# Evaluate model performance mse = mean_squared_error(y_test, y_pred) r2 = r2_score(y_test, y_pred) print(f'MSE: {mse:.2f}, R2: {r2:.2f}')

We split our data into training and testing sets and build a linear regression model using scikit-learn.

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.

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Next, we use Jupyter Notebook to explore and visualize our data. We create a histogram to understand the distribution of sales values.

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.

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. Anaconda provides a comprehensive platform for data science, making it easy to build and deploy data science solutions.

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