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<speak> Welcome to the lesson on understanding data. <break strength="strong"/>You will implement this project in four parts, understanding the historical data, exploratory data analysis, model building, and model evaluation. <break strength="strong"/>First, let us see the understanding of the historical data. <break strength="strong"/>You need historical data to train the machine learning model using the algorithm. First, you need to understand, what are the variables in the data. <break strength="x-strong"/> What are the inputs? <break strength="x-strong"/> What is the output? <break strength="x-strong"/> How data distributed? <break strength="x-strong"/> You get answers to these questions in the first part, understanding the historical data. <break strength="strong"/>In Python, you need several modules to perform data analysis, or to understand the data. <break strength="strong"/>These modules come with methods ,that will help understand the data. <break strength="strong"/>For example, if you want to find the maximum in the given data, you require maximum function.<break strength="strong"/> You can retrieve the maximum value of a particular variable, by using the maximum function. <break strength="strong"/>Suppose, if you want to check the maximum value in the GRE score. <break strength="x-strong"/> You can check the maximum GRE score with this function.<break strength="x-strong"/> You can use the existing methods in the libraries to find some insights from the data. <break strength="x-strong"/> First, you have to import the required libraries. <break strength="strong"/>In Python, there are several modules ,or packages available for machine learning. <break strength="x-strong"/> To understand the data, you need three libraries. <break strength="x-strong"/> The first module is NumPy.<break strength="x-strong"/> The second module is the Pandas.<break strength="x-strong"/> And the third module is Matplotlib. NumPy module is useful for performing numerical operations on the data.<break strength="x-strong"/> The historical data contains rows and columns. So it is like two-dimensional data or multidimensional data. <break strength="x-strong"/> So all the data is available as numerical arrays or matrices. Numpy is useful for performing several operations, on this structured data, or the Matrix data.<break strength="x-strong"/> After this, you need to use the pandas' library. <break strength="x-strong"/> Pandas is a data analysis library.<break strength="x-strong"/> We use pandas for data analysis. To gain insights into the data, we use the Pandas library. In some cases, to understand the data, we require data visualization. <break strength="x-strong"/> <break strength="x-strong"/> </speak>