Chapter 15 Which machine learning algorithm for which question?

Now that you are at the end (of the beginning) of your machine learning journey, let us reverse the proposition and look into which model for which question.

Linear regression
Logistic regression
Decision tree
Random forest
Naive Bayes
Support vector machine
AdaBoost
Gradient-boosting trees
Simple neural network
Hierarchical clustering
Gaussian mixture model
Convolutional neural network
Recurrent neural network
Recommender system

15.1 Providing a decision framework for hiring new employees

Interesting model to look into:

Decision Tree is a pro gamer here

15.2 Understanding and Predicting product attributes that make a product most likely to be purchased

Interesting model to look into:

Logistic Regression
Decision Tree

15.3 Analyzing sentiment to assess product perception in the market.

Interesting model to look into:

Naive Bayes — Support Vector Machines (NBSVM)

15.4 When you are working with time-series data or sequences (eg, audio recordings or text)

Interesting model to look into:

Recurrent neural network
LSTM

15.5 Predicting the Housing Prices

Interesting model to look into:

Advanced regression techniques like random forest and gradient boosting

15.6 Exploring customer demographic data to identify patterns

Interesting model to look into:

Clustering (elbow method)

15.7 Predicting Loan Repayment

Interesting model to look into:

Classification Algorithms for imbalanced dataset

15.8 Predicting if a skin lesion is benign or malignant based on its characteristics (size, shape, color, etc)

Interesting model to look into:

Convolutional Neural Network (U-Net being the best for segmentation)

15.9 Predicting client churn

Interesting model to look into:

Linear discriminant analysis (LDA) or Quadratic discriminant analysis (QDA)

( particularly popular because it is both a classifier and a dimensionality reduction technique)

15.10 Creating classification system to filter out spam emails

Interesting model to look into:

Classification Algorithms —

Naive Bayes, SVM , Multilayer Perceptron Neural Networks (MLPNNs) and Radial Base Function Neural Networks (RBFNN) suggested.

15.11 Predicting how likely someone is to click on an online ad

Interesting model to look into:

Logistic Regression
Support Vector Machines

15.12 Detecting fraudulent activity in credit-card transactions.

Interesting model to look into:

Adaboost
Isolation Forest
Random Forest

15.13 Predicting the price of cars based on their characteristics

Interesting model to look into:

Gradient-boosting trees are best at this.

15.14 Predicting the probability that a patient joins a healthcare program

Interesting model to look into:

Simple neural networks

15.15 Predicting whether registered users will be willing or not to pay a particular price for a product.

Interesting model to look into:

Neural Networks

15.16 Segmenting customers into groups by distinct charateristics (eg, age group)

Interesting model to look into:

K-means clustering

15.17 Featuring extraction from speech data for use in speech recognition systems

Interesting model to look into:

Gaussian mixture model

15.18 Object tracking of multiple objects, where the number of mixture components and their means Predicting object locations at each frame in a video sequence.

Interesting model to look into:

Gaussian mixture model

15.19 Organizing the genes and samples from a set of microarray experiments so as to reveal biologically interesting patterns.

Interesting model to look into:

Hierarchical clustering algorithms

15.20 Recommending what movies consumers should view based on preferences of other customers with similar attributes.

Interesting model to look into:

Recommender system

15.21 Recommending news articles a reader might want to read based on the article she or he is reading.

Interesting model to look into:

Recommender system

15.22 Recommending news articles a reader might want to read based on the article she or he is reading.

Interesting model to look into:

Recommender system

15.23 Optimizing the driving behavior of self-driving cars

Interesting model to look into:

Reinforcement Learning

15.24 Diagnosing health diseases from medical scans.

Interesting model to look into:

Convolutional Neural Networks

15.25 Balancing the load of electricity grids in varying demand cycles

Interesting model to look into:

Reinforcement Learning

15.26 Providing language translation

Interesting model to look into:

Recurrent neural network

15.27 Generating captions for images

Interesting model to look into:

Recurrent neural network

15.28 Powering chatbots that can address more nuanced customer needs and inquiries

Interesting model to look into:

Recurrent neural network