Metadata-Version: 2.1
Name: squat
Version: 0.1.12
Summary: SQUAT
Home-page: https://github.com/binayr/SQUAT.git
Author: Binay Kumar Ray
Author-email: binayray2009@gmail.com
License: UNKNOWN
Description: # **S**pend **Q**uality and **U**sage **A**nalysis **T**ool (**SQUAT**)
        
        This Project is a tool to analyse Bankstatements transactions
        to give a comprehensive report on the spend, earning and usage of an user.
        It does the following job:
        
        * Creates and trains a Machine learning model to classify transactions based on the narration.
        * All the training and other repeatative work is already done for you.
        * Once the package is installed with pip, the developer just need to pass the bankstatement dataframe
        to get the report.
        
        <br><br>
        
        ## Project Components
        
        SQUAT contains the packages or libraries required for supporting and running the whole process.
        
        1. spacy
        2. Core ENG package for spacy
        3. pandas
        4. jupyter notebook
        
        **Source**:<br>
        https://bitbucket.global.standardchartered.com/users/1586202/repos/squat/browse
        
        ## About the ML model
        
        The model is created based on most common keyword observed from the bankstatements of singapore.
        This project has a large scope of improving the accuracy and adding more classifications in future
        depending on the type of dataset available to us.
        
        Everytime we update the model a new version of SQUAT is supposed to get released.
        
        ## Create and use whl file
        
        * with and updated setup.py execute the following command to create a whl file,
            ```python setup.py bdist_wheel```
        
        * Please make sure you have pre-installed pandas, spacy and jupyter from standard chartered artifactory in your
         virtualenv
        
        * Also make sure once spacy is installed the eng core library is also pre-installed in the virtualenv using pip.
        
        * Now you can pip install squat using the whl file or from standard chartered artifactory if it is hosted.
        
        ## API
        
        * You can import the utility by typing the following,
        ```from squat.Classifier.ClassifierUtil import ClassifierUtil```
        
        * Read any csv or excel using pandas and create a dataframe. Please make sure the df has the following header atleast,
        date, description, debit, credit, runningbalance (irrespective of the order)
        
        * The ```ClassifierUtil``` can be initialized using the above df.
        
        * Once initialized please make sure to call ```obj.evaluate()``` to evaluate each transaction.
        
        * Once evaluated you can call ```get_analysis``` method to get the comprehensive analysis or call
        ```show_stat``` to get the statistics.
        
        OR
        
        * You can import the utility by typing the following,
        ```from squat.Classifier.ClassifierUtil import ClassifierUtilRaw```
        
        * Read any csv or excel using pandas and create a dataframe. Please make sure the df has the following header atleast,
        date, description, debit, credit, runningbalance (irrespective of the order)
        
        * The ```ClassifierUtilRaw``` can be initialized to get the category.
        
        * Once initialized please make sure to call ```obj.get_cat(text)``` to evaluate the category of the text.
        
        * For Example,
        	```
        	obj.get_cat('paytm transaction gurgaon')
        	Out: ('Digital', 0.9632782936096191)
        	```
Platform: UNKNOWN
Classifier: Programming Language :: Python :: 3
Classifier: License :: OSI Approved :: MIT License
Classifier: Operating System :: OS Independent
Description-Content-Type: text/markdown
