opta球员大数据预测胜负_NBA球员数据分析及薪酬预测

import numpy as np
import pandas as pd
import seaborn as sns
import matplotlib.pyplot as plt
import statsmodels.api as sm  #statsmodels是一个Python模块，它提供对许多不同统计模型估计的类和函数，并且可以进行统计测试和统计数据的探索
import statsmodels.formula.api as smf
 
from sklearn.neighbors import KNeighborsRegressor
from sklearn.model_selection import train_test_split
from sklearn.neighbors import KNeighborsClassifier
 
from sklearn.metrics import mean_squared_error
sns.set_style("whitegrid")
plt.rcParams['font.sans-serif'] = ['SimHei']  # 用来正常显示中文标签
plt.rcParams['axes.unicode_minus'] = False  # 用来正常显示负号
plt.rcParams.update({'font.size': 20})
 
%matplotlib inline
 
adv_stats = pd.read_csv('.../data/player-data-wrangled/player2017/Adv-2017wrangled.csv')
adv_stats.drop(adv_stats.columns[[0, 1, 20, 25, 3, 4, 5, 6, 7, 30]], axis = 1, inplace = True)
adv_stats.columns
 
perGame = pd.read_csv('.../data/player-data-wrangled/player2017/PerGame-2017wrangled.csv')
perGame.drop(perGame.columns[[0, 1, 20, 25]], axis = 1, inplace = True)
perGame.columns
 
perGameAdv = perGame.merge(adv_stats, on = 'Player', how = 'inner')
perGameAdv.columns
 
fa = pd.read_csv('.../data/salary-data/fixed-fa-signings/fixed-2017.csv')
fa.drop(fa.columns[[0, 1, 2, 3, 4, 5, 6]], axis = 1, inplace = True)
fa.columns

final = perGameAdv.merge(fa, left_on = 'fix', right_on = 'name', how = 'inner')
# drop 'fix' and 'name
final.drop(final.columns[[27]], axis = 1, inplace = True)
final.head()
final.rename(columns={'PS.G':'POINTS'},inplace=True)
final2 = final.copy(deep = True)
sns.set(font_scale=1.5)
sns.set_style("whitegrid")
plt.figure(figsize=(10,6))
final['average'].hist(bins = 20,
       histtype = 'bar',
       align = 'left',
       orientation = 'vertical',
       alpha=0.5,
       normed =True)
final['average'].plot(kind='kde',style='k--')
# plt.xticks(np.linspace(0,40,9))
plt.xlabel('SALARY')
# plt.axvline(20.87,color='r')

sns.set(font_scale=1.5)
sns.set_style("whitegrid")
plt.figure(figsize=(10,6))
ax = sns.lmplot(x='Age', y='Salary', data=copy_data, hue='Pos', fit_reg=False, aspect=2,size=7, legend=False, scatter_kws={"s": 200})
ax.set(xlabel='AGE', ylabel='RPM (Real Plus Minus)',title="Salary by Position: 2017-2018 Season")
plt.legend(loc='upper left', title='Position')
 
plt.figure(figsize=(10,6))
ax = sns.barplot(x="Pos", y="Salary",data=copy_data,capsize=.2)
 
plt.figure(figsize=(10,6))
ax = sns.barplot(x="Pos", y="average",data=final_c,capsize=.2)

final.drop(final.columns[[0,1,3, 49]], axis = 1, inplace = True)
final.columns

# Check correlation between features 
sns.set(font_scale=1)
sns.set_style("whitegrid")
colormap = plt.cm.magma
 
plt.figure(figsize=(30,30))
 
plt.title('Pearson Correlation of NBA Salary Features', y=1.05, size=50)
 
corr = final.astype(float).corr().round(2)
# corr = smalls.astype(float).corr().round(2)
mask = np.zeros_like(corr, dtype=np.bool)
 
# mask upper diagonal of heatmap
mask[np.triu_indices_from(mask)] = True
 
sns.heatmap(corr,
            mask = mask, linewidths=0.1,vmax=1.0, square=True, cmap=colormap, linecolor='white', annot=True)

final2.columns

#Selecting candidates
final2.drop(final2.columns[[0, 1, 2, 3, 4, 5, 9, 10, 11, 12, 13, 14, 15, 16, 18, 19, 21,
                         25, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39,
                         41, 42, 49]], axis = 1, inplace = True)
final2.columns

# Check correlation between features 
sns.set(font_scale=2)
colormap = plt.cm.magma
plt.figure(figsize=(30,30))
 
plt.title('Pearson Correlation of NBA Salary Features', y=1.05, size=50)
 
corr = final2.astype(float).corr().round(2)
mask = np.zeros_like(corr, dtype=np.bool)
 
# mask upper diagonal of heatmap
mask[np.triu_indices_from(mask)] = True
 
sns.heatmap(corr,
            mask = mask,