Alison Mitchell (alisonmitchell)

alisonmitchell

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Alison Mitchell's repositories

Stock-Prediction

Technical and sentiment analysis to predict the stock market with machine learning models based on historical time series data and news article sentiment collected using APIs and web scraping.

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Boston-Housing

Investigation of the Boston housing dataset to evaluate, train and test a regression model to predict house prices.

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Document-Metadata-Extract

An application to import, iterate over, open, extract metadata and output information for a list of PDF and Excel documents hosted on a website to identify missing metadata fields.

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my-first-binder

How to create a Binder-ready repository to transform into an interactive computational environment hosted in the cloud and shared so that the analysis code and dataset can be interactively explored.

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alisonmitchell.github.io

Portfolio website powered by Bootstrap and GitHub Pages: www.alisonmitchell.me

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Countries-Of-The-World

Exploratory Data Analysis of US Government data from the World Factbook to investigate GDP ($ per capita), Literacy (%) and Phones (per 1000).

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Django-Blog-App

A blogging website application for adding, deleting, editing and viewing blog posts. Additional features and functionality include saving and publishing drafts, user authentication and commenting.

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Django-Contact-App

A simple CRUD application where you can create contacts, edit, view and delete them.

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Domain-Checker

An application to import, iterate over, acquire WHOIS data and perform a DNS lookup for a list of domains. Regular expressions are then used to identify patterns and categorise domain names.

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Titanic

Exploration of a subset of the Titanic passenger manifest to create a predictive classification model to determine which passengers were more likely to survive.

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Django-Poll-App

A basic poll application with a public site to view and vote in polls, and an admin site to add, change and delete polls.

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Project-Euler

Python implementations applying programming fundamentals and algorithmic thinking to find simple, elegant and optimal solutions to problems, with documentation and testing.

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