We identify relevant features that can help improve our model's performance. We create new features, such as the average sales per customer and the sales growth rate.
In this story, we demonstrated how to build a data science solution using Anaconda. We covered data preparation, exploration, feature engineering, model building, evaluation, and deployment.
import matplotlib.pyplot as plt
# Load dataset df = pd.read_csv('sales_data.csv')
# Explore the data print(df.head())