- NBA player statistics from 2014-2015 (partial season): data, data dictionary
**Goal:**Predict player position using assists, steals, blocks, turnovers, and personal fouls

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```# read the data into a DataFrame
import pandas as pd
url = 'https://raw.githubusercontent.com/kjones8812/DAT4-students/master/kerry/Final/NBA_players_2015.csv'
nba = pd.read_csv(url, index_col=0)
nba.head()

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In [2]:
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# examine the columns
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# examine the positions
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# map positions to numbers
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# create feature matrix (X) (use fields: 'ast', 'stl', 'blk', 'tov', 'pf')
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# create response vector (y)
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# import class
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# instantiate with K=5
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# fit with data
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# create a list to represent a player
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# make a prediction
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# calculate predicted probabilities
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# repeat for K=50
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# calculate predicted probabilities
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In [15]:
```# allow plots to appear in the notebook
%matplotlib inline
import matplotlib.pyplot as plt
# increase default figure and font sizes for easier viewing
plt.rcParams['figure.figsize'] = (6, 4)
plt.rcParams['font.size'] = 14