# Introduction Many real-world datasets contain missing values, which can cause issues when using machine learning algorithms that assume complete and numerical data. In such cases, it is important to handle missing values appropriately to make the most of the available data. One common strategy is imputation, which involves filling in the missing values based on the known part of the data. In this tutorial, we will explore different strategies for imputing missing values using scikit-learn, a popular machine learning library in Python. ## VM Tips After the VM startup is done, click the top left corner to switch to the **Notebook** tab to access Jupyter Notebook for practice. Sometimes, you may need to wait a few seconds for Jupyter Notebook to finish loading. The validation of operations cannot be automated because of limitations in Jupyter Notebook. If you face issues during learning, feel free to ask Labby. Provide feedback after the session, and we will promptly resolve the problem for you.
Click the virtual machine below to start practicing