This repository contains a number of projects and reports of Data Camp platform in different fields, such as Android app market analysis on Google Play, Super Bowl data analysis, etc. The main objective is to develop practical skills in case study.
A brief description is given ahead:
The Super Bowl is a spectacle. There's drama in the form of blowouts, comebacks, and controversy in the games themselves. There are the ridiculously expensive ads, some hilarious, others gut-wrenching, thought-provoking, and weird. The half-time shows with the biggest musicians in the world, sometimes riding giant mechanical tigers or leaping from the roof of the stadium. And in this project, you will find out how some of the elements of this show interact with each other. You will answer questions like:
What are the most extreme game outcomes?
How does the game affect television viewership?
How have viewership, TV ratings, and ad cost evolved over time?
Who are the most prolific musicians in terms of halftime show performances?
The dataset used in this project was scraped and polished from Wikipedia. It is made up of three CSV files, one with game data, one with TV data, and one with halftime musician data for all 52 Super Bowls through 2018.
Mobile apps are everywhere. They are easy to create and can be lucrative. Because of these two factors, more and more apps are being developed. In this project, you will do a comprehensive analysis of the Android app market by comparing over ten thousand apps in Google Play across different categories. You'll look for insights in the data to devise strategies to drive growth and retention. The data for this project was scraped from the Google Play website. While there are many popular datasets for Apple App Store, there aren't many for Google Play apps, which is partially due to the increased difficulty in scraping the latter as compared to the former. The data files are as follows:
apps.csv: contains all the details of the applications on Google Play. There are 13 features that describe a given app.
user_reviews.csv: contains 100 reviews for each app, most helpful first. The text in each review has been pre-processed and attributed with three new features: Sentiment (Positive, Negative or Neutral), Sentiment Polarity and Sentiment Subjectivity.
This project comprising Web scrapping of Quotes to scrape. Web scrapping is a way to get information of web pages, specially when the information can be obtained in a systematic manner. In this example, the web page contains a lot of famous qoutes with their respective author.
web_scrap.py : Script connectS the server and request the information.