Overview / Usage

Regression is a technology technique used in machine learning to compute statistical significance to bring best predictions approximation hence very important in solving real world problems situations. Regressions uses concepts of statistics and maths to give one of the best model to build scalable output as a results of powerful algorithms it renders. One of the best thing about regression is that you can build your own algorithms and come up with a better way to build an application that can solve complex business problems.
Indeed, there exists alot of problems that hinders and inappropriate thousands of organizations to achieve more due to lack of an expert systems that can predict the trends of the business timeline stories of operations.

Methodology / Approach

Use dataset model to split them into training and test sets. From the training set, the system can be able to understand the model patterns and learns from your figures of anaylsis. Several algorithms like Forward selections algorithms, bidirectional algorithms and backward elimination algorithms are used and from them, the best that can give little time to solve model problems is adopted. In Machine learning concepts, regressions can be of different types including Linear Regressions, Multiple Linear Regressions, Polynomial Linear Regressions, Random Trees, Decision forests and Evaluation perfomance are all kind of techniques used in Machine Learning in regressions analysis to manipulate and build scalable algorithms and run a magic A.I that can understand figures patterns and give best visualization that fits to the reasonable predicted figures

Technologies Used

Python programming language and R-programming.
Numpy, Matplotlib, Pandas for python and caTools, ggplot2, rPart, e1071 for R-Programming

Repository

https://github.com/Hisaack/Machine-Learning-A.I