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# macOS (使用Homebrew)
brew install python

# Ubuntu/Debian
sudo apt update
sudo apt install python3 python3-pip

# Windows
# 从 python.org 下载安装包

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# 创建虚拟环境
python -m venv ai_env

# 激活虚拟环境
# macOS/Linux
source ai_env/bin/activate
# Windows
ai_env\Scripts\activate

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pip install numpy pandas matplotlib seaborn

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pip install scikit-learn xgboost lightgbm

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# TensorFlow
pip install tensorflow

# PyTorch
pip install torch torchvision torchaudio

# Keras
pip install keras

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pip install plotly bokeh altair

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import numpy as np
import pandas as pd
from sklearn.model_selection import train_test_split
from sklearn.linear_model import LinearRegression
from sklearn.metrics import mean_squared_error
import matplotlib.pyplot as plt

# 生成示例数据
np.random.seed(42)
X = np.random.rand(100, 1) * 10
y = 2 * X.ravel() + 1 + np.random.randn(100)

# 数据分割
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 = LinearRegression()
model.fit(X_train, y_train)

# 预测和评估
y_pred = model.predict(X_test)
mse = mean_squared_error(y_test, y_pred)
print(f"均方误差: {mse:.2f}")

# 可视化结果
plt.figure(figsize=(10, 6))
plt.scatter(X_test, y_test, color='blue', label='实际值')
plt.plot(X_test, y_pred, color='red', linewidth=2, label='预测值')
plt.xlabel('特征值')
plt.ylabel('目标值')
plt.legend()
plt.title('线性回归预测结果')
plt.show()