data_platform/
|-- data_ingestion.py
|-- data_storage.py
|-- data_processing.py
|-- data_visualization.py
|-- requirements.txt
|-- config.yaml

pandas
sqlalchemy
matplotlib
PyYAML
requests

pip install -r requirements.txt

database:
  uri: "sqlite:///data_platform.db"
api:
  url: "https://api.example.com/data"

import requests
import pandas as pd
import yaml

# Load configuration
with open("config.yaml", "r") as file:
    config = yaml.safe_load(file)

def fetch_data(api_url):
    response = requests.get(api_url)
    response.raise_for_status()
    return response.json()

def save_to_csv(data, filename):
    df = pd.DataFrame(data)
    df.to_csv(filename, index=False)

if __name__ == "__main__":
    api_url = config["api"]["url"]
    data = fetch_data(api_url)
    save_to_csv(data, "data.csv")
-----------------------------------
?著作權(quán)歸作者所有：來(lái)自51CTO博客作者魚(yú)弦CTO的原創(chuàng)作品，請(qǐng)聯(lián)系作者獲取轉(zhuǎn)載授權(quán)，否則將追究法律責(zé)任
【全網(wǎng)獨(dú)家】從中臺(tái)到數(shù)據(jù)飛輪的進(jìn)化演變
https://blog.51cto.com/chenfenglove/12017369

from sqlalchemy import create_engine
import pandas as pd
import yaml

# Load configuration
with open("config.yaml", "r") as file:
    config = yaml.safe_load(file)

def load_data_to_db(csv_file, db_uri):
    engine = create_engine(db_uri)
    df = pd.read_csv(csv_file)
    df.to_sql("data_table", engine, if_exists="replace", index=False)

if __name__ == "__main__":
    csv_file = "data.csv"
    db_uri = config["database"]["uri"]
    load_data_to_db(csv_file, db_uri)
-----------------------------------
?著作權(quán)歸作者所有：來(lái)自51CTO博客作者魚(yú)弦CTO的原創(chuàng)作品，請(qǐng)聯(lián)系作者獲取轉(zhuǎn)載授權(quán)，否則將追究法律責(zé)任
【全網(wǎng)獨(dú)家】從中臺(tái)到數(shù)據(jù)飛輪的進(jìn)化演變
https://blog.51cto.com/chenfenglove/12017369

from sqlalchemy import create_engine
import pandas as pd
import yaml

# Load configuration
with open("config.yaml", "r") as file:
    config = yaml.safe_load(file)

def process_data(db_uri):
    engine = create_engine(db_uri)
    query = "SELECT * FROM data_table"
    df = pd.read_sql(query, engine)
    
    # Example processing: Filter data where value > 50
    processed_df = df[df['value'] > 50]
    return processed_df

if __name__ == "__main__":
    db_uri = config["database"]["uri"]
    processed_df = process_data(db_uri)
    print(processed_df.head())
-----------------------------------
?著作權(quán)歸作者所有：來(lái)自51CTO博客作者魚(yú)弦CTO的原創(chuàng)作品，請(qǐng)聯(lián)系作者獲取轉(zhuǎn)載授權(quán)，否則將追究法律責(zé)任
【全網(wǎng)獨(dú)家】從中臺(tái)到數(shù)據(jù)飛輪的進(jìn)化演變
https://blog.51cto.com/chenfenglove/12017369

import matplotlib.pyplot as plt
from sqlalchemy import create_engine
import pandas as pd
import yaml

# Load configuration
with open("config.yaml", "r") as file:
    config = yaml.safe_load(file)

def visualize_data(db_uri):
    engine = create_engine(db_uri)
    query = "SELECT * FROM data_table"
    df = pd.read_sql(query, engine)
    
