sales forecasting ML model in retail using the Prophet algorithm from the Facebook Prophet library

sales forecasting ML model in retail using the Prophet algorithm from the Facebook Prophet library:

import pandas as pd

from fbprophet import Prophet

# Load the sales data into a pandas dataframe

sales_data = pd.read_csv('sales_data.csv')

# Convert the date column to a pandas datetime object

sales_data['date'] = pd.to_datetime(sales_data['date'])

# Rename the columns to 'ds' and 'y' for Prophet compatibility

sales_data = sales_data.rename(columns={'date': 'ds', 'sales': 'y'})

# Create and train the Prophet model

model = Prophet()

model.fit(sales_data)

# Create a future dataframe for the next 30 days

future = model.make_future_dataframe(periods=30)

# Make sales predictions for the next 30 days

sales_predictions = model.predict(future)

# Print the sales predictions for the next 30 days

print(sales_predictions.tail(30))

In this code, we first load the sales data into a pandas dataframe and convert the date column to a datetime object. We then rename the columns to 'ds' and 'y' to be compatible with the Prophet algorithm.

Next, we create and train the Prophet model using the sales data. We then create a future dataframe for the next 30 days and use the trained model to make sales predictions for the next 30 days.

Finally, we print the sales predictions for the next 30 days using the tail method of the sales_predictions dataframe.

Note that this is just a simple example, and there are many ways to improve the accuracy and effectiveness of sales forecasting ML models in retail. The specific code for a sales forecasting ML model will depend on the specific problem being solved and the data being used.