💖💖作者：计算机毕业设计小途 💙💙个人简介：曾长期从事计算机专业培训教学，本人也热爱上课教学，语言擅长Java、微信小程序、Python、Golang、安卓Android等，开发项目包括大数据、深度学习、网站、小程序、安卓、算法。平常会做一些项目定制化开发、代码讲解、答辩教学、文档编写、也懂一些降重方面的技巧。平常喜欢分享一些自己开发中遇到的问题的解决办法，也喜欢交流技术，大家有技术代码这一块的问题可以问我！ 💛💛想说的话：感谢大家的关注与支持！ 💜💜 网站实战项目 安卓/小程序实战项目 大数据实战项目 深度学习实战项目
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基于SpringBoot+Vue莱元元电商数据分析系统介绍
基于SpringBoot+Vue的莱元元电商数据分析系统是一个专门针对电商业务场景设计的综合性数据分析平台，采用当前主流的前后端分离架构进行开发。系统后端基于SpringBoot框架构建，整合了Spring、SpringMVC和Mybatis技术栈，提供稳定可靠的服务端支持，同时支持Python+Django技术栈实现，为不同技术偏好的开发者提供灵活选择。前端采用Vue.js配合ElementUI组件库和HTML技术，打造现代化的用户交互界面，数据存储采用MySQL关系型数据库，确保数据的安全性和一致性。系统核心功能涵盖了电商业务的各个关键环节，包括系统主页展示、用户管理模块用于处理用户信息和权限控制、电商数据管理模块负责商品和交易数据的录入与维护、销量预测功能通过数据分析算法为商家提供未来销售趋势预判、系统管理模块确保平台的正常运行、资讯分类和新闻资讯模块提供行业动态信息、数据分析看板以可视化方式呈现关键业务指标和趋势图表、个人中心和密码修改功能保障用户账户安全。整个系统采用B/S架构模式，用户可通过浏览器直接访问，无需安装客户端软件，开发环境支持IDEA和PyCharm两种主流IDE，为计算机专业学生的毕业设计项目提供了技术全面、功能完整、实用性强的解决方案。
基于SpringBoot+Vue莱元元电商数据分析系统演示视频
基于SpringBoot+Vue莱元元电商数据分析系统演示图片
基于SpringBoot+Vue莱元元电商数据分析系统代码展示
from pyspark.sql import SparkSession
from pyspark.sql.functions import *
from pyspark.ml.feature import VectorAssembler
from pyspark.ml.regression import LinearRegression
from datetime import datetime, timedelta
import pandas as pd
import numpy as np
from django.http import JsonResponse
from django.views.decorators.csrf import csrf_exempt
import json
spark = SparkSession.builder.appName("LaiYuanYuanEcommerceAnalysis").config("spark.sql.adaptive.enabled", "true").getOrCreate()
@csrf_exempt
def sales_prediction_analysis(request):
if request.method == 'POST':
data = json.loads(request.body)
product_id = data.get('product_id')
days_ahead = data.get('days_ahead', 30)
sales_df = spark.sql(f"SELECT DATE(order_date) as date, SUM(quantity) as daily_sales FROM ecommerce_orders WHERE product_id = {product_id} AND order_date >= DATE_SUB(CURRENT_DATE(), 90) GROUP BY DATE(order_date) ORDER BY date")
pandas_df = sales_df.toPandas()
pandas_df['date'] = pd.to_datetime(pandas_df['date'])
pandas_df['day_of_week'] = pandas_df['date'].dt.dayofweek
pandas_df['day_of_month'] = pandas_df['date'].dt.day
pandas_df['month'] = pandas_df['date'].dt.month
pandas_df['days_from_start'] = (pandas_df['date'] - pandas_df['date'].min()).dt.days
spark_df = spark.createDataFrame(pandas_df)
assembler = VectorAssembler(inputCols=['day_of_week', 'day_of_month', 'month', 'days_from_start'], outputCol='features')
feature_df = assembler.transform(spark_df)
lr = LinearRegression(featuresCol='features', labelCol='daily_sales')
model = lr.fit(feature_df)
future_dates = []
base_date = pandas_df['date'].max()
for i in range(1, days_ahead + 1):
future_date = base_date + timedelta(days=i)
future_dates.append({
'day_of_week': future_date.weekday(),
'day_of_month': future_date.day,
'month': future_date.month,
'days_from_start': pandas_df['days_from_start'].max() + i,
'date': future_date.strftime('%Y-%m-%d')
})
future_df = spark.createDataFrame(future_dates)
future_feature_df = assembler.transform(future_df)
predictions = model.transform(future_feature_df)
prediction_results = predictions.select('date', 'prediction').collect()
prediction_list = [{'date': row['date'], 'predicted_sales': max(0, round(row['prediction'], 2))} for row in prediction_results]
