Data Science and Machine Learning (Theory and Projects) A to Z - Python for Data Science: Dataset Preprocessing

The video tutorial covers data cleaning, focusing on handling missing values, detecting outliers, and encoding categorical variables. It uses the Titanic dataset as an example, demonstrating how to replace missing values with the median, identify and remove outliers, and convert categorical data into numeric form using one-hot encoding. The tutorial concludes with preparing the dataset for machine learning applications.

How do you determine whether to fill missing values or drop records with missing values?

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What is the significance of using the Pandas library for data manipulation?

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Why might it be beneficial to drop certain attributes from a dataset?

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What steps would you take to prepare a dataset for machine learning algorithms?

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