markbassmodel 0.1.0

This is a package containing several bass model functions that are useful for solving or evaluating marketing related problems

# markbassmodel

The markbassmodel package is a Python package that provides functions for fitting and predicting using the Bass diffusion model.

To forecast how quickly new items will be adopted in a market, the Bass model is a well-liked method in marketing analytics. The Bass model package can be used to aid with a number of issues relating to the marketing and adoption of new products, such as:

• Forecasting product sales

• Estimating the market potential with diffusion

• Comparing the performance of different products

## Functions

‘diffusion(sales)’ This function calculates the cumulative diffusion curve for a given set of data’s slaes column.

‘adoption_rate(t, p, q, m, N)’ This function calculates the adoption rate for a given set of parameters and time.

‘bass_f(t, p, q)’ This function calculates the Bass diffusion curve for a given set of parameters and time.

‘bass_F(t, p, q)’ This function calculates the cumulative Bass diffusion curve for a given set of parameters and time.

‘predict_bass_model(params, m)’ This function predicts the diffusion of a new product using the parameters p and q and the total market potential m.

‘plot_bass(p, q, title)’ This function plots the Bass diffusion curve for a given set of parameters.

## Installation

To install the bassmodel package, you can use pip:

` pip install markbassmodel ` ## Usage

To use the markbassmodel package, first import it: ` import markbassmodel `

calculate the parameters

` diffusion(sales) `

plot the Bass diffusion curve: ` bassmodel.plot_bass(params['p'], params['q'], 'Bass diffusion curve') `

Sample data to check the package

[ https://drive.google.com/drive/folders/1rtiKrg9xa2TMH8cTqN2l-eHWqQG1ZH6c?usp=sharing ]

the data contain smartphones sales over some period git