total_predicted = sum([item['predicted_sales'] for item in prediction_list])
return JsonResponse({
'status': 'success',
'predictions': prediction_list,
'total_predicted_sales': total_predicted,
'analysis_period': f'{days_ahead}天预测',
'model_rmse': model.summary.rootMeanSquaredError
})
@csrf_exempt
def ecommerce_data_analysis(request):
if request.method == 'GET':
date_range = request.GET.get('date_range', '30')
category_id = request.GET.get('category_id', 'all')
start_date = (datetime.now() - timedelta(days=int(date_range))).strftime('%Y-%m-%d')
base_query = f"SELECT p.product_id, p.product_name, p.category_id, c.category_name, o.quantity, o.price, o.order_date FROM ecommerce_orders o JOIN products p ON o.product_id = p.product_id JOIN categories c ON p.category_id = c.category_id WHERE o.order_date >= '{start_date}'"
if category_id != 'all':
base_query += f" AND p.category_id = {category_id}"
orders_df = spark.sql(base_query)
sales_summary = orders_df.groupBy('product_id', 'product_name', 'category_name').agg(
sum('quantity').alias('total_quantity'),
sum(col('quantity') * col('price')).alias('total_revenue'),
avg('price').alias('avg_price'),
count('*').alias('order_count')
).orderBy(desc('total_revenue'))
daily_trends = orders_df.groupBy('order_date').agg(
sum('quantity').alias('daily_quantity'),
sum(col('quantity') * col('price')).alias('daily_revenue'),
countDistinct('product_id').alias('unique_products')
).orderBy('order_date')
category_performance = orders_df.groupBy('category_id', 'category_name').agg(
sum('quantity').alias('category_quantity'),
sum(col('quantity') * col('price')).alias('category_revenue'),
avg('price').alias('avg_category_price')
).orderBy(desc('category_revenue'))
top_products = sales_summary.limit(10).collect()
daily_data = daily_trends.collect()
category_data = category_performance.collect()
revenue_growth = []
if len(daily_data) > 1:
for i in range(1, len(daily_data)):
prev_revenue = daily_data[i-1]['daily_revenue']
curr_revenue = daily_data[i]['daily_revenue']
growth_rate = ((curr_revenue - prev_revenue) / prev_revenue * 100) if prev_revenue > 0 else 0
revenue_growth.append({
'date': daily_data[i]['order_date'].strftime('%Y-%m-%d'),
'growth_rate': round(growth_rate, 2)
})
return JsonResponse({
'status': 'success',
'top_products': [{'product_id': row['product_id'], 'product_name': row['product_name'], 'total_revenue': float(row['total_revenue']), 'total_quantity': row['total_quantity']} for row in top_products],
'daily_trends': [{'date': row['order_date'].strftime('%Y-%m-%d'), 'revenue': float(row['daily_revenue']), 'quantity': row['daily_quantity']} for row in daily_data],
'category_performance': [{'category_name': row['category_name'], 'revenue': float(row['category_revenue']), 'quantity': row['category_quantity']} for row in category_data],
'revenue_growth': revenue_growth,
'analysis_period': f'最近{date_range}天'
})
@csrf_exempt
def user_behavior_analysis(request):
if request.method == 'POST':
data = json.loads(request.body)
analysis_type = data.get('analysis_type', 'comprehensive')
time_period = data.get('time_period', 30)
start_date = (datetime.now() - timedelta(days=time_period)).strftime('%Y-%m-%d')
