🌄 The Cloud Native Interactive Landscape filters and sorts hundreds of projects and products, and shows details including GitHub stars, funding, first and last commits, contributor counts and headquarters location.
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Updated
Oct 4, 2024
🌄 The Cloud Native Interactive Landscape filters and sorts hundreds of projects and products, and shows details including GitHub stars, funding, first and last commits, contributor counts and headquarters location.
🌄 Open Source AI & Data Landscape - provides overview of top tier projects in the open source AI and Data ecosystem, shows projects through GitHub data, funding or market cap, first and last commits, contributor count and much other information.
Scrape crunchbase companies, people, investors, acquisitions data including website urls, social urls, emails, phone numbers, employee count, funding information etc.
Predicting the likelihood of success for startups and their founders
Merge and Acquisitions Prediction based on M&A information from Crunchbase.
Scraper to get data from crunchbase.com and read - write the data using SQLite database and JSON file.
I am assigned to a data collection task, to collect 2000 company information from [owler](www.owler.com) and [crunchbase](www.crunchbase.com) Collecting structured data by hand and feed them into an excel table is always boring and time consuming. So I tried to use web crawl method to solve it and save some time (50 hours work estimated).
Machine learning on Crunchbase data set
Analysis of information about startup companies done using machine learning and data analytics methods to predict the success of the startup companies.
A Ruby Library for CrunchBase API v4
A Python tool utilizing Selenium for automated extraction of funding round data from Crunchbase.
Scrape results from a curated list of unicorn startups for educational purposes and output as JSON
Use CrunchBase data from 2013, before it was relicensed to deny commercial use without payment.
a lean and sustainable conscientious cannabusiness; target market? Black market.
Thesis in partial fulfillment of the MSc in Information Systems & E-business at Copenhagen Business School. The thesis received the grade 10, as well as praise from the supervising PhD (https://www.linkedin.com/in/nielsbuuslassen) and the co-examiner (https://www.linkedin.com/in/bodilreumert/; Director at Talent Garden Rainmaking, Copenhagen, DK).
The repository of Team 5 for the HKUST course TEMG4952A "Special Project: Financial Investment Prototyping for UBS Zurich".
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