Awesome GitHub RepositoriesMissing Data Imputation

Pandas is a high-performance data analysis library that provides a comprehensive framework for manipulating, cleaning, and transforming structured datasets. It centers on labeled one-dimensional and two-dimensional data structures, allowing users to construct, filter, and reshape tabular information while performing complex arithmetic and logical operations. The library distinguishes itself through a sophisticated indexing engine that enables automatic data alignment during calculations and relational merges. By utilizing a block-based memory layout, it optimizes cache locality for vectorized

Polars is a high-performance columnar data processing library designed for efficient analytical workflows. It functions as a structured data library that organizes information into typed columns, utilizing the Apache Arrow memory format to enable zero-copy data sharing and cache-friendly, vectorized operations. The engine is built to handle large-scale tabular datasets, providing both local and distributed analytical runtimes that scale from single-machine environments to multi-node clusters. The project distinguishes itself through a sophisticated lazy query engine that constructs abstract e

This project is an educational platform and research toolkit designed to teach deep learning through a combination of mathematical theory, visual diagrams, and executable code. It provides a comprehensive environment for building, training, and evaluating neural networks, grounding complex concepts in interactive computational notebooks that allow for hands-on experimentation. The framework distinguishes itself by interleaving theoretical foundations—including linear algebra, calculus, and probability—with practical implementations across multiple industry-standard libraries. It supports flex

Fastai is a high-level deep learning library built on PyTorch that provides a unified interface for managing the entire machine learning lifecycle. It functions as a comprehensive training toolkit, abstracting hardware management and automating complex training loops to simplify the construction and execution of neural network models. The framework is distinguished by its notebook-centric development environment and a type-dispatching data pipeline that automatically applies transformations based on input data formats. It emphasizes transfer learning through discriminative layer-wise optimiza

Backtrader is a Python framework designed for the development, backtesting, and live execution of algorithmic trading strategies. It provides a comprehensive environment for quantitative finance, allowing users to simulate trading logic against historical market data or connect directly to brokerage platforms for automated real-time trading. The project distinguishes itself through a unified event-driven architecture that treats backtesting and live trading with the same API. This consistency is supported by a flexible data-feed abstraction layer that normalizes diverse financial sources, ena

This project is a machine learning algorithm reference and implementation guide that provides theoretical foundations and code for supervised learning, deep learning, and natural language processing. It serves as a comprehensive toolkit for implementing predictive models and a technical reference for algorithm engineering. The project focuses on ensemble learning frameworks, including the construction of decision trees, random forests, and gradient boosting models. It also functions as a probabilistic graphical model library and an NLP algorithm reference, with specific implementations for se

Statsmodels is a comprehensive Python library designed for statistical modeling, econometric research, and data analysis. It provides a robust framework for estimating and diagnosing a wide range of statistical models, enabling users to perform rigorous hypothesis testing, regression analysis, and complex data exploration within structured environments. The library distinguishes itself through its support for advanced statistical methodologies, including state space representation for dynamic systems and generalized linear frameworks that accommodate non-normal response variables. It offers s

This project is a framework for the efficient serialization and deserialization of data structures. It provides a unified, macro-based interface that automates the conversion of complex internal objects into standardized formats and reconstructs them from raw input streams or buffers. By leveraging compile-time code generation, the library minimizes manual implementation overhead while ensuring consistent logic across diverse data types. The framework distinguishes itself through a format-agnostic data model and a visitor-based parsing architecture that decouples data structures from specific

PyMC is a Bayesian probabilistic programming framework used for building probabilistic models and performing Bayesian inference. It provides a probabilistic graphical model library for specifying random variables, priors, and likelihood functions, supported by an MCMC sampling engine and variational inference tools to estimate posterior distributions. The framework features a GPU-accelerated inference backend that compiles models into machine code to increase execution speed. It utilizes a backend-agnostic tensor execution model and just-in-time graph compilation to optimize the computation o

tsfresh is an automated feature engineering tool and library designed to extract statistical characteristics from raw time series data. It transforms sequential data into tabular datasets, converting time series into a flat format where each row represents a unique entity and columns represent extracted features. The project distinguishes itself through a parallel data processing framework that distributes heavy computational workloads across multiple CPU cores. It also implements hypothesis-based feature selection to identify the most predictive characteristics and filter out irrelevant ones

