基于大数据的护肤品店铺运营数据分析系统 | Hadoop+Spark双引擎驱动：护肤品店铺运营数据分析系统7天快速上手指南

💖💖作者：计算机毕业设计江挽 💙💙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

基于大数据的护肤品店铺运营数据分析系统介绍

护肤品店铺运营数据分析系统是一个基于大数据技术架构的综合性数据分析平台，采用Hadoop+Spark双引擎驱动模式，实现海量护肤品电商数据的高效存储与实时分析处理。系统以Python作为核心开发语言，结合Django框架构建稳定的后端服务架构，前端采用Vue+ElementUI+Echarts技术栈打造直观的数据可视化界面。系统核心功能覆盖护肤店铺基础数据管理、营销渠道效能深度分析、用户消费行为模式挖掘、用户增长活跃度趋势监测以及精准用户画像构建等五大业务模块。通过HDFS分布式文件系统存储大规模结构化数据，利用Spark SQL进行复杂查询分析，结合Pandas和NumPy进行数据预处理与统计建模，为护肤品电商经营者提供科学的决策支持。系统能够处理千万级别的交易记录和用户行为数据，通过多维度数据交叉分析，深入洞察用户购买偏好、季节性消费规律、渠道转化效率等关键业务指标，帮助企业优化运营策略。

基于大数据的护肤品店铺运营数据分析系统演示视频

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基于大数据的护肤品店铺运营数据分析系统代码展示

from pyspark.sql import SparkSession
from pyspark.sql.functions import *
from pyspark.sql.types import *
import pandas as pd
import numpy as np
from django.http import JsonResponse
from django.views import View
import json

spark = SparkSession.builder.appName("SkinCareDataAnalysis").config("spark.some.config.option", "some-value").getOrCreate()

def marketing_channel_analysis(self):
    df = spark.read.format("jdbc").option("url", "jdbc:mysql://localhost:3306/skincare_db").option("dbtable", "marketing_data").option("user", "root").option("password", "123456").load()
    channel_stats = df.groupBy("channel_name").agg(sum("cost").alias("total_cost"), sum("conversion_count").alias("total_conversion"), countDistinct("user_id").alias("unique_users"))
    channel_stats = channel_stats.withColumn("conversion_rate", col("total_conversion") / col("unique_users") * 100)
    channel_stats = channel_stats.withColumn("cost_per_conversion", col("total_cost") / col("total_conversion"))
    roi_analysis = df.groupBy("channel_name").agg(sum("revenue").alias("total_revenue"), sum("cost").alias("total_cost"))
    roi_analysis = roi_analysis.withColumn("roi", (col("total_revenue") - col("total_cost")) / col("total_cost") * 100)
    monthly_trend = df.withColumn("month", date_format(col("create_time"), "yyyy-MM")).groupBy("channel_name", "month").agg(sum("cost").alias("monthly_cost"), sum("conversion_count").alias("monthly_conversion"))
    efficiency_score = channel_stats.join(roi_analysis, "channel_name").withColumn("efficiency_score", col("conversion_rate") * 0.4 + col("roi") * 0.6)
    result_pandas = efficiency_score.toPandas()
    channel_performance = result_pandas.groupby('channel_name').agg({'conversion_rate': 'mean', 'cost_per_conversion': 'mean', 'roi': 'mean', 'efficiency_score': 'mean'}).round(2)
    performance_ranking = channel_performance.sort_values('efficiency_score', ascending=False)
    top_channels = performance_ranking.head(5).to_dict('index')
    trend_data = monthly_trend.toPandas().pivot_table(index='month', columns='channel_name', values=['monthly_cost', 'monthly_conversion'], fill_value=0)
    return {"channel_performance": top_channels, "trend_analysis": trend_data.to_dict(), "roi_ranking": roi_analysis.orderBy(col("roi").desc()).limit(10).toPandas().to_dict('records')}

