# 使用AI进行信用风险评估
$ python
>>> import pandas as pd
>>> import numpy as np
>>> from sklearn.model_selection import train_test_split
>>> from sklearn.ensemble import GradientBoostingClassifier
>>> from sklearn.metrics import accuracy_score, classification_report

# 加载数据集
data = pd.read_csv(‘credit_risk_dataset.csv’)

# 数据预处理
data = data.dropna()
data[‘income’] = data[‘income’].astype(float)

# 特征和标签
X = data[[‘income’, ‘age’, ‘loan_amount’, ‘loan_term’]]
y = data[‘default’]

# 分割数据
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 = GradientBoostingClassifier()
model.fit(X_train, y_train)

# 预测
y_pred = model.predict(X_test)

# 评估
print(f”Accuracy: {accuracy_score(y_test, y_pred):.4f}”)
print(classification_report(y_test, y_pred))

# 特征重要性
feature_importance = pd.DataFrame({
‘feature’: X.columns,
‘importance’: model.feature_importances_
}).sort_values(‘importance’, ascending=False)
print(feature_importance)

# 使用AI进行欺诈检测
$ python
>>> import pandas as pd
>>> import numpy as np
>>> from sklearn.preprocessing import StandardScaler
>>> from sklearn.ensemble import IsolationForest
>>> from sklearn.metrics import classification_report

# 加载数据集
data = pd.read_csv(‘credit_card_transactions.csv’)

# 数据预处理
X = data.drop([‘transaction_id’, ‘timestamp’, ‘label’], axis=1)
y = data[‘label’] # 1表示欺诈，0表示正常

# 特征标准化
scaler = StandardScaler()
X_scaled = scaler.fit_transform(X)

# 训练孤立森林模型
model = IsolationForest(contamination=0.01, random_state=42)
y_pred = model.fit_predict(X_scaled)

# 转换预测结果：-1表示异常（欺诈），1表示正常
y_pred = np.where(y_pred == -1, 1, 0)

# 评估
print(classification_report(y, y_pred))

# 使用AI进行疾病诊断
$ python
>>> import tensorflow as tf
>>> from tensorflow.keras.models import Sequential
>>> from tensorflow.keras.layers import Conv2D, MaxPooling2D, Flatten, Dense
>>> from tensorflow.keras.preprocessing.image import ImageDataGenerator

# 构建模型
model = Sequential([
Conv2D(32, (3, 3), activation=’relu’, input_shape=(224, 224, 3)),
MaxPooling2D((2, 2)),
Conv2D(64, (3, 3), activation=’relu’),
MaxPooling2D((2, 2)),
Conv2D(128, (3, 3), activation=’relu’),
MaxPooling2D((2, 2)),
Flatten(),
Dense(128, activation=’relu’),
Dense(10, activation=’softmax’)
])

# 编译模型
model.compile(optimizer=’adam’,
loss=’categorical_crossentropy’,
metrics=[‘accuracy’])

# 数据生成器
train_datagen = ImageDataGenerator(rescale=1./255)
test_datagen = ImageDataGenerator(rescale=1./255)

# 加载数据
train_generator = train_datagen.flow_from_directory(
‘medical_images/train’,
target_size=(224, 224),
batch_size=32,
class_mode=’categorical’
)

test_generator = test_datagen.flow_from_directory(
‘medical_images/test’,
target_size=(224, 224),
batch_size=32,
class_mode=’categorical’
)

# 训练模型
history = model.fit(
train_generator,
steps_per_epoch=len(train_generator),
epochs=10,
validation_data=test_generator,
validation_steps=len(test_generator)
)

# 评估模型
loss, accuracy = model.evaluate(test_generator)
print(f”Test accuracy: {accuracy:.4f}”)

# 使用AI进行药物分子筛选
$ python
>>> import numpy as np
>>> import pandas as pd
>>> from rdkit import Chem
>>> from rdkit.Chem import Descriptors
>>> from sklearn.model_selection import train_test_split
>>> from sklearn.ensemble import RandomForestClassifier

# 加载数据集
data = pd.read_csv(‘molecule_dataset.csv’)

# 计算分子描述符
def calculate_descriptors(smiles):
mol = Chem.MolFromSmiles(smiles)
if mol is None:
return [0]*10
return [
Descriptors.MolWt(mol),
Descriptors.MolLogP(mol),
Descriptors.NumHDonors(mol),
Descriptors.NumHAcceptors(mol),
Descriptors.TPSA(mol),
Descriptors.NumRotatableBonds(mol),
Descriptors.NumAromaticRings(mol),
Descriptors.NumAliphaticRings(mol),
Descriptors.NumSaturatedRings(mol),
Descriptors.FractionCSP3(mol)
]

