构建沈阳智慧城市的数据中台实践

import pandas as pd

# 假设这是两个部门的数据集

df_traffic = pd.read_csv("traffic_data.csv")

df_health = pd.read_csv("health_data.csv")

# 数据清洗

df_traffic.dropna(inplace=True)

df_health.dropna(inplace=True)

# 数据合并

merged_df = pd.merge(df_traffic, df_health, on='date', how='inner')

print(merged_df.head())

import tensorflow as tf

from sklearn.model_selection import train_test_split

# 加载数据并预处理

X = merged_df[['hour', 'weekday']].values

y = merged_df['traffic_volume'].values

X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2)

# 构建模型

model = tf.keras.Sequential([

tf.keras.layers.Dense(64, activation='relu', input_shape=(X_train.shape[1],)),

tf.keras.layers.Dense(1)

])

model.compile(optimizer='adam', loss='mse')

model.fit(X_train, y_train, epochs=100, validation_data=(X_test, y_test))