Python Codes

Commonly used Python libraries are:

👉🏻NumPy: This library is used for scientific computing and working with arrays of data. It provides functions for working with arrays of data, including mathematical operations, linear algebra, and random number generation.

👉🏻Pandas: This library is used for data manipulation and analysis. It provides tools for importing, cleaning, and transforming data, as well as tools for working with time series data and performing statistical analysis.

👉🏻Matplotlib: This library is used for data visualization. It provides functions for creating a wide range of plots, including scatter plots, line plots, bar plots, and histograms.

👉🏻Scikit-learn: This library is used for machine learning. It provides a range of algorithms for classification, regression, clustering, and dimensionality reduction, as well as tools for model evaluation and selection.

👉🏻TensorFlow: This library is used for deep learning. It provides a range of tools and libraries for building and training neural networks, including support for distributed training and hardware acceleration.

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Commonly used Python libraries are:

👉🏻NumPy: This library is used for scientific computing and working with arrays of data. It provides functions for working with arrays of data, including mathematical operations, linear algebra, and random number generation.

👉🏻Pandas: This library is used for data manipulation and analysis. It provides tools for importing, cleaning, and transforming data, as well as tools for working with time series data and performing statistical analysis.

👉🏻Matplotlib: This library is used for data visualization. It provides functions for creating a wide range of plots, including scatter plots, line plots, bar plots, and histograms.

👉🏻Scikit-learn: This library is used for machine learning. It provides a range of algorithms for classification, regression, clustering, and dimensionality reduction, as well as tools for model evaluation and selection.

👉🏻TensorFlow: This library is used for deep learning. It provides a range of tools and libraries for building and training neural networks, including support for distributed training and hardware acceleration.

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