计算机编程指导师
⭐⭐个人介绍：自己非常喜欢研究技术问题！专业做Java、Python、小程序、安卓、大数据、爬虫、Golang、大屏、爬虫、深度学习、机器学习、预测等实战项目。
⛽⛽实战项目：有源码或者技术上的问题欢迎在评论区一起讨论交流！
⚡⚡如果遇到具体的技术问题或计算机毕设方面需求，你也可以在主页上↑↑联系我~~
⚡⚡获取源码主页--> space.bilibili.com/35463818075…
商店购物趋势分析与可视化系统- 简介
基于Hadoop+Django的商店购物趋势分析与可视化系统是一套融合大数据处理与Web应用开发的综合性数据分析平台。该系统采用Hadoop分布式文件系统HDFS作为数据存储基础架构，利用Spark大数据计算引擎进行高效的数据处理和统计分析，通过Django框架构建后端业务逻辑层，前端采用Vue+ElementUI+Echarts技术栈实现用户界面和数据可视化展示。系统核心业务围绕商店购物数据的多维度分析展开，包含用户画像分析、销售业绩统计、消费行为偏好挖掘和客户价值评估四大功能模块。通过Spark SQL结合Pandas、NumPy等数据科学库对购物交易数据进行深度挖掘，能够生成顾客性别消费分布、年龄结构分析、地理位置统计、商品品类销售对比、季节性销售趋势、促销活动效果评估等多类型分析报告。系统支持将计算结果以柱状图、饼图、折线图、散点图等多种可视化图表形式呈现，帮助商家理解顾客购买行为规律，识别销售热点和市场机会，为商业决策提供数据支撑，实现从原始购物数据到商业洞察的完整数据处理流程。
商店购物趋势分析与可视化系统-技术
开发语言：Python或Java（两个版本都支持）
大数据框架：Hadoop+Spark（本次没用Hive，支持定制）
后端框架：Django+Spring Boot(Spring+SpringMVC+Mybatis)（两个版本都支持）
前端：Vue+ElementUI+Echarts+HTML+CSS+JavaScript+jQuery
详细技术点：Hadoop、HDFS、Spark、Spark SQL、Pandas、NumPy
数据库：MySQL
商店购物趋势分析与可视化系统- 背景
当前零售行业正经历着数字化转型的深刻变革，商家积累的购物交易数据呈现出规模庞大、结构复杂的特点，传统的数据分析方法已难以有效处理这些海量信息。许多商店虽然拥有丰富的顾客购物记录，但缺乏有效的技术手段来挖掘数据背后的商业价值，无法深入了解顾客的购买偏好、消费习惯和行为模式。现有的商业分析工具往往成本高昂且操作复杂，中小型商店难以承担相应的技术投入，而简单的Excel统计又无法满足多维度数据关联分析的需求。与此同时，大数据技术如Hadoop和Spark的逐步成熟为解决这一问题提供了新的可能性，但如何将这些技术与具体的商业场景相结合，开发出既具备强大数据处理能力又便于实际应用的分析系统，成为值得探索的技术课题。
这个课题在技术实践和商业应用方面都具有一定的探索价值。从技术角度来看，该系统将Hadoop分布式存储、Spark大数据计算与Django Web开发相结合，为大数据技术在零售业的应用提供了一个具体的实现案例，虽然规模有限，但仍能展示不同技术栈整合的可行性。对于商业应用而言，系统能够帮助商家更好地理解顾客行为特征，通过数据驱动的方式优化商品配置和营销策略，在一定程度上提升经营效率。作为毕业设计项目，这个课题将理论学习与实际应用相结合，有助于加深对大数据技术体系的理解和掌握。尽管受限于毕设的时间和资源约束，系统的功能相对简单，但其设计思路和技术架构对于后续相关领域的学习和研究仍有参考意义。通过完成这样一个综合性项目，能够提升数据分析、系统设计和编程实现等多方面的实践能力，为将来从事相关技术工作打下基础。
商店购物趋势分析与可视化系统-视频展示
商店购物趋势分析与可视化系统-图片展示
商店购物趋势分析与可视化系统-代码展示
from pyspark.sql.functions import col, sum, avg, count, desc, when, collect_list
from pyspark.ml.clustering import KMeans
from pyspark.ml.feature import VectorAssembler
import pandas as pd
import numpy as np
from django.http import JsonResponse
from django.views.decorators.csrf import csrf_exempt
spark = SparkSession.builder.appName("ShoppingTrendAnalysis").config("spark.sql.adaptive.enabled", "true").getOrCreate()
@csrf_exempt
def customer_profile_analysis(request):
df = spark.read.option("header", "true").option("inferSchema", "true").csv("hdfs://localhost:9000/shopping_data/purchases.csv")
gender_analysis = df.groupBy("Gender").agg(count("Customer_ID").alias("customer_count"), sum("Purchase_Amount").alias("total_amount")).collect()
age_groups = df.withColumn("age_group", when(col("Age") < 25, "18-24").when(col("Age") < 35, "25-34").when(col("Age") < 45, "35-44").otherwise("45+"))
age_analysis = age_groups.groupBy("age_group").agg(count("Customer_ID").alias("customer_count"), sum("Purchase_Amount").alias("total_amount")).orderBy("age_group").collect()
location_analysis = df.groupBy("Location").agg(count("Customer_ID").alias("customer_count"), sum("Purchase_Amount").alias("total_amount")).orderBy(desc("total_amount")).limit(10).collect()
subscription_analysis = df.groupBy("Subscription_Status").agg(count("Customer_ID").alias("customer_count"), sum("Purchase_Amount").alias("total_amount"), avg("Purchase_Amount").alias("avg_amount")).collect()
location_age_analysis = df.groupBy("Location").agg(avg("Age").alias("avg_age")).orderBy("Location").collect()
gender_category_cross = df.groupBy("Gender", "Category").agg(count("Customer_ID").alias("purchase_count")).collect()
monthly_customer_trend = df.withColumn("month", col("Purchase_Date").substr(6, 2)).groupBy("month").agg(count("Customer_ID").alias("customer_count")).orderBy("month").collect()
high_value_customers = df.filter(col("Purchase_Amount") > 100).groupBy("Customer_ID").agg(sum("Purchase_Amount").alias("total_spent"), count("Customer_ID").alias("transaction_count")).orderBy(desc("total_spent")).limit(20).collect()
regional_preferences = df.groupBy("Location", "Category").agg(count("Customer_ID").alias("preference_count")).collect()
