Building Data Science Solutions With Anaconda Pdf 2021 -

We evaluate our model's performance using metrics such as mean squared error and R-squared.

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.

# Make predictions on testing set y_pred = model.predict(X_test)

# Split data into training and testing sets X_train, X_test, y_train, y_test = train_test_split(df.drop('sales', axis=1), df['sales'], test_size=0.2, random_state=42)

import pandas as pd from sklearn.model_selection import train_test_split