机器学习科比投篮预测随机森林
时间: 2023-11-27 18:47:26 浏览: 109
根据提供的引用内容,设计思路是将科比投篮数据进行分析,找出对投篮结果有影响的标签,然后使用随机森林算法训练出可以预测科比是否能够投篮命中的模型。以下是一个简单的机器学习科比投篮预测随机森林的例子:
```python
# 导入必要的库
import pandas as pd
from sklearn.ensemble import RandomForestClassifier
from sklearn.model_selection import train_test_split
# 读取数据
data = pd.read_csv('kobe_data.csv')
# 数据预处理
data = data.drop(['game_event_id', 'game_id', 'lat', 'lon', 'team_id', 'team_name', 'matchup', 'shot_id'], axis=1)
data['remaining_time'] = data['minutes_remaining'] * 60 + data['seconds_remaining']
data = data.drop(['minutes_remaining', 'seconds_remaining'], axis=1)
data['home_play'] = data['home'] == data['htm']
data['away_play'] = data['home'] != data['htm']
data = data.drop(['home', 'htm'], axis=1)
data['action_type'] = pd.factorize(data['action_type'])[0]
data['combined_shot_type'] = pd.factorize(data['combined_shot_type'])[0]
data['shot_type'] = pd.factorize(data['shot_type'])[0]
data['shot_zone_basic'] = pd.factorize(data['shot_zone_basic'])[0]
data['shot_zone_area'] = pd.factorize(data['shot_zone_area'])[0]
data['shot_zone_range'] = pd.factorize(data['shot_zone_range'])[0]
data['opponent'] = pd.factorize(data['opponent'])[0]
data = data.dropna()
# 划分训练集和测试集
X = data.drop(['shot_made_flag'], axis=1)
y = data['shot_made_flag']
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)
# 训练模型
rf = RandomForestClassifier(n_estimators=100, max_depth=10, random_state=42)
rf.fit(X_train, y_train)
# 预测结果
y_pred = rf.predict(X_test)
# 输出准确率
print('Accuracy:', rf.score(X_test, y_test))
```
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