This is a package containing several bass model functions that are useful for solving or evaluating marketing related problems
Project description
# 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).
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