足球运动员身价估计

标杆：足球运动员身价估计

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)

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)