How to Load a CSV Dataset in Jupyter Notebook Using Pandas

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Loading a dataset is one of the first essential steps for anyone beginning a machine learning project in Python. The process requires importing the Pandas library, a widely used Python tool for data analysis and manipulation. Using the read_csv() function, a CSV file can be loaded either from a local path or a remote URL into a Pandas DataFrame, which organizes data into rows and columns. Once loaded, the df.head() command allows users to preview the first few rows and quickly understand the dataset's structure. These three lines of code form the foundation for further data exploration and preprocessing in Jupyter Notebook.

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