It offers easy-to-use data structures and data analysis tools, making it ideal for tasks related to data preprocessing and exploration.

Pandas integrates seamlessly with other libraries and is widely used in the data science community.
4. TensorFlow

TensorFlow is a popular deep-learning framework that provides a high-performance numerical

computation library. It supports a wide range of machine learning and deep learning algorithms and offers flexible tools for building and training neural networks.

TensorFlow’s scalability and compatibility make it a preferred choice for large-scale projects.
5. PyTorch

PyTorch is a widely adopted library for deep learning. It combines tensor computation with GPU acceleration and features a tape-based automatic differentiation system.

PyTorch’s intuitive interface and dynamic computational graph make it an excellent choice for prototyping and production-level deep learning models.
6. Keras

Keras is a user-friendly library that simplifies the process of building and training deep neural networks. It provides a high-level API and supports various backends, including TensorFlow.

Keras’s simplicity and versatility have made it a popular choice for both researchers a

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