# 应用描述符计算
data[‘descriptors’] = data[‘smiles’].apply(calculate_descriptors)
X = np.array(data[‘descriptors’].tolist())
y = data[‘activity’] # 1表示有活性，0表示无活性

# 分割数据
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)

# 预测
y_pred = model.predict(X_test)

# 评估
from sklearn.metrics import accuracy_score, classification_report
print(f”Accuracy: {accuracy_score(y_test, y_pred):.4f}”)
print(classification_report(y_test, y_pred))

# 使用AI进行设备预测性维护
$ python
>>> import pandas as pd
>>> import numpy as np
>>> from sklearn.model_selection import train_test_split
>>> from sklearn.ensemble import RandomForestRegressor
>>> from sklearn.metrics import mean_squared_error

# 加载传感器数据
data = pd.read_csv(‘sensor_data.csv’)

# 数据预处理
data = data.dropna()
X = data.drop([‘timestamp’, ‘failure’], axis=1)
y = data[‘failure’] # 剩余使用寿命

# 分割数据
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 = RandomForestRegressor()
model.fit(X_train, y_train)

# 预测
y_pred = model.predict(X_test)

# 评估
mse = mean_squared_error(y_test, y_pred)
rmse = np.sqrt(mse)
print(f”RMSE: {rmse:.4f}”)

# 特征重要性
feature_importance = pd.DataFrame({
‘feature’: X.columns,
‘importance’: model.feature_importances_
}).sort_values(‘importance’, ascending=False)
print(feature_importance)

# 使用协同过滤进行商品推荐
$ python
>>> import pandas as pd
>>> import numpy as np
>>> from surprise import Dataset, Reader, KNNBasic
>>> from surprise.model_selection import train_test_split
>>> from surprise import accuracy

# 加载数据
reader = Reader(rating_scale=(1, 5))
data = Dataset.load_from_df(pd.read_csv(‘user_ratings.csv’)[[‘user_id’, ‘product_id’, ‘rating’]], reader)

# 分割数据
trainset, testset = train_test_split(data, test_size=0.2, random_state=42)

# 训练模型
model = KNNBasic(sim_options={‘name’: ‘cosine’, ‘user_based’: True})
model.fit(trainset)

# 预测
predictions = model.test(testset)

# 评估
print(f”RMSE: {accuracy.rmse(predictions):.4f}”)
print(f”MAE: {accuracy.mae(predictions):.4f}”)

# 为用户推荐商品
def get_recommendations(user_id, model, trainset, n=10):
# 获取用户未评分的商品
user_items = set([item for (user, item, rating) in trainset.ur[trainset.to_inner_uid(user_id)]])
all_items = set(range(trainset.n_items))
items_to_predict = list(all_items – user_items)

# 预测评分
predictions = [model.predict(user_id, trainset.to_raw_iid(item)) for item in items_to_predict]

# 按预测评分排序
predictions.sort(key=lambda x: x.est, reverse=True)

# 返回前n个推荐
return [(trainset.to_raw_iid(item.iid), item.est) for item in predictions[:n]]

# 示例：为用户1推荐商品
recommendations = get_recommendations(1, model, trainset)
print(“Recommendations for user 1:”)
for product_id, score in recommendations:
print(f”Product {product_id}: {score:.2f}”)

# 使用AI进行交通流量预测
$ python
>>> import pandas as pd
>>> import numpy as np
>>> from sklearn.preprocessing import MinMaxScaler
>>> from tensorflow.keras.models import Sequential
>>> from tensorflow.keras.layers import LSTM, Dense

# 加载交通流量数据
data = pd.read_csv(‘traffic_data.csv’)
data[‘timestamp’] = pd.to_datetime(data[‘timestamp’])
data = data.set_index(‘timestamp’)

# 数据预处理
scaler = MinMaxScaler(feature_range=(0, 1))
scaled_data = scaler.fit_transform(data[‘traffic_volume’].values.reshape(-1, 1))

# 创建时间序列数据
def create_dataset(data, look_back=24):
X, y = [], []
for i in range(len(data) – look_back):
X.append(data[i:(i + look_back), 0])
y.append(data[i + look_back, 0])
return np.array(X), np.array(y)

X, y = create_dataset(scaled_data, look_back=24)
X = np.reshape(X, (X.shape[0], X.shape[1], 1))

# 分割数据
train_size = int(len(X) * 0.8)
test_size = len(X) – train_size
X_train, X_test = X[0:train_size], X[train_size:len(X)]
y_train, y_test = y[0:train_size], y[train_size:len(y)]

