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"info": {
"author": "Binay Kumar Ray",
"author_email": "binayray2009@gmail.com",
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"description": "# **S**pend **Q**uality and **U**sage **A**nalysis **T**ool (**SQUAT**)\n\nThis Project is a tool to analyse Bankstatements transactions\nto give a comprehensive report on the spend, earning and usage of an user.\nIt does the following job:\n\n* Creates and trains a Machine learning model to classify transactions based on the narration.\n* All the training and other repeatative work is already done for you.\n* Once the package is installed with pip, the developer just need to pass the bankstatement dataframe\nto get the report.\n\n
\n\n## Project Components\n\nSQUAT contains the packages or libraries required for supporting and running the whole process.\n\n1. spacy\n2. Core ENG package for spacy\n3. pandas\n4. jupyter notebook\n\n**Source**:
\nhttps://github.com/binayr/SQUAT\n\n## About the ML model\n\nThe model is created based on most common keyword observed from the bankstatements of singapore.\nThis project has a large scope of improving the accuracy and adding more classifications in future\ndepending on the type of dataset available to us.\n\nEverytime we update the model a new version of SQUAT is supposed to get released.\n\n## Create and use whl file\n\n* with and updated setup.py execute the following command to create a whl file,\n ```python setup.py bdist_wheel```\n\n* Please make sure you have pre-installed pandas, spacy and jupyter from standard chartered artifactory in your\n virtualenv\n\n* Also make sure once spacy is installed the eng core library is also pre-installed in the virtualenv using pip.\n\n* Now you can pip install squat using the whl file or from standard chartered artifactory if it is hosted.\n\n## API\n\n* You can import the utility by typing the following,\n```from squat.Classifier.ClassifierUtil import ClassifierUtil```\n\n* Read any csv or excel using pandas and create a dataframe. Please make sure the df has the following header atleast,\ndate, description, debit, credit, runningbalance (irrespective of the order)\n\n* The ```ClassifierUtil``` can be initialized using the above df.\n\n* Once initialized please make sure to call ```obj.evaluate()``` to evaluate each transaction.\n\n* Once evaluated you can call ```get_analysis``` method to get the comprehensive analysis or call\n```show_stat``` to get the statistics.\n\nOR\n\n* You can import the utility by typing the following,\n```from squat.Classifier.ClassifierUtil import ClassifierUtilRaw```\n\n* Read any csv or excel using pandas and create a dataframe. Please make sure the df has the following header atleast,\ndate, description, debit, credit, runningbalance (irrespective of the order)\n\n* The ```ClassifierUtilRaw``` can be initialized to get the category.\n\n* Once initialized please make sure to call ```obj.get_cat(text)``` to evaluate the category of the text.\n\n* For Example,\n\t```\n\tobj.get_cat('paytm transaction gurgaon')\n\tOut: ('Digital', 0.9632782936096191)\n\t```\n\n",
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