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apache_sparkler_post_processing

This code gets connected to Solr DB created for Sparkler Crawled Data to do further data extraction, classification, filtering and insights generation using various Machine Learning models. The ML models are capable of using keywords list from user, extract features from URL content, and classify (score) output and update Solr parameter accordingly. Apache Sparkler Link: https://github.com/USCDataScience/sparkler

autopilot

A self driving car model for humans.

domain_googleapi_top_url

This project uses Google Search API to provide a list of most occurred urls based on domain keywords and phrases list. The code generates the phrases first based on the provided keywords and then use them for searching. After each searching, top 10 urls are considered and added to a dictionary. Iterating through all keywords, the dictionary is finally sorted based on the frequency of occurrence.

domains_top_url_list_googleapi

This project uses Google Search API to provide a list of most occurred urls based on domain keywords and phrases list. The code generates the phrases first based on the provided keywords and then use them for searching. After each searching, top 10 urls are considered and added to a dictionary. Iterating through all keywords, the dictionary is finally sorted based on the frequency of occurrence.

netron

Visualizer for deep learning and machine learning models

polar.usc.edu

Polar USC activities related to NSF Polar CyberInfrastructure program at the University of Southern California

sce

sce-domain-discovery

Domain Discovery for the Sparkler Crawl Environment

sce_training

sf_crime

Crime Data from Kaggle for San Francisco City

simin

smart-home-bot

sparkler

Spark-Crawler : Evolving Apache Nutch to run on Spark.

uscdatascience.github.io

USC Information Retrieval and Data Science Group