markbassmodel 0.1.1

# MarkBassModel

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

## Description

The Bass diffusion model is a popular method in marketing analytics used to forecast the rate of adoption of new products in a market. The MarkBassModel package offers functionality to assist with various tasks related to product marketing and adoption, including:

• Forecasting product sales

• Estimating market potential through diffusion analysis

• Comparing the performance of different products

## Functions

The MarkBassModel package provides the following functions:

### diffusion(sales)

This function calculates the cumulative diffusion curve based on the sales data provided.

### adoption_rate(t, p, q, m, N)

This function calculates the adoption rate at a given time based on the Bass diffusion model’s parameters.

• t: Time at which the adoption rate is calculated.

• p: Coefficient of innovation.

• q: Coefficient of imitation.

• m: Total market potential.

• N: Cumulative number of adopters at time t.

### bass_f(t, p, q)

This function calculates the Bass diffusion curve at a given time based on the Bass diffusion model’s parameters.

### bass_F(t, p, q)

This function calculates the cumulative Bass diffusion curve at a given time based on the Bass diffusion model’s parameters.

### predict_bass_model(params, m)

This function predicts the diffusion of a new product using the Bass diffusion model’s parameters (p and q) and the total market potential (m).

### plot_bass(p, q, title)

This function plots the Bass diffusion curve for the given parameters p and q. The title parameter specifies the title of the plot.

## Installation

You can install the MarkBassModel package using pip:

`shell pip install markbassmodel `

## Usage

To use the MarkBassModel package, start by importing it:

`python import markbassmodel `

To calculate the parameters:

`python markbassmodel.diffusion(sales) `

To plot the Bass diffusion curve:

`python markbassmodel.plot_bass(params['p'], params['q'], 'Bass diffusion curve') `

## Example Data

You can find a sample dataset of smartphone sales over a period of time [here](https://drive.google.com/drive/folders/1rtiKrg9xa2TMH8cTqN2l-eHWqQG1ZH6c?usp=sharing). You can use this dataset to test and experiment with the MarkBassModel package.

## License

The MarkBassModel package is released under the [MIT License](https://opensource.org/licenses/MIT).