This repository is a curated collection of over 30 independent Streamlit in Snowflake (SiS) demo applications, each self-contained in its own folder. The demos span a wide range of use cases including business intelligence dashboards, LLM-powered chatbots (Cortex, DeepSeek-R1, RAG), geospatial H3 visualizations, custom UI components (AgGrid, ECharts, Slickgrid), CI/CD pipelines with Snowflake CLI, and embedding SiS into external portals via Next.js. Each app includes a `streamlit_app.py` entry point, a `data/creation_script.sql` for Snowflake object setup, and sample data. The code is educational, heavily commented, and demonstrates patterns like caching, dynamic page navigation, and local/cloud-aware connections. While the apps are not production-grade (some hardcoded values, duplication), they serve as excellent reference implementations for building SiS applications.
This repository is a curated collection of recipes and tools for Go development, organized into categories such as testing, dependencies, code visualization, code generation, refactoring, errors, build, assembly, deployment, execution, monitoring, benchmarking, documentation, security, static analysis, and guides. The actual codebase is minimal: a Go module with a three-line main.go that generates the README.md from a structured YAML file (page.yaml) using the mdpage library. The primary value is the content itself, which serves as a reference for Go developers to discover and utilize various tools and techniques. The project is actively maintained, with up-to-date dependencies and a simple CI workflow for markdown link validation.
This repository is a curated list of resources for practicing data science with Python, organized in the style of 'awesome' lists. It contains links to libraries, tutorials, blog posts, talks, code snippets, and other resources across various data science domains including core libraries, machine learning, NLP, computer vision, and more. The repository serves as a reference guide for data scientists and practitioners rather than containing executable code or performing data processing tasks.
This repository implements an end-to-end predictive modeling pipeline for Premier League football match outcomes (home win, draw, away win). It ingests raw match statistics from the api-football.com REST API, processes JSON data into structured DataFrames, engineers features using rolling averages (5- and 10-game windows) and team-vs-opponent differentials, trains multiple classifiers (Random Forest, SVM, KNN, MLP) with hyperparameter tuning, and serves live predictions via a Flask web application. The dominant character is data research: the primary deliverable is a set of trained models and an analytical pipeline, with the web server being a secondary deployment artifact. The best-performing model is a Random Forest achieving ~50-51% test accuracy, demonstrating that football outcomes are modestly predictable from historical statistics. The codebase includes modular helper modules for feature engineering, data scaling, and evaluation plotting that are reusable for other sports or time-series classification tasks. However, the project suffers from hardcoded absolute paths, outdated dependencies (Python 3.7, sklearn 0.23.1), plain-text API keys in the repository, and a static dataset that will drift over time.