Metadata-Version: 2.1
Name: husky_simplex
Version: 0.0.2
Summary: A text pre-processing package
Project-URL: Homepage, https://github.com/Sudhendra/Simplex
Project-URL: Download URL, https://github.com/Sudhendra/Simplex/archive/refs/tags/0.0.1.tar.gz
Author-email: "Sudhendra Kambhamettu, Nikhil, Karan, Arya, Harshal" <sudhendrakambhamettu@gmail.com>
License: MIT License
        
        Copyright (c) 2022 Sudhendra
        
        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.
Classifier: License :: OSI Approved :: MIT License
Classifier: Operating System :: OS Independent
Classifier: Programming Language :: Python :: 3
Requires-Python: >=3.7
Description-Content-Type: text/markdown

# Husky_Simplex
Text processing package

Data preprocessing is the first and most essential stage in developing a machine learning
model as it affects the overall accuracy and efficiency of the outcome. Ordinary text data
contains non-contextual words, noise, misspelled words, symbols, punctuations, and
unnecessary syntactic connotations. To circumvent these hindrances, we need to clean
raw text data into data that is acceptable for statistical and computational analysis.

The purpose of the package is to provide a one-stop platform for most of the necessary
text preprocessing techniques. These steps are used to augment the computational
significance of text data for Natural Language Processing tasks.

## Package Functions
1. Tokenization - Converting string input to a list of words.
2. Word counter - Counting the total number of words in the input.
3. Stopword removal - Removing non-contextual words that are only used for the
grammatical structure.
4. Punctuation removal - Removing punctuations.
5. Symbol removal - Removing symbols.
6. Stemming - Removing tense connotations.
7. Bag of words - Quantifying words.
8. Count vectorization - Vectorization of text based on term frequency.
9. TF-IDF vectorization - Vectorization of text based on term frequency in relation to
document frequency

## Installation
``` pip install husky_simplex ```

or 
``` git clone https://github.com/Sudhendra/Husky_Simplex.git ```
``` cd Husky_Simplex ```
``` pip install - r requirements.txt ``