This project is a Python financial analytics framework and quantitative trading library. It provides a suite of mathematical tools for asset pricing, statistical market analysis, and the development of algorithmic trading strategies. The library is distinguished by its focus on currency and commodity correlation modeling, using regression and normalization to identify exchange rate drivers. It features a specialized portfolio optimization engine that applies graph theory, such as clique centrality and degeneracy ordering, alongside quadratic programming to balance risk-adjusted returns. The

tsai ist eine Deep-Learning-Bibliothek für Zeitreihenklassifizierung, Regression und Prognosen. Basierend auf PyTorch und fastai bietet sie ein Framework, um sequenziellen Daten Labels zuzuweisen, zukünftige Werte in univariaten oder multivariaten Sequenzen vorherzusagen und Repräsentationen in unbeschrifteten Daten mittels selbstüberwachtem Lernen zu trainieren. Die Bibliothek zeichnet sich durch spezialisierte Funktionen für Temporal Engineering und Skalierung aus. Sie enthält Tools für zyklische zeitliche Kodierung zur Erfassung saisonaler Muster sowie Online-Window-Slicing zur Verarbeitung von Datensätzen, die den verfügbaren Arbeitsspeicher übersteigen. Zudem unterstützt sie multimodale Input-Pipelines, die statische kategoriale Merkmale mit dynamischen kontinuierlichen Sequenzen kombinieren. Das Toolkit deckt ein breites Spektrum an Anforderungen für Vorverarbeitung und Evaluierung ab, darunter Sliding-Window-Segmentierung, Imputation fehlender Daten und die Konvertierung von tabellarischen Dataframes in strukturierte Tensoren. Die Modellleistung wird durch Walk-Forward-Validierung und Feature-Importance-Analyse bewertet, um zeitliche Konsistenz sicherzustellen.

OSMnx ist eine Python-Bibliothek zum Herunterladen, Modellieren und Analysieren von Straßennetzwerken und anderen geodatenbasierten Merkmalen aus OpenStreetMap. Sie ermöglicht es Benutzern, reale Infrastrukturdaten weltweit abzurufen und damit zu arbeiten, und bietet Werkzeuge für Netzwerkanalyse, räumliche Abfragen und Visualisierung. Die Bibliothek bietet Funktionen für die Arbeit mit städtischen Merkmalen wie Gebäudeumrissen, Haltestellen des öffentlichen Nahverkehrs und Höhendaten sowie Netzwerkstatistiken wie Kreuzungsdichte und Umwegigkeit. Sie unterstützt mehrere Reisemodi, einschließlich Fahren, Gehen und Radfahren, und kann kürzeste Wege berechnen, Reisegeschwindigkeiten imputieren und Isolinienkarten generieren. Zusätzliche Funktionen umfassen Geocodierung, Map-Matching, Koordinatenprojektion sowie die Möglichkeit, Netzwerke in verschiedenen Formaten zu speichern und zu laden. OSMnx bietet Werkzeuge zur Visualisierung von Straßennetzwerken und geodatenbasierten Merkmalen als statische Karten oder interaktive Webkarten und kann Figure-Ground-Diagramme zeichnen. Die Bibliothek ist über Standard-Python-Paketinstallationsmethoden verfügbar.

