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Machine learning for analytic data

Artificial Intelligence or machine leaning has nothing to do with intelligence it is the self-configuration of parameters on an ordinary application. The difference is, that human configures application parameter intelligence but are limited to a handful while machine learning uses statistical methods for the best fit of parameter and can use 10'000 or more parameters. The combination gives the best results. Humans configure the frame (input data and parameter ranges that are useful for the target of the application) and the machine leaning fits inside this frame all other parameters. This solution shows different samples how analytic data can be used for Machine learning. Analytic means data for Marketing, Transportation or Finance. Image recognizing is possible but not a target of this implementation.

Prerequisites

 

GNU Octave is a free of charge high-level language, primarily intended for numerical computations.  https://octave.org 
 
OR
 
Use an account on https://matlab.mathworks.com 
 
MATLAB is a Licensed online solution with a productive computing environment for engineers and scientists. With math, graphics, and programming, it's designed for the way you think and the work you do.

Process Steps

 
Train
Learn form known data. After the training the configuration "theta" is saved.
 
Predict
Use the saved configuration to predict unknown data.

Modules

Linear Regression

 
 
In statistics, linear regression is a linear approach to modeling the relationship between a scalar response (or dependent variable) and one or more explanatory variables (or independent variables). The case of one explanatory variable is called simple linear regression.

Logistic Regression

 
 
In statistics, the logistic model is used to model the probability of a certain class or event existing such as pass/fail, win/lose, alive/dead or healthy/sick. This can be extended to model several classes of events such as determining whether an image contains a cat, dog, lion, etc. Each object being detected in the image would be assigned a probability between 0 and 1 and the sum adding to one. Linear Regression returns a value but Logistic Regression only 0 or 1

Neural Network

 
 
Artificial neural networks (ANN) or connectionist systems are computing systems that are inspired by, but not identical to, biological neural networks that constitute animal brains. Such systems "learn" to perform tasks by considering examples, generally without being programmed with task-specific rules. For example, in image recognition, they might learn to identify images that contain cats by analyzing example images that have been manually labeled as "cat" or "no cat" and using the results to identify cats in other images. They do this without any prior knowledge of cats, for example, that they have fur, tails, whiskers and cat-like faces. Instead, they automatically generate identifying characteristics from the examples that they process.

Support Vector Machines

 
 
In machine learning, support-vector machines are supervised learning models with associated learning algorithms that analyze data used for classification and regression analysis. Given a set of training examples, each marked as belonging to one or the other of two categories, an SVM training algorithm builds a model that assigns new examples to one category or the other, making it a non-probabilistic binary linear classifier (although methods such as Platt scaling exist to use SVM in a probabilistic classification setting). An SVM model is a representation of the examples as points in space, mapped so that the examples of the separate categories are divided by a clear gap that is as wide as possible. New examples are then mapped into that same space and predicted to belong to a category based on the side of the gap on which they fall.

Centroids Analysis

 
 
 
In machine learning, a nearest centroid classifier or nearest prototype classifier is a classification model that assigns to observations the label of the class of training samples whose mean (centroid) is closest to the observation.
 
Centroids Analysis does not need training.

Anomaly Detection

  
 
 
In data mining, anomaly detection (also outlier detection) is the identification of rare items, events or observations which raise suspicions by differing significantly from the majority of the data. Typically the anomalous items will translate to some kind of problem such as bank fraud, a structural defect, medical problems or errors in a text. Anomalies are also referred to as outliers, novelties, noise, deviations and
exceptions.
 
Anomaly detection does not need training.

Donwload

 Documentation:
 Application (ZIP):
 OCTAVE: