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JS-MachineLearning

Introduction

This Repository contains code related to Machine Learning on Javascript Platform. All the examples follow the same basic rules for execution.

  1. Install Node.JS onto your computer.
  2. Install the required packages mentioned below in the test cases.
  3. You can install the modules/packages globally.
  4. Clone the code fron the Repository and type "node File_Name.js" to run it.
  5. All the examples will contain comments and console.log outputs for better understanding of the code.

NOTE : This repository will not teach you the entire theory behind the programs. For that, it is better to do individual research and understand how each model works. The repository will be beneficial for people who have active knowledge about the models and know basic JS.

Examples

1) Perceptron Neural Network based XOR Prediction

This Program is based on the synaptic library for Neural Networks. It has a prewritten algorithm for XOR so that a model can be easily trained using a 'Trainer' object.

Required Packages (Install using npm) : synaptic Name of the script : XOR-synaptic.js Extra Files : NIL

2) Linear Regression Model

This Program is based on the ml node_module. ML module contains many tools for machine learning, and one of the basic tools is SLR or Simple Linear Regression. This certain program uses an external .csv file to read data from, using the csvtojson package for faster parsing.

Required Packages (Install using npm) : ml, csvtojson Name of the script : SLR.js Extra Files : Advertisement.cls (Input data for the model)

MORE EXAMPLES WILL BE ADDED SOON. [ONHOLD - Will Update in another 6 months]