Dieses Projekt ist eine umfassende Bildungsressource für Machine Learning und eine Tutorial-Reihe, die als Sammlung interaktiver Jupyter Notebooks bereitgestellt wird. Es bietet praktische Python-Implementierungen für den gesamten Machine-Learning-Lebenszyklus und deckt überwachtes (supervised) und unüberwachtes (unsupervised) Lernen, Deep Learning sowie Reinforcement Learning ab. Die Ressource zeichnet sich durch detaillierte Implementierungsanleitungen für komplexe Architekturen aus, darunter Transformer, Generative Adversarial Networks (GANs) und Convolutional Neural Networks (CNNs). Zudem enthält sie spezialisierte Kursmaterialien für die Entwicklung von Reinforcement-Learning-Agenten mittels Q-Learning und Deep Q-Networks in simulierten Umgebungen. Die Inhalte decken ein breites Spektrum an Data-Science-Fähigkeiten ab, einschließlich Data-Engineering-Pipelines, Feature-Encoding und Dimensionsreduktion. Es bietet umfangreiches Material zur Modellevaluierung durch Kreuzvalidierung und diagnostische Metriken sowie fortgeschrittene Themen wie Natural Language Processing (NLP), Sentiment-Analyse und generative KI. Der gesamte Lehrplan ist für die interaktive Ausführung in Jupyter Notebooks konzipiert und kombiniert ausführbaren Code, Rich Text und Visualisierungen.

This project is a comprehensive collection of Python programming education materials, including tutorials, exercises, and curated code samples. It serves as a learning curriculum and software engineering toolkit, utilizing Jupyter Notebooks to combine executable code with descriptive educational text. The repository provides practical implementation guides for building large language model applications, such as retrieval-augmented generation systems, stateful AI agents, and machine learning workflows. It distinguishes itself by offering a structured approach to agentic coding workflows, cover

Vega-Lite is a high-level declarative language for specifying interactive, multi-view visualizations. It compiles a concise JSON specification into a full Vega visualization, automatically inferring scales, axes, and legends from encoding declarations. The grammar-of-graphics encoding maps data fields to visual channels such as position, color, size, and shape, while a multi-view composition grammar enables layered, faceted, concatenated, and repeated layouts. Reactive parameter binding links named parameters to input widgets, selections, and expressions for dynamic updates. The project suppo

This is an interactive notebook-based course that teaches machine learning from Python fundamentals through deep learning and natural language processing. It uses real datasets and multiple frameworks within a structured, hands-on curriculum that combines concise explanations with executable code cells, built-in datasets, and embedded exercise checkpoints. Learning progresses through data preparation and exploration, classical machine learning workflows, computer vision with convolutional neural networks, and natural language processing with deep learning, all delivered as a cohesive progressi

Connexion ist ein spezifikationsgetriebenes Framework für den Bau von APIs, das OpenAPI-Spezifikationen automatisch auf Anwendungslogik abbildet. Es nutzt diese Spezifikationen, um Routing, Request-Validierung und Response-Serialisierung zu automatisieren und API-Operationen über Operations-IDs mit Backend-Handler-Funktionen zu verknüpfen. Das Projekt zeichnet sich dadurch aus, dass es einen schema-getriebenen Mock-Server bereitstellt, der API-Verhalten unter Verwendung von Beispielantworten aus der Spezifikation simuliert, ohne Backend-Logik zu erfordern. Es enthält zudem ein dynamisches Dokumentations-Hosting-System, das die API-Spezifikation in eine interaktive Live-Konsole zum Erkunden und Testen von Endpunkten übersetzt. Das Framework deckt breite Funktionsbereiche ab, einschließlich Sicherheitsdurchsetzung durch Middleware-basierte Authentifizierung und Scope-Validierung, austauschbare Request- und Response-Validierungslogik sowie automatisierte Parameter-Injektion in typisierte Funktionsargumente. Es bietet zudem Dienstprogramme für das Application-Lifespan-Management, Middleware-Integration und Request-Simulation für Tests. Das Projekt kann verwendet werden, um Standalone-Webanwendungen zu booten oder bestehende Frameworks zu umhüllen, um spezifikationsgetriebene Funktionen hinzuzufügen.

This project is a collection of comprehensive guides and reference materials designed for technical interviews, machine learning system design, and professional development. It serves as a technical knowledge base and a career coaching manual, providing structured resources to help candidates navigate the machine learning hiring landscape. The resource distinguishes itself by offering detailed frameworks for comparing industry roles, analyzing company types, and planning long-term career progression. It provides specific guidance on evaluating employer organizational health, identifying resea