A synthetic data example of MMM

First, we’ll begin with the necessary imports and setup:

import warnings
import arviz as az
import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
import pymc as pm
import seaborn as sns
from pymc_marketing.mmm.transformers import geometric_adstock, 
    logistic_saturation
from pymc_marketing.mmm.delayed_saturated_mmm import 
    DelayedSaturatedMMM
warnings.filterwarnings("ignore")
az.style.use("arviz-darkgrid")
plt.rcParams["figure.figsize"] = [12, 7]
plt.rcParams["figure.dpi"] = 100
%load_ext autoreload
%autoreload 2
%config InlineBackend.figure_format = "retina"

Synthetic data generation

Let’s generate some synthetic data to test the model. We will generate data for two media channels, with adstock and saturation effects, and a trend and seasonality component. We’ll start by generating a DataFrame containing weekly...