Skip to content

README.md

Latest commit

 

History

History
101 lines (55 loc) · 5.49 KB

README.md

File metadata and controls

101 lines (55 loc) · 5.49 KB

Back to documentation-hicala

Machine Learning 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/Artificial-Intelligence-Deep-Learning-Machine-Learning-Tutorials

    A comprehensive list of Deep Learning / Artificial Intelligence and Machine Learning tutorials - rapidly expanding into areas of AI/Deep Learning / Machine Vision / NLP and industry specific areas such as Climate / Energy, Automotives, Retail, Pharma, Medicine, Healthcare, Policy, Ethics and more.

    Overview

    I will be updating this tutorials site on a daily basis adding all relevant topcis, including latest researches papers from internet such as arxiv.org, BIORXIV - Specifically Neuroscience to name a few.

More importantly the applications of ML/DL/AI into industry areas such as Transportation, Medicine/Healthcare etc. will be something I'll watch with keen interest and would love to share the same with you. Finally, it is YOUR help I will seek to make it more useful and less boring, so please do suggest/comment/contribute!

  1. https://github.com/hicala/awesome-machine-learning

    A curated list of awesome Machine Learning frameworks, libraries and software.

    Overview

    A curated list of awesome machine learning frameworks, libraries and software (by language). Inspired by awesome-php.

  2. https://github.com/hicala/facial-expressions-classifier

    This project covers all the techniques and elements I masterd during the processing on a Machine Learning research.

    Overview

    This project covers all the techniques and elements I masterd during the processing on a Machine Learning research.

The main goal of this project is to classify human facial expressions and depict them to emojis. We build a convolution neural network to recognize facial emotions. Then we will map those emotions with the corresponding catoons, emojis or avatars.