likely to be purchased

5 
Naive Bayes
allows the probability of an event to be calculated based on knowledge of factors that might affect that event (eg, if an email contains the word “money,” then the probability of it being spam is high)

Analyze sentiment to assess product perception in the market
Create classifiers to filter spam emails

6 
Support vector machine
typically used for classification but can be transformed to perform regression. It draws an optimal division between classes (as wide as possible). It also can be quickly generalized to solve nonlinear problems

Predict how many patients a hospital will need to serve in a time period
Predict how likely someone is to click on an online ad

7 
Random forest
model that improves the accuracy of a simple decision tree by generating multiple decision trees and taking a majority vote of them to predict the output, which is a continuous variable (

Predict call volume in call centers for staffing decisions
Predict power usage in an electricaldistribution grid

8 
AdaBoost
Classification or regression technique that uses a multitude of models to come up with a decision but weighs them based on their accuracy in predicting the outcome

Detect fraudulent activity in creditcard transactions. Achieves lower accuracy than deep learning
Simple, lowcost way to classify images (eg, recognize land usage from satellite images for climatechange models). Achieves lower accuracy than deep learning

9 
Gradientboosting trees
Classification or regression technique that generates decision trees sequentially, where each tree focuses on correcting the errors coming from the previous tree model. The final output is a combination of the results from all trees

Forecast product demand and inventory levels
Predict the price of cars based on their characteristics (eg, age and mileage)

10 
Simple neural network
Model in which artificial neurons (softwarebased calculators) make up three layers (an input layer, a hidden layer where calculations take place, and an output layer) that can be used to classify data or find the relationship between variables in regression problems

Predict the probability that a patient joins a healthcare program
Predict whether registered users will be willing or not to pay a particular price for a product

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