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Kaggle

Tutorial using Kobe Bryant Dataset – Part 3

This part is a kaggle tutorial using Kobe Bryant Dataset – Part 3. You can get the data from https://www.kaggle.com/c/kobe-bryant-shot-selection . What excited me was that this dataset is excellent to practice classification basics, feature engineering, and time series analysis. This is continued from here

#columns not needed
notNeeded = []
In [183]:
#Action type column
print(df['action_type'].unique())
['Jump Shot' 'Driving Dunk Shot' 'Layup Shot' 'Running Jump Shot'
 'Driving Layup Shot' 'Reverse Layup Shot' 'Reverse Dunk Shot'
 'Slam Dunk Shot' 'Turnaround Jump Shot' 'Tip Shot' 'Running Hook Shot'
 'Alley Oop Dunk Shot' 'Dunk Shot' 'Alley Oop Layup shot'
 'Running Dunk Shot' 'Driving Finger Roll Shot' 'Running Layup Shot'
 'Finger Roll Shot' 'Fadeaway Jump Shot' 'Follow Up Dunk Shot' 'Hook Shot'
 'Turnaround Hook Shot' 'Running Tip Shot' 'Jump Hook Shot'
 'Running Finger Roll Shot' 'Jump Bank Shot' 'Turnaround Finger Roll Shot'
 'Hook Bank Shot' 'Driving Hook Shot' 'Running Reverse Layup Shot'
 'Driving Finger Roll Layup Shot' 'Fadeaway Bank shot' 'Pullup Jump shot'
 'Finger Roll Layup Shot' 'Turnaround Fadeaway shot'
 'Driving Reverse Layup Shot' 'Driving Slam Dunk Shot'
 'Step Back Jump shot' 'Reverse Slam Dunk Shot' 'Turnaround Bank shot'
 'Running Finger Roll Layup Shot' 'Floating Jump shot'
 'Putback Slam Dunk Shot' 'Running Bank shot' 'Driving Bank shot'
 'Putback Layup Shot' 'Driving Jump shot' 'Putback Dunk Shot'
 'Pullup Bank shot' 'Running Slam Dunk Shot' 'Cutting Layup Shot'
 'Driving Floating Jump Shot' 'Running Pull-Up Jump Shot' 'Tip Layup Shot'
 'Driving Floating Bank Jump Shot' 'Turnaround Fadeaway Bank Jump Shot'
 'Cutting Finger Roll Layup Shot']
In [184]:
#Combined shot type 
print(df['combined_shot_type'].unique())
['Jump Shot' 'Dunk' 'Layup' 'Tip Shot' 'Hook Shot' 'Bank Shot']
In [185]:
#game event and game IDs not needed
notNeeded.extend(['game_event_id','game_id'])
In [29]:
#loc_x,loc_y,lat,lon
#sns.set_style('whitegrid')
sns.pairplot(df, vars=['loc_x', 'loc_y', 'lat', 'lon'], hue='shot_distance',size = 3)
Out[29]:
<seaborn.axisgrid.PairGrid at 0x197094bbd30>