Gathering, cleaning, and analysing Twitter Archive Data.

In this project I will be gathering, assessing, cleaning and analysing data from the WeRateDogs Twitter archive data. WeRateDogs is a Twitter account with over 4 million followers that rates people's dogs with humurous captions. WeRateDogs has a unique rating system. The rating denominators are almost always 10 while the numerators are almost always greater than 10 i.e. 11/10, 12/10, 13/10 because "they're good dogs Brent".

The project will follow these steps:

1. Gathering
2. Assessment
3. Cleaning
4. Storing Cleaned Data
5. Analysis and Visualisation of Data
6. Reporting

I used Python 3.8.8 for this project and Juptyer Notebook as an IDE. Then main packages are as follows: pandas, numpy, requests, os, json, tweepy and matplotlib.pyplot. I also used additonal packages such as seaborn and skimage for further analysis and visualisation.

• 'twitter-archive-enhanced.csv': the enhanced archive data provided by Udacity, which contains extra columns for dog stages (doggo, floofer, pupper and puppo).
• 'image-predictions.tsv': image predictions that were compiled after the archive data was run through a neural network that can classify dog breeds.
• 'tweet-json.txt': a json text file gathered from Twitter's API.
• 'twitter_archive_master.csv': a master dataset created from merging the above 3 after assessment and cleaning, and to be used for analysis and visualisation.

• wrangle_act.ipynb: this is where all the code for wrangling and analysis is executed.
• wrangle_act.html: this is the html version of the IPython notebook.
• wrangle_report: this is a summary of the steps I took to assess, clean and store the data.
• act_report.pdf: this is includes insights and visualisations from the analysis.

Gorata Malose
Linkedin: Gorata Malose

This project is licensed under the MIT License and it was submitted by Gorata Malose as part of Udacity's Data Analyst Nanodegree programme.