# Introduction In this lab, we will build a pipeline for dimensionality reduction and classification using Principal Component Analysis (PCA) and Logistic Regression. We will use the scikit-learn library to perform unsupervised dimensionality reduction on the digits dataset using PCA. We will then use a logistic regression model for classification. We will use GridSearchCV to set the dimensionality of the PCA and find the best combination of PCA truncation and classifier regularization. ## 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.
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