A data analysis package that helps users determine the mean, standard deviation and probability distribution of different datasets and outcomes.
The goal is to create a package that would help academics and data professionals alike in quickly determining the Probability Distributions of their dataset and visualising it.
The setup.py and test.py scripts are the set-up configuration and the unittest files for the distribution package respectively. distributions package contains Binomialdistribution_challenge.py for Binomial distribution, Gaussiandistribution.py for Gaussian distribution and Generaldistribution.py for reading the data fromm file.
The package was written in Python. Any Python version above 3.0 should work. If you would like to install the package in your local machine, it is advisable to install it in a new environment with the latest python installation.
The MIT License (MIT)
Copyright (c) 2021 Idowu Odesanmi. All rights reserved.
Permission is hereby granted, free of charge, to any person obtaining a copy of this software and associated documentation files (the "Software"), to deal in the Software without restriction, including without limitation the rights to use, copy, modify, merge, publish, distribute, sublicense, and/or sell copies of the Software, and to permit persons to whom the Software is furnished to do so, subject to the following conditions:
The above copyright notice and this permission notice shall be included in all copies or substantial portions of the Software.
THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE SOFTWARE..
- Credits to UDACITY for laying the groundwork for this package for me to build on.