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Back to documentation-hicala

Data Mining Compilation

  1. https://github.com/hicala/news-classifier

    News Classifier

    Overview

    In this research project we took a political dataset (news.csv) from the 2016 US Presidential elections and created a machine learning model using Python to classify the news as REAL or FAKE. We implemented a TfidfVectorizer, initialized a PassiveAggressiveClassifier, and fit our model. Finally, we run an uncertainty evaluation of the model to obtain the level of accuracy.

  2. https://github.com/hicala/prj_911_kaggle

    Data analytical review of the 911 Call incindents in 2016

    Overview

    In this research I am analyzing the 911 call dataset.

Tools: Python, Numpy, Seaborn, Matplotlib, Pyplot

Data Source: Kaggle.

The data contains the following fields( all are declared as a String variable):

lat : Latitude
lng: Longitude
desc: Description of the Emergency
zip: Zipcode
title: Title
timeStamp: YYYY-MM-DD HH:MM:SS
twp: Township
addr: Address
e: Dummy variable (always 1)

  1. https://github.com/hicala/gdp_python-data-mining

    List of countries by nominal GDP

    Overview

    This App is a result of my personal efforts to master the web scraping process using Python and BeatifuSoup. The document contains all the step by steps about how to scrape a Wikipedia page using Python3 and Beautiful Soup and finally exporting it to a CSV file.

    1. https://github.com/hicala/piracy_reporting_centre_app

    Exploring Contemporary Sea Piracy. Data extraction from a Live Piracy & Armed Robbery Report

    Overview

    In this study the main goal is to evaluate the concentrations of the modern piracy incidents around the world. Modern-day pirates around the world share the legal designation of their historic brethren as “enemies of all mankind” because they disrupt and hinder the safe navigation of maritime vessels containing goods and people.

Piracy is a global crime which impedes the free movement of ships containing people and goods, with its attendant economic ramifications. The perpetrators are usually heavily armed, with sophisticated weapons to enable them to hijack a vessel or vessels and redirect them to their desired location for the payment of an expected ransom.

I am using Beautiful Soup for this Python app. Beautiful Soup is a Python library for parsing data out of HTML and XML files (aka webpages). It works with your favorite parser to provide idiomatic ways of navigating, searching, and modifying the parse tree.

The major concept with Beautiful Soup is that it allows you to access elements of your page by following the CSS structures, such as grabbing all links, all headers, specific classes, or more. It is a powerful library. Once we grab elements, Python makes it easy to write the elements or relevant components of the elements into other files, such as a CSV, that can be stored in a database or opened in other software.

The data I used came from Live Piracy & Armed Robbery Report 2020. Reference: https://www.icc-ccs.org/index.php/piracy-reporting-centre/live-piracy-report

  1. https://github.com/hicala/nba_roster_analytic

    Data extraction from a Atlanta Hawks Roster web site

    Overview

    This study is part of a serie of statistical analysis in the composition and salary earned by main and key players in the NBA.

I am using Beautiful Soup for the this Python app. Beautiful Soup is a Python library for parsing data out of HTML and XML files (aka webpages). It works with your favorite parser to provide idiomatic ways of navigating, searching, and modifying the parse tree.

The data I used came from Atlanta Hawks Roster. Reference: https://www.espn.com/nba/team/roster/_/name/atl/atlanta-hawks

  1. https://github.com/hicala/pm4py-core

    Public repository for the PM4Py (Process Mining for Python) project.

    Overview

    PM4Py is a python library that supports (state-of-the-art) process mining algorithms in python. It is completely open source and intended to be used in both academia and industry projects. PM4Py is a product of the Fraunhofer Institute for Applied Information Technology.

  2. https://github.com/hicala/pirple-py-data-mining-course

    This repository contains all practices from Pirple's "Data Mining With Python" course.

    Overview

    This repository contains all practices from Pirple's "Data Mining With Python" course.

  3. https://github.com/hicala/APIs-python-data-mining

    This repo explores the many ways to interact with twitter data through the twitter API. Specifically, there are three different methods explored here.

    Overview

    Twitter streaming API access code

  4. https://github.com/hicala/jaeger-analytics-flink

    Big data analytics for Jaeger using Apache Flink

    Overview

    This repository was created from the internal Uber repository used to run Flink jobs. It was created by stripping away Uber specific components, and hasn't been tested in it's current form. It is intended to serve as a starting point for a more generic OSS release.

  5. https://github.com/hicala/DataScience365

    DataScience365

    Overview

    It is about exploring the world of data science and its concept.

  6. https://github.com/hicala/Data-Mining-and-Machine-Learning-with-Python

    This repository includes teaching material for a Data Mining and Machine Learning class. The class is offered in Python

    Overview

    This repository includes teaching material for a Data Mining and Machine Learning class. The class is offered in Python

  7. https://github.com/hicala/data-science-portfolio

    Portfolio of data science projects completed by me for academic, self learning, and hobby purposes.

    Overview

    Repository containing portfolio of data science projects completed by me for academic, self learning, and hobby purposes. Presented in the form of iPython Notebooks, and R markdown files (published at RPubs).

For a more visually pleasant experience for browsing the portfolio, check out sajalsharma.com

  1. https://github.com/hicala/tdm_course

    In this repository you will find the Python and other associated files used in the practical guide of the "Introduction to Text and Data Mining" course.

    Overview

    Introduction to Text and Data Mining

This is an introductory course primarily intended for research support administrative staff, but others such as researchers, librarians and repository managers may also find this introductory course useful.

In this Github page you will not be able to find the course - this is available here.

Nonetheless, here you will be able to find and download locally all the Python files relating to the practical activities of the course and more specifically the "Text mining practical activities using the CORE API".

  1. https://github.com/hicala/mining

    programs for data mining

    Overview

    sample programs for data mining