NumPy, short for Numerical Python, is an open source package for scientific computing and data analysis. NumPy serves as the foundation of Python’s scientific computing stack. Among the features of NumPy is ndarray, or N-dimensional array object, which is a flexible storage for data sets in Python. It also contains tools for integrating C/C++ and has capabilities for random number generation and Fourier transform.


NumPy is often preferred over regular Python lists for a variety of reasons. It is seen to be convenient, owing to its many free vector and matrix operations. Data structures in NumPy also takes up less space and performance in terms of speed is better, as well.

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    I have a large dataset on which you need to perform the following: (a) exploratory data analysis; (b) simple visualizations; (c) classification. You need to implement at least three classification algorithms. For instance, you can implement Logistic Regression and SVM Classification as two of the three algorithms. That is it! This will be a maximum of four to five hours of work. I will take a look...

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