#Data-driven
Showing 21 of 21 repositories tagged #data-driven, ranked by stars
Cloud-native application life-cycle orchestration. Keptn automates your SLO-driven multi-stage delivery and operations & remediation of your applications.
:bar_chart: :clipboard: Dashboards using YAML or JSON files
A fast data-driven routing library for Clojure/Script
Graph is a semantic database that is used to create data-driven applications.
🟣 A robust Swift state-management framework designed for complex applications, featuring an integrated ORM for efficient data handling.
Data-Driven Astrology 💫 Kerykeion is a Python library for astrology. It generates SVG charts and extracts detailed structured data for birth charts, synastry, transits, composite charts, and more.
💾 🔜📱 Type-safe data-driven CollectionView, TableView Framework. (We can also use ASCollectionNode)
A Large-Scale Multimodal Car Dataset with Computational Fluid Dynamics Simulations and Deep Learning Benchmarks
A Tool for Writing Declarative, Type-Safe and Data-Driven Applications in SwiftUI using GraphQL
Feature Flag Management and A/B Testing platform
database gateway for building data-driven applications
A Python toolbox for quantitative, reproducible flow cytometry analysis
Python library that implements DeePC: Data-Enabled Predictive Control
Sample code of application examples to build microservices with converged Oracle database and multi-cloud / hybrid cloud services
moai is a PyTorch-based AI Model Development Kit (MDK) created to improve data-driven model workflows, design and reproducibility.
Awesome list of Top Performing Bangladeshi Developers on Github
Information-Theoretic Measures for Revealing Variable Interactions
⚡ Instantly turn your python function into web app.
re|thread is an open collective of computer scientists, artists, and designers, in Stockholm (Sweden)
Presentations on Quantified Self and Self-Tracking with Python
使用 Colab 建立，利用python，分別對台灣的證券交易所和yahoo奇摩股市進行股票價格的網頁爬蟲，並對爬取下來的資料以data-driven的方式進行股市分析，其中包括"計算技術指標"、"K線可視化"、"資料清洗與轉置"等，接著以"ARIMA模型"、"機器學習(線性回歸、決策樹、隨機森林"、"深度學習(ANN、CNN、LSTM)"訓練模型並進行股價漲跌的預測，最後搭配交易策略和技術指標(SMA、RSI、MACD、KD等)，並以績效回測的方式進行報酬率的必較，實現量化交易。