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Name: Lalith
Type: User
Company: Synopsys
Bio: R&D Engineer ll
Twitter: Lalith123456789
Location: Hyderabad, India
Name: Lalith
Type: User
Company: Synopsys
Bio: R&D Engineer ll
Twitter: Lalith123456789
Location: Hyderabad, India
Predicting the Air Quality Index from Web Scrapping to Deployment of the model
Checking the strength of a given Password from 0,1 & 2 means Weak password,Medium strength of password & Strong password respectively using ML MultiClassification
Open Source Computer Vision Library
I have tries to solve "Pima diabetes" dataset on my own from Data Pre-Proccessing,Algorithms and predicting the model with enough accuracy.
Problem Statement: In a PUBG game, up to 100 players start in each match (matchId). Players (Id) can be on teams (groupId) which get ranked at the end of the game (winPlacePerc) based on how many other teams are still alive when they are eliminated. During the game, players can pick up different amunitions, revive downed-but-not-dead (knocked) teammates, drive vehicles, swim, run, shoot, and experience all of the consequences -- such as falling too far or running themselves over and eliminating themselves. The team at PUBG has made official game data available for the public to explore and scavenge outside of "The Blue Circle." This workshop is not an official or affiliated PUBG site. Its based on the data collected by Kaggle and made available through the PUBG Developer API. You are provided with a large number of anonymized PUBG game stats, formatted so that each row contains one player's post-game stats. The data comes from matches of all types: solos, duos, squads, and custom; there is no guarantee of there being 100 players per match, nor at most 4 player per group. Goal: Perform the PUBG data analysis and answer the following questions: Does killing more people increases the chance of winning the game? Hint: Use the correlation between the match winning percentage and number of kills to determine the relationship How do we catch the fraudsters in the game? Hint: Use various logical conditions based on game knowledge to determine fraudsters in the game Can we predict the finishing position of a player in the game? Hint: Regression Problem: Train and test a model using regression algorithm to predict the final position of the player at the end of the game. Create a model which predicts players' finishing placement based on their final stats, on a scale from 1 (first place) to 0 (last place).
Analysis of PubG game using Python 3.6
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