The Big Game is the annual American football championship and one of the most celebrated events in the history of sports. It is not only one of the most-watched sports events across the globe, it also has the reputation of being the second-largest day in terms of food consumption in the US. Every fan eagerly waits for the Game Day to find out if their favorite team wins.

A sports betting firm has utilized the data augmentation technique to synthesize a data set of championship outcome of the Big Game's participants and other data. Your task is to generate a model to determine and classify whether a given team will win the championship or not.

Data

Data Files

train.csv : contains the training data [6500 x 9]

test.csv : contains the test data [3500 x 8]

sample_submission.csv : example for submission format of Results.csv

Data Description

Columns	Description
ID	A unique identifier for the record
Average_Player_Age	Average age of the players on the team
Coach_Experience_Level	Level of experience of the head coach
Number_Of_First_Round_Draft_Picks	Number of players on the team that were first round draft picks
Number_Of_Injured_Players	Number of injured players on the team
Number_Of_Wins_This_Season	Number of wins that the team has leading up to the Big Game
Playing_Style	Represents playing style in levels
Previous_SB_Wins	Number of times the team has won the game in the past
Team_Value	Total value of the team in USD [Less than four billion implies greater than 3 billion]
Won_Championship [Target]	1 signifies winning the game 0 signifies losing the game

Submission Format

You are required to your predictions in a .csv file and upload it to 'Upload File'.

Evaluation Criteria

\(score = {100 * f1\_score(actual\_values, predicted\_values, average='binary')}\)