Package: businessmodels
businessmodelsTable of ContentsIntroductionInstallationDocumentationContributingLicenseIntroductionThis python package has several modules to work for data analysis.InstallationFollowing code is to install package.pipinstallbusinessmodelsDOCUMENTATIONbusinessmodels is a helpful package in data science. It contains several modules to get desired analysis output. Most of the modules are dedicated to business analysis purposes.
You can find the documentationhereCustomer SegmentationData Overview:Function Call -
Perform customer segmentation using the 'businessmodels' library and 'customersegmentation.cust_seg' is a function in the library, so it takes the 'df' DataFrame and a date (Ex.- '2023-04-02') as arguments.frombusinessmodelsimportcustomersegmentationSegments=customersegmentation.cust_seg(df,'2023-04-02')Customer_rfm,active_customer_segments,inactive_customers,segments_summary=Segments.Daily()By changing the Segments.daily to Weekly, Monthly, Quarterly get others segments customers.Price Elasticity of ProductsData Overview:Function Call -
Calculate price elasticity using the 'pricing' function from the 'price_elasticity' module. Get the results of price elasticity, separating products into different categories'Elastic_Products' are products with elastic demand'Unit_Elastic_Products' are products with unitary elastic demand'Inelastic_Products' are products with inelastic demandfrombusinessmodelsimportprice_elasticitySeparated=price_elasticity.pricing(df)Elastic_Products,Unit_Elastic_Products,Inelastic_Products=Separated.price_elasticity()Recommendation EngineData Overview:Function Call -
Call the 'recommendation' method from the recommendation_engine module and pass the 'df' DataFrame as an argument than Call the 'final_recomendations' method on the 'run' object to generate the list of recommended products.
Print or use the 'product_list' variable, which contains the recommended products.frombusinessmodelsimportrecommendation_enginerun=recommendation_engine.recommendation(df)product_list=run.final_recomendations()GET SLA ModuleFunction Call -frombusinessmodelsimportget_slasla_determination=get_sla.SLADetermination(df)sla_determination.filter_and_convert_dates(st,et)sla_determination.calculate_lead_to_sale_diff()x,y=sla_determination.categorize_lead_to_sale_calculate()GET SLA DETERMINATION ModuleFunction Call -frombusinessmodelsimportsla_determinecolumn="Required Column Name"sla_check=sla_determine.SLA_Determination(df,column)a,b,c,d=sla_check.calculate_sla(st,et)MAIN FEATURESperformance rank model (model name: performance_rank)Get affiliates' ranked table on basis of their performance and their interpreted result. This module's documentation is here:customer segmentation model (model name: customersegmentation)to compute Price Elasticity of Products (model name: price_elasticity)recommendate a product to a customer (model name: recommendation_engine)get sla model (model name: get_sla)CONTRIBUTINGThis Code of Conduct applies both within project spaces and in public spaces when an individual is representing the project or its community.Instances of abusive, harassing, or otherwise unacceptable behavior may be reported by contacting the working group by e-mail or text messages sent to.Shuvadeep MaityManas RoyLICENSEIt is licensed under OSI approved MIT open license.