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.


MATLAB® Account on (Licensed online solution)


Download OCTAVE from (Free software)

Process Steps

1. Train: Learn form known data. After the training the configuration (theta) is saved.

2. Predict: Use the saved configuration to predict unknown data


Linear Regression with Gradient Descent

Logistic Regression with Gradient Descent

Neural Network

Support Vector Machines

Centroids Analysis (no training needed)

Anomaly Detection and Collaborative Filtering (no training needed)