    # Example visualization: Histogram of 'value' column
    plt.hist(df['value'], bins=10)
    plt.xlabel('Value')
    plt.ylabel('Frequency')
    plt.title('Histogram of Values')
    plt.show()

if __name__ == "__main__":
    db_uri = config["database"]["uri"]
    visualize_data(db_uri)
-----------------------------------
?著作權(quán)歸作者所有：來(lái)自51CTO博客作者魚(yú)弦CTO的原創(chuàng)作品，請(qǐng)聯(lián)系作者獲取轉(zhuǎn)載授權(quán)，否則將追究法律責(zé)任
【全網(wǎng)獨(dú)家】從中臺(tái)到數(shù)據(jù)飛輪的進(jìn)化演變
https://blog.51cto.com/chenfenglove/12017369

import pandas as pd
from sklearn.model_selection import train_test_split
from sklearn.ensemble import RandomForestClassifier
from sklearn.metrics import accuracy_score

# 假設(shè)我們有用戶行為數(shù)據(jù)和產(chǎn)品數(shù)據(jù)
user_behavior_data = pd.read_csv('user_behavior.csv')
product_data = pd.read_csv('products.csv')

# 合并數(shù)據(jù)集
data = pd.merge(user_behavior_data, product_data, on='product_id')

# 特征選擇
features = ['user_id', 'product_id', 'category', 'price', 'user_age', 'user_gender']
X = data[features]
y = data['purchase']

# 數(shù)據(jù)分割
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)

# 模型訓(xùn)練
model = RandomForestClassifier(n_estimators=100, random_state=42)
model.fit(X_train, y_train)

# 預(yù)測(cè)與評(píng)估
y_pred = model.predict(X_test)
accuracy = accuracy_score(y_test, y_pred)
print(f'推薦系統(tǒng)的準(zhǔn)確率: {accuracy:.2f}')

# 數(shù)據(jù)飛輪：將新的用戶行為數(shù)據(jù)不斷加入并重新訓(xùn)練模型
# 在實(shí)際應(yīng)用中，可以使用在線學(xué)習(xí)或定期批處理方式更新模型
-----------------------------------
?著作權(quán)歸作者所有：來(lái)自51CTO博客作者魚(yú)弦CTO的原創(chuàng)作品，請(qǐng)聯(lián)系作者獲取轉(zhuǎn)載授權(quán)，否則將追究法律責(zé)任
【全網(wǎng)獨(dú)家】從中臺(tái)到數(shù)據(jù)飛輪的進(jìn)化演變
https://blog.51cto.com/chenfenglove/12017369

import pandas as pd
from sklearn.model_selection import train_test_split
from sklearn.ensemble import IsolationForest
from sklearn.metrics import classification_report

# 加載交易數(shù)據(jù)
transaction_data = pd.read_csv('transactions.csv')
features = ['amount', 'transaction_type', 'account_age', 'location']

X = transaction_data[features]

# 訓(xùn)練Isolation Forest模型進(jìn)行異常檢測(cè)
model = IsolationForest(contamination=0.01, random_state=42)
model.fit(X)

# 預(yù)測(cè)（-1表示異常，1表示正常）
transaction_data['fraud_prediction'] = model.predict(X)

print(classification_report(transaction_data['label'], transaction_data['fraud_prediction']))

# 數(shù)據(jù)飛輪：實(shí)時(shí)監(jiān)控新交易數(shù)據(jù)，并將其反饋到模型中進(jìn)行再訓(xùn)練
-----------------------------------
?著作權(quán)歸作者所有：來(lái)自51CTO博客作者魚(yú)弦CTO的原創(chuàng)作品，請(qǐng)聯(lián)系作者獲取轉(zhuǎn)載授權(quán)，否則將追究法律責(zé)任
【全網(wǎng)獨(dú)家】從中臺(tái)到數(shù)據(jù)飛輪的進(jìn)化演變
https://blog.51cto.com/chenfenglove/12017369

import pandas as pd
from sklearn.model_selection import train_test_split
from sklearn.linear_model import LinearRegression