user_orders_df = spark.sql(f"SELECT u.user_id, u.username, u.register_date, o.order_id, o.order_date, o.quantity, o.price, p.category_id FROM users u JOIN ecommerce_orders o ON u.user_id = o.user_id JOIN products p ON o.product_id = p.product_id WHERE o.order_date >= '{start_date}'")
user_summary = user_orders_df.groupBy('user_id', 'username', 'register_date').agg(
count('order_id').alias('order_frequency'),
sum(col('quantity') * col('price')).alias('total_spending'),
avg(col('quantity') * col('price')).alias('avg_order_value'),
sum('quantity').alias('total_items'),
countDistinct('category_id').alias('category_diversity')
)
user_summary = user_summary.withColumn('customer_value_score',
(col('total_spending') * 0.4 + col('order_frequency') * 0.3 + col('avg_order_value') * 0.2 + col('category_diversity') * 0.1))
user_segments = user_summary.withColumn('customer_segment',
when(col('customer_value_score') >= 100, '高价值客户')
.when(col('customer_value_score') >= 50, '中等价值客户')
.otherwise('普通客户'))
retention_analysis = spark.sql(f"SELECT user_id, COUNT(DISTINCT DATE_FORMAT(order_date, 'yyyy-MM')) as active_months FROM ecommerce_orders WHERE order_date >= '{start_date}' GROUP BY user_id")
retention_summary = retention_analysis.groupBy('active_months').count().orderBy('active_months')
purchase_patterns = user_orders_df.withColumn('hour', hour('order_date')).withColumn('day_of_week', dayofweek('order_date')).groupBy('hour', 'day_of_week').agg(count('order_id').alias('order_count')).orderBy(desc('order_count'))
top_customers = user_segments.orderBy(desc('customer_value_score')).limit(20).collect()
segment_distribution = user_segments.groupBy('customer_segment').count().collect()
retention_data = retention_summary.collect()
pattern_data = purchase_patterns.limit(10).collect()
churn_risk_users = user_summary.filter(col('order_frequency') <= 2).filter(col('total_spending') <= 100).orderBy(desc('total_spending')).limit(10).collect()
return JsonResponse({
'status': 'success',
'top_customers': [{'user_id': row['user_id'], 'username': row['username'], 'total_spending': float(row['total_spending']), 'order_frequency': row['order_frequency'], 'customer_segment': row['customer_segment']} for row in top_customers],
'segment_distribution': [{'segment': row['customer_segment'], 'count': row['count']} for row in segment_distribution],
'retention_analysis': [{'active_months': row['active_months'], 'user_count': row['count']} for row in retention_data],
'purchase_patterns': [{'hour': row['hour'], 'day_of_week': row['day_of_week'], 'order_count': row['order_count']} for row in pattern_data],
'churn_risk_users': [{'user_id': row['user_id'], 'username': row['username'], 'total_spending': float(row['total_spending'])} for row in churn_risk_users],
'analysis_summary': {
'total_analyzed_users': user_summary.count(),
'high_value_customers': user_segments.filter(col('customer_segment') == '高价值客户').count(),
'average_customer_value': user_summary.agg(avg('customer_value_score')).collect()[0][0]
}
})
基于SpringBoot+Vue莱元元电商数据分析系统文档展示
💖💖作者：计算机毕业设计小途 💙💙个人简介：曾长期从事计算机专业培训教学，本人也热爱上课教学，语言擅长Java、微信小程序、Python、Golang、安卓Android等，开发项目包括大数据、深度学习、网站、小程序、安卓、算法。平常会做一些项目定制化开发、代码讲解、答辩教学、文档编写、也懂一些降重方面的技巧。平常喜欢分享一些自己开发中遇到的问题的解决办法，也喜欢交流技术，大家有技术代码这一块的问题可以问我！ 💛💛想说的话：感谢大家的关注与支持！ 💜💜 网站实战项目 安卓/小程序实战项目 大数据实战项目 深度学习实战项目
