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opgg-web-scrape's Introduction

opgg-web-scrape

Scrape opgg page
-trying to scrape opgg as part of my interest-

Google colab: Open In Colab

Firstly,
Input for url

  1. Enter the region of the summoner
  • South Korea: kr
  • North America: na
  • EU West: euw
  • EU Nordic & East: eune
  • Oceania: oce
  • Japan: jp
  • Brazil: br
  • LAS: las
  • LAN: lan
  • Russia: ru
  • Turkey: tr
  1. Enter summoner's name

The maximum number of games that could be scraped from opgg using while-true: 500 ±10

The ipynb file contains three parts:
a. Scrape details of specific player (that has an opgg page) from opgg website and store the details in dataframe
b. Some simple analysis and simple plots
c. One simple machine learning method (decision tree) for predicting the status (victory or defeat) of the game, in this case, only "Ranked Solo" queue type were used.

a. Web scraping
For web scraping, Selenium and Beautiful Soup were used. The data were store in form of dataframe with the help of pandas.
b. Simple analysis
Different plots were created, eg.
  • Pie Chart
  • Bar chart (horizontal and vertical)
  • Scatter matrix
  • Scatter plot
  • KDE plot
  • Heatmap
  • Boxplot
  • Line graph
  • Different tables were generated using different conditions, eg. average victory and defeat time for each champion. groupby and apply were mainly involved.

    c. Machine Learning
    Play with simple machine learning. In this project, decision tree was involved. Two different model evaluation methods were involved: train test split and cross validation. Both generate different results.

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