This Project applies various methods to a dataset to clean, transform, visualise and report on observations.
The chosen dataset is titled "Funding Successful Projects on Kickstarter" and can be found on Kaggle here, uploaded by user Lathwal
The dataset was released by Kickstarter, a crowdfunding company that connects community investors with start-up projects in an 'all-or-nothing' fashion: The user sets a goal for their project, and if it falls short by even $1, zero funding is attained.
The data was initially released from the perspective of the company, in that it had an interest of predicting the success of a project. There is also, however, information that potential creators may find useful.
This project aims to address the initial business objective set by Kickstarter: to help predict whether a project will be successfully funded. Various classification and clustering methods will be used to achieve this.
- GitHub repository (with source code and sample csv file) can be found here.
A pythonic tool (importing no libraries) that processes a given CSV file, forms statistical information and outputs that information. User calls main(csvfile, year, type) such that they call the file, then the year of interest, and then whether they want info on general statistics (type = 'stats') or on correlations (type = 'corr').
- GitHub repository (with source code) can be found here.
A program to process WARC files and extract information from the HTML data within. The program analyses text to produce insights related to public sentiment, in several countries, towards their government. The user inputs 3 arguments: a WARC (web archive) file to be processed, a text file containing words defined as 'positive', and a text file containing words defined as 'negative'. The output produced is 4 lists: the first 3 being floats of statistics related to the counts of positive and negative words, and the last list being a list of the top-5 most occurring domain names in the file, along with their count.