足球运动员身价估计

标杆：足球运动员身价估计     

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门将/非门将随机森林(Python)

该模型预测结果的MAE为：26.7350

# -*- coding: utf-8 -*-
import pandas as pd
import numpy as np
from datetime import date
from sklearn.ensemble import RandomForestRegressor


# 读取数据
train = pd.read_csv('train.csv')
test = pd.read_csv('test.csv')
submit = pd.read_csv('sample_submit.csv')


# 获得球员年龄
today = date(2018, 4, 15)

train['birth_date'] = pd.to_datetime(train['birth_date'])
train['age'] = (today - train['birth_date']).apply(lambda x: x.days) / 365.

test['birth_date'] = pd.to_datetime(test['birth_date'])
test['age'] = (today - test['birth_date']).apply(lambda x: x.days) / 365.


# 获得球员最擅长位置上的评分
positions = ['rw', 'rb', 'st', 'lw', 'cf', 'cam', 'cm', 'cdm', 'cb', 'lb', 'gk']

train['best_pos'] = train[positions].max(axis=1)
test['best_pos'] = test[positions].max(axis=1)


# 计算球员的身体质量指数(BMI)
train['BMI'] = 10000. * train['weight_kg'] / (train['height_cm'] ** 2)
test['BMI'] = 10000. * test['weight_kg'] / (test['height_cm'] ** 2)


# 判断一个球员是否是守门员
train['is_gk'] = train['gk'] > 0
test['is_gk'] = test['gk'] > 0


# 用多个变量准备训练随机森林
test['pred'] = 0
cols = ['height_cm', 'weight_kg', 'potential', 'BMI', 'pac',
        'phy', 'international_reputation', 'age', 'best_pos']


# 用非守门员数据训练随机森林
reg_ngk = RandomForestRegressor(random_state=100)
reg_ngk.fit(train[train['is_gk'] == False][cols], train[train['is_gk'] == False]['y'])

preds = reg_ngk.predict(test[test['is_gk'] == False][cols])
test.loc[test['is_gk'] == False, 'pred'] = preds


# 用守门员数据训练随机森林
reg_gk = RandomForestRegressor(random_state=100)
reg_gk.fit(train[train['is_gk'] == True][cols], train[train['is_gk'] == True]['y'])

preds = reg_gk.predict(test[test['is_gk'] == True][cols])
test.loc[test['is_gk'] == True, 'pred'] = preds


# 输出预测值
submit['y'] = np.array(test['pred'])
submit.to_csv('my_RF_prediction.csv', index=False)

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四个变量的决策树模型(Python)

该模型预测结果的MAE为：39.0321

# -*- coding: utf-8 -*-
import pandas as pd
import numpy as np
from datetime import date
from sklearn.tree import DecisionTreeRegressor


# 读取数据
train = pd.read_csv('train.csv')
test = pd.read_csv('test.csv')
submit = pd.read_csv('sample_submit.csv')


# 获得球员年龄
today = date(2018, 4, 15)

train['birth_date'] = pd.to_datetime(train['birth_date'])
train['age'] = (today - train['birth_date']).apply(lambda x: x.days) / 365.

test['birth_date'] = pd.to_datetime(test['birth_date'])
test['age'] = (today - test['birth_date']).apply(lambda x: x.days) / 365.


# 获得球员最擅长位置上的评分
positions = ['rw', 'rb', 'st', 'lw', 'cf', 'cam', 'cm', 'cdm', 'cb', 'lb', 'gk']

train['best_pos'] = train[positions].max(axis=1)
test['best_pos'] = test[positions].max(axis=1)


# 用‘潜力’，‘国际知名度’，‘年龄’，‘最擅长位置评分’这四个变量来建立决策树模型
cols = ['potential', 'international_reputation', 'age', 'best_pos']

reg = DecisionTreeRegressor(random_state=100)
reg.fit(train[cols], train['y'])


# 输出预测值
submit['y'] = reg.predict(test[cols])
submit.to_csv('my_DT_prediction.csv', index=False)