对话一：需求分析
代码实现：数据清洗
import pandas as pd
def clean_data(file_path):
# 加载数据
data = pd.read_csv(file_path)
# 处理缺失值
data.fillna(data.mean(), inplace=True)
# 去除重复行
data.drop_duplicates(inplace=True)
return data
# 示例调用
cleaned_data = clean_data('soil_samples.csv')
print(cleaned_data.head())
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对话二：模型构建
代码实现：随机森林模型
from sklearn.ensemble import RandomForestRegressor
from sklearn.model_selection import train_test_split
def build_model(data):
X = data[['temperature', 'humidity', 'ph']]
y = data['yield']
# 划分训练集和测试集
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)
# 初始化模型
model = RandomForestRegressor(n_estimators=100, random_state=42)
# 训练模型
model.fit(X_train, y_train)
return model
# 示例调用
model = build_model(cleaned_data)
print("模型已成功构建！")
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总结