businessmodels 0.1.5

It is a Data Science package containing many modules to help in analysis.

businessmodels

Table of Contents

• Introduction
• Installation
• Documentation
• Contributing
• License

Introduction

This python package has several modules to work for data analysis.

Installation

Following code is to install package.

pip install businessmodels

DOCUMENTATION

businessmodels 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 documentation here

• Customer Segmentation

Data 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.

from businessmodels import customersegmentation
Segments = 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 Products

Data 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 demand

from businessmodels import price_elasticity
Separated = price_elasticity.pricing(df)
Elastic_Products,Unit_Elastic_Products,Inelastic_Products = Separated.price_elasticity()

• Recommendation Engine

Data 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.

from businessmodels import recommendation_engine
run = recommendation_engine.recommendation(df)
product_list = run.final_recomendations()

• GET SLA Module

Function Call -

from businessmodels import get_sla
sla_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 Module

Function Call -

from businessmodels import sla_determine
column = "Required Column Name"
sla_check = sla_determine.SLA_Determination(df, column)
a,b,c,d = sla_check.calculate_sla(st, et)

MAIN FEATURES

• performance 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)

CONTRIBUTING

This 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 Maity Manas Roy

LICENSE

It is licensed under OSI approved MIT open license.