Data Cleaning with Python: Removing Missing Values and Duplicates

The video tutorial covers data preparation for fine-tuning using Python. It begins with an introduction to the importance of clean data and demonstrates how to use Google Colab for executing Python code. The instructor guides viewers through uploading datasets to Google Drive, connecting to Google Colab, and importing the Pandas library. The tutorial then focuses on reading CSV files, removing missing values and duplicates, and saving the cleaned dataset. The process is aimed at preparing data for machine learning models.

Why is it important to remove duplicates from a dataset before fine-tuning?

• Duplicates improve the accuracy of the model.

• Duplicates can lead to overfitting and reduce model performance.

• Duplicates are necessary for data validation.

• Duplicates help in increasing the dataset size.

What is Google Colab primarily used for?

• Designing graphics

• Editing videos

• Creating presentations

• Running and executing Python code

What must you ensure before accessing your data on Google Colab?

• You have a premium Google account.

• Your data is in a PDF format.

• You are logged into your Google account linked to your Drive.

• Your data is stored on a local hard drive.

Which Python package is used to handle CSV files in this tutorial?

• NumPy

• Matplotlib

• Scikit-learn

• Pandas

What function is used to display the first few rows of a dataset in pandas?

• df.display()

• df.preview()

• df.head()

• df.show()

What does the 'inplace=True' parameter do when removing missing values?

• It saves the dataset to a new file.

• It replaces the original dataset with the cleaned version.

• It creates a new dataset with missing values removed.

• It ignores the missing values.

What is the final step in the data preparation process described in the tutorial?

• Visualizing the data

• Sharing the dataset with others

• Saving the cleaned dataset to Google Drive

• Training a machine learning model