def user_behavior_analysis(self):
    behavior_df = spark.read.format("jdbc").option("url", "jdbc:mysql://localhost:3306/skincare_db").option("dbtable", "user_behavior").option("user", "root").option("password", "123456").load()
    purchase_patterns = behavior_df.filter(col("action_type") == "purchase").groupBy("user_id").agg(count("*").alias("purchase_frequency"), sum("amount").alias("total_spent"), avg("amount").alias("avg_order_value"))
    user_segments = purchase_patterns.withColumn("user_segment", when(col("total_spent") > 1000, "high_value").when(col("total_spent") > 500, "medium_value").otherwise("low_value"))
    browsing_behavior = behavior_df.filter(col("action_type") == "view").groupBy("user_id", "product_category").agg(count("*").alias("view_count"))
    category_preference = browsing_behavior.groupBy("user_id").agg(collect_list(struct("product_category", "view_count")).alias("preferences"))
    seasonal_analysis = behavior_df.filter(col("action_type") == "purchase").withColumn("season", when(month(col("action_time")).isin([12, 1, 2]), "winter").when(month(col("action_time")).isin([3, 4, 5]), "spring").when(month(col("action_time")).isin([6, 7, 8]), "summer").otherwise("autumn"))
    seasonal_spending = seasonal_analysis.groupBy("season", "product_category").agg(sum("amount").alias("seasonal_revenue"), count("*").alias("purchase_count"))
    conversion_funnel = behavior_df.groupBy("user_id").agg(sum(when(col("action_type") == "view", 1).otherwise(0)).alias("views"), sum(when(col("action_type") == "cart", 1).otherwise(0)).alias("add_to_cart"), sum(when(col("action_type") == "purchase", 1).otherwise(0)).alias("purchases"))
    funnel_rates = conversion_funnel.agg(avg(col("add_to_cart") / col("views") * 100).alias("view_to_cart_rate"), avg(col("purchases") / col("add_to_cart") * 100).alias("cart_to_purchase_rate"))
    behavior_pandas = user_segments.toPandas()
    segment_analysis = behavior_pandas.groupby('user_segment').agg({'purchase_frequency': 'mean', 'total_spent': 'mean', 'avg_order_value': 'mean'}).round(2)
    return {"user_segments": segment_analysis.to_dict(), "seasonal_trends": seasonal_spending.toPandas().to_dict('records'), "conversion_rates": funnel_rates.collect()[0].asDict()}

def user_portrait_analysis(self):
    user_df = spark.read.format("jdbc").option("url", "jdbc:mysql://localhost:3306/skincare_db").option("dbtable", "user_info").option("user", "root").option("password", "123456").load()
    order_df = spark.read.format("jdbc").option("url", "jdbc:mysql://localhost:3306/skincare_db").option("dbtable", "order_info").option("user", "root").option("password", "123456").load()
    user_order_stats = order_df.groupBy("user_id").agg(count("*").alias("order_count"), sum("order_amount").alias("total_amount"), avg("order_amount").alias("avg_amount"), max("order_time").alias("last_order_time"))
    user_profile = user_df.join(user_order_stats, "user_id", "left")
    age_groups = user_profile.withColumn("age_group", when(col("age") < 25, "18-24").when(col("age") < 35, "25-34").when(col("age") < 45, "35-44").otherwise("45+"))
    demographic_analysis = age_groups.groupBy("age_group", "gender").agg(count("*").alias("user_count"), avg("total_amount").alias("avg_spending"))
    geographic_distribution = user_profile.groupBy("province", "city").agg(count("*").alias("user_count"), sum("total_amount").alias("total_revenue"))
    spending_behavior = user_profile.withColumn("spending_level", when(col("total_amount") > 2000, "premium").when(col("total_amount") > 800, "regular").otherwise("basic"))
    loyalty_analysis = user_profile.withColumn("days_since_last_order", datediff(current_date(), col("last_order_time"))).withColumn("loyalty_level", when(col("days_since_last_order") < 30, "active").when(col("days_since_last_order") < 90, "at_risk").otherwise("inactive"))
    brand_preference = order_df.join(user_df.select("user_id", "age", "gender"), "user_id").groupBy("brand_name", "gender").agg(count("*").alias("purchase_count"), sum("order_amount").alias("brand_revenue"))
    lifestyle_segments = user_profile.withColumn("lifestyle_segment", when((col("age") < 30) & (col("total_amount") > 1000), "young_premium").when((col("age") >= 30) & (col("order_count") > 10), "mature_frequent").when(col("total_amount") < 300, "price_sensitive").otherwise("mainstream"))
    profile_pandas = user_profile.toPandas()
    correlation_matrix = profile_pandas[['age', 'order_count', 'total_amount', 'avg_amount']].corr().round(3)
    segment_summary = lifestyle_segments.groupBy("lifestyle_segment").agg(count("*").alias("segment_size"), avg("total_amount").alias("avg_spending"), avg("order_count").alias("avg_frequency"))
    return {"demographic_stats": demographic_analysis.toPandas().to_dict('records'), "geographic_data": geographic_distribution.orderBy(col("total_revenue").desc()).limit(20).toPandas().to_dict('records'), "lifestyle_segments": segment_summary.toPandas().to_dict('records'), "correlation_analysis": correlation_matrix.to_dict()}

基于大数据的护肤品店铺运营数据分析系统文档展示

💖💖作者：计算机毕业设计江挽 💙💙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