NDY

NDY Machine learning project for M.Tech Software Systems Sem4.

This Project is meant for dissertation for M.Tech Software System, FS-20-21 of Birla Institute of Science and Technology.

Installation

First, you will need to install git, if you don't have it already.

Next, clone this repository by opening a terminal and typing the following commands:

$ cd $HOME  # or any other development directory you prefer
$ git clone https://github.com/ChiragPatel8/NDY.git
$ cd NDY

Python and dependent libraries

You will need Python. Python 3 is already readily available on many systems. You can check the version of python by typing the following command (you may need to replace python3 with python):

$ python3 --version  # for Python it should be 3

Any Python 3 version is fine. If you do not have Python 3, I recommend installing it.

To install python 3: on Windows or MacOSX, see python.org. On MacOSX, you can also use MacPorts or Homebrew. If the python version is 3.6 on MacOSX, you need to execute the following command to install the certifi package of certificates. (see this StackOverflow question):

$ /Applications/Python\ 3.6/Install\ Certificates.command

On linux, you can use system's packaging system. For example, on Ubuntu:

$ sudo apt-get update
$ sudo apt-get install python3 python3-pip

Using pip

There are several scientific Python libraries needed for this project which can be installed using Python's integrated packaging system, pip.

First check if you have the latest version of pip:

$ python3 -m pip install --user --upgrade pip

Next, you need to create an isolated environment:

$ python3 -m pip install --user --upgrade virtualenv
$ python3 -m virtualenv -p `which python3` env

Now to activate this environment. Note: You need to run this command every time to use this environment.

$ source ./env/bin/activate

On Windows:

$ .\env\Scripts\activate

Then, use pip to install the required python packages.

$ python3 -m pip install --upgrade -r depends/requirements.txt

How to run.

Go to source directory

$ cd source

run runner.py

$python3 ./runner.py

There are already trained model in cache dir. If you need to re-train the model, get the training data and uncomment a line in runner.py and re-run the same command, it will pre-process the data and train the model.