# 构建LSTM模型
model = Sequential([
LSTM(50, return_sequences=True, input_shape=(24, 1)),
LSTM(50),
Dense(1)
])

# 编译模型
model.compile(optimizer=’adam’, loss=’mean_squared_error’)

# 训练模型
model.fit(X_train, y_train, epochs=50, batch_size=32, validation_data=(X_test, y_test))

# 预测
y_pred = model.predict(X_test)

# 反归一化
y_pred = scaler.inverse_transform(y_pred)
y_test = scaler.inverse_transform(y_test.reshape(-1, 1))

# 评估
from sklearn.metrics import mean_squared_error
rmse = np.sqrt(mean_squared_error(y_test, y_pred))
print(f”RMSE: {rmse:.4f}”)

# 使用AI进行学习路径推荐
$ python
>>> import pandas as pd
>>> import numpy as np
>>> from sklearn.cluster import KMeans
>>> from sklearn.preprocessing import StandardScaler

# 加载学生数据
data = pd.read_csv(‘student_data.csv’)

# 数据预处理
X = data[[‘math_score’, ‘reading_score’, ‘writing_score’, ‘attendance_rate’]]
scaler = StandardScaler()
X_scaled = scaler.fit_transform(X)

# 聚类分析
kmeans = KMeans(n_clusters=4, random_state=42)
data[‘cluster’] = kmeans.fit_predict(X_scaled)

# 分析每个聚类的特征
cluster_analysis = data.groupby(‘cluster’).mean()
print(cluster_analysis)

# 为每个聚类制定学习路径
def get_learning_path(cluster_id):
paths = {
0: [‘基础数学’, ‘基础阅读’, ‘基础写作’],
1: [‘中级数学’, ‘中级阅读’, ‘中级写作’],
2: [‘高级数学’, ‘高级阅读’, ‘高级写作’],
3: [‘专家数学’, ‘专家阅读’, ‘专家写作’]
}
return paths.get(cluster_id, [‘基础数学’, ‘基础阅读’, ‘基础写作’])

# 示例：为学生推荐学习路径
data[‘learning_path’] = data[‘cluster’].apply(get_learning_path)
print(data[[‘student_id’, ‘cluster’, ‘learning_path’]].head())

# 使用AI进行能源需求预测
$ python
>>> import pandas as pd
>>> import numpy as np
>>> from sklearn.model_selection import train_test_split
>>> from sklearn.ensemble import GradientBoostingRegressor
>>> from sklearn.metrics import mean_squared_error

# 加载能源数据
data = pd.read_csv(‘energy_data.csv’)
data[‘date’] = pd.to_datetime(data[‘date’])
data[‘hour’] = data[‘date’].dt.hour
data[‘day_of_week’] = data[‘date’].dt.dayofweek
data[‘month’] = data[‘date’].dt.month

# 特征和标签
X = data[[‘hour’, ‘day_of_week’, ‘month’, ‘temperature’, ‘humidity’, ‘wind_speed’]]
y = data[‘energy_demand’]

# 分割数据
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 = GradientBoostingRegressor()
model.fit(X_train, y_train)

# 预测
y_pred = model.predict(X_test)

# 评估
rmse = np.sqrt(mean_squared_error(y_test, y_pred))
print(f”RMSE: {rmse:.4f}”)

# 特征重要性
feature_importance = pd.DataFrame({
‘feature’: X.columns,
‘importance’: model.feature_importances_
}).sort_values(‘importance’, ascending=False)
print(feature_importance)

# 使用AI进行城市交通流量优化
$ python
>>> import pandas as pd
>>> import numpy as np
>>> from sklearn.linear_model import LinearRegression
>>> from sklearn.metrics import mean_squared_error

# 加载城市交通数据
data = pd.read_csv(‘city_traffic_data.csv’)

# 数据预处理
data = data.dropna()
X = data[[‘population’, ’employment_rate’, ‘public_transport_usage’, ‘road_length’]]
y = data[‘traffic_congestion_index’]

# 分割数据
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
y_pred = model.predict(X_test)

# 评估
rmse = np.sqrt(mean_squared_error(y_test, y_pred))
print(f”RMSE: {rmse:.4f}”)

# 特征系数
feature_coefficients = pd.DataFrame({
‘feature’: X.columns,
‘coefficient’: model.coef_
}).sort_values(‘coefficient’, ascending=False)
print(feature_coefficients)

# 政策建议
print(“Policy recommendations:”)
print(“1. Increase public transport usage to reduce traffic congestion”)
print(“2. Optimize road infrastructure based on population density”)
print(“3. Implement smart traffic management systems”)