# 加載生產(chǎn)和供應(yīng)鏈數(shù)據(jù)
supply_chain_data = pd.read_csv('supply_chain.csv')

features = ['material_cost', 'labor_cost', 'demand_forecast', 'lead_time']
X = supply_chain_data[features]
y = supply_chain_data['production_output']

# 數(shù)據(jù)分割
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)

# 模型訓(xùn)練
model = LinearRegression()
model.fit(X_train, y_train)

# 預(yù)測(cè)與評(píng)估
y_pred = model.predict(X_test)
print(f'生產(chǎn)效率預(yù)測(cè)誤差: {mean_squared_error(y_test, y_pred):.2f}')

# 數(shù)據(jù)飛輪：定期更新預(yù)測(cè)模型以反映最新的供應(yīng)鏈狀況
-----------------------------------
?著作權(quán)歸作者所有：來(lái)自51CTO博客作者魚(yú)弦CTO的原創(chuàng)作品，請(qǐng)聯(lián)系作者獲取轉(zhuǎn)載授權(quán)，否則將追究法律責(zé)任
【全網(wǎng)獨(dú)家】從中臺(tái)到數(shù)據(jù)飛輪的進(jìn)化演變
https://blog.51cto.com/chenfenglove/12017369

import pandas as pd
from sklearn.cluster import KMeans
import matplotlib.pyplot as plt

# 加載交通數(shù)據(jù)
traffic_data = pd.read_csv('traffic_data.csv')
features = ['location_latitude', 'location_longitude', 'traffic_volume']

X = traffic_data[features]

# 使用KMeans進(jìn)行聚類分析
kmeans = KMeans(n_clusters=5, random_state=42)
traffic_data['cluster'] = kmeans.fit_predict(X)

# 可視化結(jié)果
plt.scatter(traffic_data['location_longitude'], traffic_data['location_latitude'], c=traffic_data['cluster'])
plt.xlabel('Longitude')
plt.ylabel('Latitude')
plt.title('Traffic Clusters')
plt.show()

# 數(shù)據(jù)飛輪：不斷收集新的交通數(shù)據(jù)并更新聚類模型，以優(yōu)化交通管理和資源分配
-----------------------------------
?著作權(quán)歸作者所有：來(lái)自51CTO博客作者魚(yú)弦CTO的原創(chuàng)作品，請(qǐng)聯(lián)系作者獲取轉(zhuǎn)載授權(quán)，否則將追究法律責(zé)任
【全網(wǎng)獨(dú)家】從中臺(tái)到數(shù)據(jù)飛輪的進(jìn)化演變
https://blog.51cto.com/chenfenglove/12017369

import pandas as pd
from sklearn.model_selection import train_test_split
from sklearn.ensemble import RandomForestClassifier
from sklearn.metrics import accuracy_score

# 數(shù)據(jù)采集
data = pd.read_csv('data.csv')

# 數(shù)據(jù)清洗
data.dropna(inplace=True)

# 特征工程
X = data.drop('target', axis=1)
y = data['target']

# 數(shù)據(jù)劃分
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)

# 模型訓(xùn)練
model = RandomForestClassifier()
model.fit(X_train, y_train)

# 結(jié)果反饋
y_pred = model.predict(X_test)
print("Accuracy:", accuracy_score(y_test, y_pred))
-----------------------------------
?著作權(quán)歸作者所有：來(lái)自51CTO博客作者魚(yú)弦CTO的原創(chuàng)作品，請(qǐng)聯(lián)系作者獲取轉(zhuǎn)載授權(quán)，否則將追究法律責(zé)任
【全網(wǎng)獨(dú)家】從中臺(tái)到數(shù)據(jù)飛輪的進(jìn)化演變
https://blog.51cto.com/chenfenglove/12017369

def test_model_accuracy():
    assert accuracy_score(y_test, y_pred) > 0.8, "Model accuracy is below acceptable threshold"

test_model_accuracy()