Scikit-learn is an open-source Python library for classical machine learning. It provides consistent tools for classification, regression, clustering, dimensionality reduction, data preprocessing and model evaluation, such as cross-validation. Built on NumPy and SciPy, it is widely used in education, research and practice thanks to its simple, uniform interface. For deep neural networks, frameworks such as PyTorch or TensorFlow are usually chosen instead.
pandas is an open-source Python library for analysing and manipulating tabular data, developed from 2008 onwards by Wes McKinney. Its central object, the DataFrame, resembles a table with named columns. It can be used to load data from CSV files, Excel or databases and to clean, filter, group and merge it. pandas is built on NumPy and is a standard tool in data analysis, reporting and training data preparation.
NumPy is the fundamental open-source Python library for numerical computing. It provides multidimensional arrays and fast functions for linear algebra, statistics and random numbers, with its core running in compiled code. Many data analysis and machine learning tools, such as pandas, scikit-learn and numerous visualisation libraries, are built on NumPy. Anyone working with data in Python therefore almost always uses it, directly or indirectly.
XGBoost (Extreme Gradient Boosting) is an open-source library that implements gradient boosting with decision trees particularly efficiently. Many small trees are trained one after another, each correcting the errors of the previous ones, while regularisation helps prevent overfitting. XGBoost is regarded as a proven choice for tabular data, for example in credit risk scoring, churn prediction or sales forecasting.
Machine learning is a branch of artificial intelligence in which computers derive patterns from data instead of having every rule explicitly programmed. Depending on the task, models learn from labelled examples (supervised), from unlabelled data (unsupervised) or through rewards (reinforcement). Applications range from spam filters and demand forecasts to product recommendations. Quality depends heavily on the quantity and quality of the training data.