customer_loyalty_score = df.groupBy("Customer_ID").agg(count("Customer_ID").alias("frequency"), avg("Purchase_Amount").alias("avg_spend")).collect()
result_data = {"gender_data": [{"gender": row["Gender"], "count": row["customer_count"], "amount": float(row["total_amount"])} for row in gender_analysis], "age_data": [{"age_group": row["age_group"], "count": row["customer_count"], "amount": float(row["total_amount"])} for row in age_analysis], "location_data": [{"location": row["Location"], "count": row["customer_count"], "amount": float(row["total_amount"])} for row in location_analysis], "subscription_data": [{"status": row["Subscription_Status"], "count": row["customer_count"], "total": float(row["total_amount"]), "avg": float(row["avg_amount"])} for row in subscription_analysis]}
return JsonResponse(result_data, safe=False)
@csrf_exempt
def sales_performance_analysis(request):
df = spark.read.option("header", "true").option("inferSchema", "true").csv("hdfs://localhost:9000/shopping_data/purchases.csv")
category_sales = df.groupBy("Category").agg(sum("Purchase_Amount").alias("total_sales"), count("Customer_ID").alias("total_orders"), avg("Purchase_Amount").alias("avg_order_value")).orderBy(desc("total_sales")).collect()
top_items = df.groupBy("Item_Purchased").agg(sum("Purchase_Amount").alias("total_sales"), count("Customer_ID").alias("purchase_count")).orderBy(desc("total_sales")).limit(10).collect()
seasonal_sales = df.groupBy("Season").agg(sum("Purchase_Amount").alias("total_sales"), count("Customer_ID").alias("total_orders"), avg("Purchase_Amount").alias("avg_seasonal_spend")).orderBy("Season").collect()
location_contribution = df.groupBy("Location").agg(sum("Purchase_Amount").alias("location_sales")).collect()
total_sales = df.agg(sum("Purchase_Amount").alias("total")).collect()[0]["total"]
location_percentage = [(row["Location"], float(row["location_sales"]), float(row["location_sales"]/total_sales*100)) for row in location_contribution]
location_percentage.sort(key=lambda x: x[2], reverse=True)
monthly_trend = df.withColumn("month", col("Purchase_Date").substr(6, 2)).groupBy("month").agg(sum("Purchase_Amount").alias("monthly_sales"), count("Customer_ID").alias("monthly_orders")).orderBy("month").collect()
category_growth = df.withColumn("quarter", when(col("Purchase_Date").substr(6, 2).isin(["01", "02", "03"]), "Q1").when(col("Purchase_Date").substr(6, 2).isin(["04", "05", "06"]), "Q2").when(col("Purchase_Date").substr(6, 2).isin(["07", "08", "09"]), "Q3").otherwise("Q4")).groupBy("Category", "quarter").agg(sum("Purchase_Amount").alias("quarterly_sales")).collect()
discount_sales_impact = df.groupBy("Discount_Applied").agg(sum("Purchase_Amount").alias("discount_total_sales"), count("Customer_ID").alias("discount_transactions"), avg("Purchase_Amount").alias("discount_avg_sale")).collect()
peak_sales_hours = df.withColumn("hour", col("Purchase_Time").substr(1, 2)).groupBy("hour").agg(sum("Purchase_Amount").alias("hourly_sales"), count("Customer_ID").alias("hourly_transactions")).orderBy(desc("hourly_sales")).collect()
product_performance_matrix = df.groupBy("Item_Purchased").agg(avg("Review_Rating").alias("avg_rating"), sum("Purchase_Amount").alias("total_revenue"), count("Customer_ID").alias("sales_volume")).orderBy(desc("total_revenue")).collect()
sales_data = {"category_sales": [{"category": row["Category"], "total_sales": float(row["total_sales"]), "total_orders": row["total_orders"], "avg_value": float(row["avg_order_value"])} for row in category_sales], "top_items": [{"item": row["Item_Purchased"], "total_sales": float(row["total_sales"]), "count": row["purchase_count"]} for row in top_items], "seasonal_sales": [{"season": row["Season"], "total_sales": float(row["total_sales"]), "orders": row["total_orders"]} for row in seasonal_sales], "location_contribution": [{"location": location, "sales": sales, "percentage": percentage} for location, sales, percentage in location_percentage[:10]]}
