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Exploratory and Predictive analysis of the Dota 2 dataset

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Dota 2 Project

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Description

Exploratory and Predictive analysis of the dataset

  • Defined heroes with highest pick and wining rates
  • Identified some heroes combinations with the highest winning probability
  • Built ML models for predicting a winning team and game mode

Overview

Dota 2 is a popular computer game with two teams of 5 players. At the start of the game each player chooses a unique hero with different strengths and weaknesses. The dataset is reasonably sparse as only 10 of 113 possible heroes are chosen in a given game. All games were played in a space of 2 hours on the 13th of August, 2016


Dataset info

Each row of the dataset is a single game with the following features (in the order in the vector):

  1. Team won the game (1 or -1)
  2. Cluster ID (related to location)
  3. Game mode (eg All Pick)
  4. Game type (eg. Ranked)
  5. till end: Each element is an indicator for a hero.
    Value of 1 indicates that a player from team '1' played as that hero and '-1' for the other team.
    Hero can be selected by only one player each game. This means that each row has five '1' and five '-1' values.

Software implementation

All source code used to generate the results and figures in the paper are in the dota2.ipynb. The calculations and figure generation are all run inside Jupyter notebooks. The data used in this study is provided in dataset folder and was taken from here.


Getting the code

You can download a copy of all the files in this repository by cloning the git repository:

git clone https://github.com/DthRazak/Dota2Project.git

or download a zip archive.


Examples

Hero pick analysis

20 Most playable heroes

Winning rate calculation

Hero WINS LOSES WIN RATE
Anti-Mage 6859 7858 46.61
Axe 10476 9375 52.77
Bane 1119 1369 44.98
Bloodseeker 5860 5674 50.81
Crystal Maiden 5428 4492 54.72
... ... ... ...
Terrorblade 2167 2269 48.85
Phoenix 1939 1838 51.34
Oracle 1120 1468 43.28
Winter Wyvern 1465 1808 44.76
Arc Warden 768 1001 43.41
112 rows × 3 columns

20 Heroes with highest Win-Rate

Mode Name Prediction (XGBoost Model)

XGBoost Model Tree

Feature importance

Feture importance


Authors


License

This project is licensed with the MIT License.

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