diff --git a/exercise-4/exercise-4.md b/exercise-4/exercise-4.md index 382a5ce..86943f0 100644 --- a/exercise-4/exercise-4.md +++ b/exercise-4/exercise-4.md @@ -40,7 +40,7 @@ DirectLake mode now eliminates this import requirement by loading the data files In this exercise, you will take on the role of a data scientist tasked with exploring, cleaning, and transforming a dataset containing taxi trip data. You will build a machine learning model to predict the duration of taxi trips using the New York taxi greencab dataset from 2009 to 2018, which includes information like pickup and drop-off times, locations, fares, and passenger counts. 1. **Download the Exercise Notebook**: - - Download the provided Jupyter notebook, [Exercise 4 - Consume Data using Data Science](Exercise%204%20-%20Consume%20Data%20using%20Data%20Science.ipynb), to your local computer. This notebook contains the steps you will follow to complete the task. + - Download the provided Jupyter notebook, [Exercise 4 - Consume Data using Data Science](Exercise%204%20-%20Consume%20Data%20using%20Data%20Science.ipynb), to your local computer. This notebook contains the steps you will follow to complete the task. [This screenshot presents the steps to do it](./../media/extra/download-notebook-2.jpg). 2. **Import the Notebook into Fabric Workspace**: - Navigate to your Fabric workspace, either in the Data Engineering or Data Science section. diff --git a/media/extra/download-notebook-2.jpg b/media/extra/download-notebook-2.jpg new file mode 100644 index 0000000..00cc5e8 Binary files /dev/null and b/media/extra/download-notebook-2.jpg differ