Data Manipulation and Analysis
Pandas: A powerful library for data structures (DataFrames and Series) and data analysis tools.
NumPy: Provides efficient numerical operations on arrays and matrices.
Matplotlib: A versatile library for creating static, animated, and interactive visualizations.
Machine Learning Algorithms
Scikit-learn: A comprehensive library with a wide range of algorithms for classification, regression, clustering, and more.
TensorFlow: An open-source platform for machine learning, particularly deep learning, with a flexible architecture.
PyTorch: Another popular deep learning framework known for its dynamic computational graph and ease of use.
XGBoost: An efficient gradient boosting framework that often achieves state-of-the-art results.
CatBoost: A gradient boosting library that handles categorical features effectively.
DMLC XGBoost: A distributed version of XGBoost for large-scale datasets.
Development Environment
Jupyter Notebook: An interactive environment for creating and sharing documents that contain live code, equations, visualizations, and narrative text.
Anaconda: A distribution of Python that includes many popular data science packages, making it easier to set up a development environment.
Note: While these are some of the most common libraries, there are many others available depending on specific needs and preferences.
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