展望未来：在【PyCharm】中结合【机器学习】实现高效的图形化处理

import pandas as pd

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

# 数据预处理（示例）
data = data.dropna()  # 删除缺失值
data['feature'] = data['feature'].apply(lambda x: x / 100)  # 特征缩放

import matplotlib.pyplot as plt
import seaborn as sns

# 绘制直方图
plt.figure(figsize=(10, 6))
sns.histplot(data['feature'], kde=True)
plt.title('Feature Distribution')
plt.show()

# 绘制散点图探索特征间关系
plt.figure(figsize=(10, 6))
sns.scatterplot(x='feature1', y='feature2', data=data)
plt.title('Feature1 vs Feature2')
plt.show()

from sklearn.model_selection import train_test_split
from sklearn.linear_model import LinearRegression
from sklearn.metrics import mean_squared_error

# 划分数据集
X_train, X_test, y_train, y_test = train_test_split(data[['feature']], data['target'], test_size=0.2, random_state=42)

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

# 预测与评估
y_pred = model.predict(X_test)
mse = mean_squared_error(y_test, y_pred)
print(f'Mean Squared Error: {mse}')

# 绘制回归线
plt.figure(figsize=(10, 6))
plt.scatter(X_test, y_test, color='black', label='Actual data')
plt.plot(X_test, y_pred, color='blue', linewidth=3, label='Predicted regression line')
plt.xlabel('Feature')
plt.ylabel('Target')
plt.title('Linear Regression Model')
plt.legend()
plt.show()

import seaborn as sns
import pandas as pd

# 假设data是一个DataFrame，其中包含'category'和'target'两列
# 这里我们使用seaborn的内置数据集tips作为示例
data = sns.load_dataset('tips')

# 绘制箱型图
plt.figure(figsize=(10, 6))
sns.boxplot(x='day', y='total_bill', data=data)
plt.title('Distribution of Total Bill by Day of Week')
plt.xlabel('Day of Week')
plt.ylabel('Total Bill')
plt.show()

from sklearn.datasets import make_classification
from sklearn.model_selection import train_test_split
from sklearn.linear_model import LogisticRegression
from sklearn.preprocessing import StandardScaler
import numpy as np

# 生成模拟数据
X, y = make_classification(n_samples=1000, n_features=2, n_redundant=0, n_clusters_per_class=1, random_state=42)

# 数据预处理
scaler = StandardScaler()
X_scaled = scaler.fit_transform(X)

# 划分数据集
X_train, X_test, y_train, y_test = train_test_split(X_scaled, y, test_size=0.2, random_state=42)

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

# 绘制决策边界
def plot_decision_boundary(model, X, y, h=0.02):
    x_min, x_max = X[:, 0].min() - 1, X[:, 0].max() + 1
    y_min, y_max = X[:, 1].min() - 1, X[:, 1].max() + 1
    xx, yy = np.meshgrid(np.arange(x_min, x_max, h), np.arange(y_min, y_max, h))
    Z = model.predict(np.c_[xx.ravel(), yy.ravel()])
    Z = Z.reshape(xx.shape)
    plt.contourf(xx, yy, Z, alpha=0.4)
    plt.scatter(X[:, 0], X[:, 1], c=y, s=40, edgecolor='k')

plt.figure(figsize=(8, 6))
plot_decision_boundary(model, X_train, y_train)
plt.title('Logistic Regression Decision Boundary')
plt.xlabel('Feature 1')
plt.ylabel('Feature 2')
plt.show()