Data Science Project
Pubg work.rar contains 2 files
- The ipynb file [Note if you try to open the ipynb from github site you will find missing markdowns as I use html and some encodings that is not available on github so you must open it with your notebook].
- The dataset
By: Ahmed Metwalli
Content
1. Introduction
2. Data Explore And Data Wrangling
3. Choosing Area Of Interest
4. Data Visualization
5. Predictions
Introduction
Pubg is a great multiplayer online game that has conquered the market since 2018 till now. It contains my features and analytics to explore from variety of players. In this report we would like to investigate how do a player finish in the first place in terms of kill [killPlace #1] performing wrangling visualization and predictions.
About initial data train_v2.csv: Shape 4446966 x 29
The data set, available here ['https://www.kaggle.com/c/pubg-finish-placement-prediction/data?select=train_V2.csv'], consists of 29 statistics collected for 4446966 players. The statistics are:
Id: player’s Id
assists: number of enemy that this player damaged and were killed by teammates
boosts: number of boost items used
heals: number of healing items used
revives: number of times this player revived teammates
damageDealt: total damage dealt. Note: Self inflicted damage is subtracted
DBNOs: number of enemy knocked
killPlace: ranking in match for number of enemy killed
killPoints: kills-based external ranking of player [“0” should be treated as a “None” for rankPoints equal to -1]
killStreaks: max number of enemy killed in a short amount of time
kills: number of enemy killed
headshotKills: number of enemy killed with headshots
roadKills: number of kills while in a vehicle
teamKills: number of times this player killed a teammate
longestKill: longest distance between this player and a player killed at time of death
rideDistance: total distance traveled in vehicles measured in meters
swimDistance: total distance traveled by swimming measured in meters
vehicleDestroys: number of vehicles destroyed
walkDistance: total distance traveled on foot measured in meters
weaponsAcquired: number of weapons picked up
winPoints: win-based external ranking for player [“0” should be treated as a “None” for rankPoints equal to -1]
winPlacePerc: percentile winning placement, where 1 corresponds to 1st place. It is calculated off of maxPlace [TARGET]
rankPoints: Elo-like ranking of player, inconsistent and is being deprecated in the API’s next version
groupId: ID group within a match. In different matches the same group of players will have a different IDs
matchDuration: duration of match in seconds
matchId: ID to identify match
matchType: game mode such as: “solo”, “duo”, “squad”, “solo-fpp”, “duo-fpp”, “squad-fpp”, and other custom modes
numGroups: number of groups we have data for in the match
maxPlace: worst placement we have data for in the match
About the wrangled data: (The area of interest we have chosen 8 features to use in order to answer the research question)
Writing color encoding along the report:
Questions: Indigo
Notes Or Wranglings: Red
Explanations And Comments: Blue