Metadata-Version: 2.1
Name: witcher
Version: 0.0.26
Summary: Automated AI tool including: recommender system, deep Learnings ...
Home-page: https://github.com/BabakEA/witcher
Author: Babak EA, Founder and CEO: AI Forest Inc 
Author-email: emami.babak@gmail.com
License: UNKNOWN
Description: # witcher
        Witcher is an automated, driverless tool to build an error-free machine learning application.
        
        
        Witcher is an automated AI application designed to speed up the data processing phases.
        The current witcher has :
        
        <UL>
        
        <li><B>Recommender:</B> will provide you with a comprehensive product recommendation using product popularity, product similarity and user similarity models.</li>
        
        
        <li><B>StockMarket :</B> Will read the stock information from Yahoo API and analysis the Stock prices using ARIMA Time series</li>
        
        
        <li><B>FileChooser :</B> Automated FileChooser function, Filechooser will read your data regardless it format and will provides you a Datafram. Currently witcher can read csv,xls,xlsx,sas,and images automaticaly </li>
        
        
        <li><B>ImageToCartoon :</B> A fun project will read your images and do some processing such as image blurred, edges extraction, image to cartoon,...</li>
        </UL>
        
        
         
        Magical data processing, predictive, and deep learning models will be joining witchers functions very soon :)
        
        I hope you will enjoy using the witcher library
        
        
        Thank you. 
        
        
        Babak.EA
        Founder and CEO: AI Forest Inc
        
        
        
        
        How to run : 
        
        <b> using Jupyter notbook </b> 
        
        <UL>
        
        <li><B>import wirtcher </B></li>
        <li><B>from witcher import Recommender</B></li>
        <li><B>from witcher import StockMarket</B></li>
        <li><B>from witcher import FileChooser</B></li>
        <li><B>from witcher import ImageToCartoon</B></li>
        </UL>
        
        
        from witcher import FileChooser
        
                FileChooser.Filechooser ==>
        		
                will read the file and will returen the filename, filepath, and dataframe for
                CSV, XLS,XLSX, SAS, and Images
        		
        
                Reprort=witcher.FileChooser.filechoose()
        		
                Report
        		
        
                report.files == > file path
        		
                report.df ==> dataframe
        		
        
            from witcher import FileChooser, ImageToCartoon
        	
                img=FileChooser.Filechooser()
        		
                img
        		
        
                image=img.df or
        		
                image=ImageToCartoon.Img_Reader(img.files[0]) # read image and return numpy vector
        		
        
                ImageToCartoon.ImageShow(image) ### Show the image
        		
                Image_D=IMG_D=ImageToCartoon.Decolorization(image)
        		
                Blurred=Blurred(image)
        		
                edges=ImageToCartoon.Edgedetection(image)
        		
                Bluured=ImageToCartoon.Blurred(image)
        		
                Cartoon=Cartoon(bluured=Blurred,mask=edges)
        		
                or
        		
                Cartoon=Cartoon(bluured=Blurred,decolor=Image_D,mask=edges)
        		
        
        
            from witcher import StockMarket
        	
                select your stock and starting date to end date
        		
                df=StockMarket.Stock_Reader(stock=["AC.TO"],Period="1D",Start_date="2010-01-01",End_date="Today")
        		
                df=StockMarket.Dataset_Spliter(df,col="Close",split=.1,Forecasting=True)
        		
                select the column you want to analysis and pass it to the finction 
        
        
        	from witcher import Recommender
        	
                submit your user products and run the recommender system
        		
                Model_generator=Recommender.Model_Generator(User Product dataframe,User Satisfaction DataFrame,User Informatiin  dataframe ,knn_neighbors=number of the neighbors) 
        		*** Witcher Recommender system work based on 3 different recommender systems, SVD, PCA, and KNN with a total accuracy of 80-85%
        										
        
                df=StockMarket.Dataset_Spliter(df,col="Close",split=.1,Forecasting=True)
        		Get Recommendation : 
        		products=Recommender.Product_Recommender("User ID")
        		Recommender.pprint.pprint(products.report)
        		
                Recommendation for a new user: 
        		products=Recommender.Product_Recommender("User Name",NEW_USER="True",USER_feature=[list of the selected producte by user]+[User incom]+[User satisfaction level] ( new user is 0))
        		Recommender.pprint.pprint(products.report)
        
        
        source code : github
        https://github.com/BabakEA/witcher
        
        YouTube:https://www.youtube.com/channel/UCBqqRv8vWV3NZFF2tQV4e-w
        
        PyPi : 
        pip install witcher 
        
Platform: UNKNOWN
Description-Content-Type: text/markdown
Provides-Extra: test