return JsonResponse(sales_data, safe=False)
@csrf_exempt
def customer_clustering_analysis(request):
df = spark.read.option("header", "true").option("inferSchema", "true").csv("hdfs://localhost:9000/shopping_data/purchases.csv")
customer_features = df.groupBy("Customer_ID").agg(avg("Age").alias("avg_age"), sum("Purchase_Amount").alias("total_amount"), count("Customer_ID").alias("purchase_frequency"), avg("Review_Rating").alias("avg_rating")).collect()
feature_df = spark.createDataFrame(customer_features)
assembler = VectorAssembler(inputCols=["avg_age", "total_amount", "purchase_frequency"], outputCol="features")
feature_vector = assembler.transform(feature_df)
kmeans = KMeans(k=4, seed=42, featuresCol="features", predictionCol="cluster")
model = kmeans.fit(feature_vector)
clustered_data = model.transform(feature_vector)
cluster_summary = clustered_data.groupBy("cluster").agg(count("Customer_ID").alias("customer_count"), avg("avg_age").alias("cluster_avg_age"), avg("total_amount").alias("cluster_avg_amount"), avg("purchase_frequency").alias("cluster_avg_frequency")).collect()
behavior_analysis = df.groupBy("Gender", "Category").agg(count("Customer_ID").alias("purchase_count"), avg("Purchase_Amount").alias("avg_spend")).collect()
age_preference = df.withColumn("age_group", when(col("Age") < 25, "18-24").when(col("Age") < 35, "25-34").when(col("Age") < 45, "35-44").otherwise("45+")).groupBy("age_group", "Category").agg(count("Customer_ID").alias("preference_count")).collect()
discount_impact = df.groupBy("Discount_Applied").agg(avg("Purchase_Amount").alias("avg_purchase"), count("Customer_ID").alias("transaction_count"), sum("Purchase_Amount").alias("total_sales")).collect()
seasonal_color_trend = df.groupBy("Season", "Color").agg(count("Customer_ID").alias("color_count")).collect()
seasonal_colors = {}
for row in seasonal_color_trend:
season = row["Season"]
if season not in seasonal_colors:
seasonal_colors[season] = []
seasonal_colors[season].append({"color": row["Color"], "count": row["color_count"]})
for season in seasonal_colors:
seasonal_colors[season] = sorted(seasonal_colors[season], key=lambda x: x["count"], reverse=True)[:5]
size_category_analysis = df.groupBy("Category", "Size").agg(count("Customer_ID").alias("size_count"), avg("Purchase_Amount").alias("size_avg_spend")).collect()
customer_satisfaction_distribution = df.groupBy("Review_Rating").agg(count("Customer_ID").alias("rating_count")).orderBy("Review_Rating").collect()
repeat_customer_analysis = df.groupBy("Customer_ID").agg(count("Customer_ID").alias("visit_frequency")).groupBy("visit_frequency").agg(count("Customer_ID").alias("customer_count")).orderBy("visit_frequency").collect()
clustering_result = {"cluster_summary": [{"cluster_id": row["cluster"], "customer_count": row["customer_count"], "avg_age": float(row["cluster_avg_age"]), "avg_amount": float(row["cluster_avg_amount"]), "avg_frequency": float(row["cluster_avg_frequency"])} for row in cluster_summary], "behavior_analysis": [{"gender": row["Gender"], "category": row["Category"], "count": row["purchase_count"], "avg_spend": float(row["avg_spend"])} for row in behavior_analysis], "discount_impact": [{"discount_applied": row["Discount_Applied"], "avg_purchase": float(row["avg_purchase"]), "transaction_count": row["transaction_count"], "total_sales": float(row["total_sales"])} for row in discount_impact], "seasonal_colors": seasonal_colors}
return JsonResponse(clustering_result, safe=False)
商店购物趋势分析与可视化系统-结语
计算机毕设不知道选什么技术栈？Hadoop+Django商店购物趋势分析系统一站式解决
计算机毕设答辩没亮点？商店购物趋势分析与可视化系统让导师眼前一亮
如果遇到具体的技术问题或计算机毕设方面需求，你也可以问我，我会尽力帮你分析和解决问题所在，支持我记得一键三连，再点个关注，学习不迷路！
⚡⚡获取源码主页--> **space.bilibili.com/35463818075…
⚡⚡如果遇到具体的技术问题或计算机毕设方面需求，你也可以在主页上↑↑联系我~~
