Project description: Data Analytics is all about finding valuable insights that help businesses take right decisions. In this Project, I have done exploratory data analysis using python. The dataset in action is IPL(Indian Premier League) dataset between 2008 and 2016. IPL is world’s most famous cricket tournament which is held every year, it is one of the most successful tournament. In this analysis I have tried to explore the data set using different plots.
The dataset is picked from kaggle. It consist of total 18 columns and 636 rows. The columns are namely:
(‘id’, ‘season’, ‘city’, ‘date’, ‘team1’, ‘team2’, ‘toss_winner’,’toss_decision’, ‘result’, ‘dl_applied’, ‘winner’, ‘win_by_runs’,’win_by_wickets’, ‘player_of_match’, ‘venue’, ‘umpire1’, ‘umpire2’,’umpire3’)
Just to get a rough idea about the dataset below is the variable(cloumn) details:
|id||636 non-null int64|
|season||636 non-null int64|
|city||629 non-null object|
|date||636 non-null object|
|team1||636 non-null object|
|team2||636 non-null object|
|toss_winner||636 non-null object|
|toss_decision||636 non-null object|
|result||636 non-null object|
|dl_applied||636 non-null int64|
|winner||633 non-null object|
|win_by_runs||636 non-null int64|
|win_by_wickets||636 non-null int64|
|player_of_match||633 non-null object|
|venue||636 non-null object|
|umpire1||635 non-null object|
|umpire2||635 non-null object|
|umpire3||0 non-null float64|
To view the notebook see GitHub.
Number of matches played in IPL:
Best IPL team:
City with most number of matches:
Venue with most number of matches:
All top 10 players:
Are match Winning and Toss Winning Correlated?
This notebook shows Exploratory Data Analysis of IPL dataset. There is lot more to explore in this dataset when one deep dives as which venue(stadium) had most number of wins, which city has most number of wins etc.