支持泰国的下一代人工智能创业公司

# 导入必要的库
import pandas as pd
from sklearn.model_selection import train_test_split
from sklearn.ensemble import RandomForestClassifier
from sklearn.metrics import accuracy_score

# 加载数据集
data = pd.read_csv('medical_data.csv')

# 将数据分为特征和标签
X = data.drop('diagnosis', axis=1)
y = data['diagnosis']

# 将数据分为训练集和测试集
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)

# 训练模型
model = RandomForestClassifier()
model.fit(X_train, y_train)

# 进行预测
predictions = model.predict(X_test)

# 评估模型
accuracy = accuracy_score(y_test, predictions)
print(f'准确率：{accuracy * 100:.2f}%')

# Importing necessary libraries
import pandas as pd
from sklearn.model_selection import train_test_split
from sklearn.ensemble import RandomForestClassifier
from sklearn.metrics import accuracy_score

# Load dataset
data = pd.read_csv('medical_data.csv')

# Splitting data into features and labels
X = data.drop('diagnosis', axis=1)
y = data['diagnosis']

# Splitting data into training and testing sets
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)

# Training the model
model = RandomForestClassifier()
model.fit(X_train, y_train)

# Making predictions
predictions = model.predict(X_test)

# Evaluating the model
accuracy = accuracy_score(y_test, predictions)
print(f'Accuracy: {accuracy * 100:.2f}%')