💖💖作者：计算机毕业设计杰瑞 💙💙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 深度学校实战项目 计算机毕业设计选题推荐
淘宝用户购物可视化与行为预测系统介绍
《淘宝用户购物可视化与行为预测系统》是一个基于大数据技术的综合性电商数据分析平台，采用Hadoop+Spark分布式计算框架作为核心技术架构，结合Python数据处理能力和Django Web框架构建而成。系统通过HDFS分布式文件系统存储海量用户购物数据，利用Spark SQL进行高效数据查询和分析，运用Pandas、NumPy等科学计算库实现复杂的数据处理算法。前端采用Vue+ElementUI+Echarts技术栈构建直观的可视化界面，用户可以通过系统首页、用户管理、购物日志管理、运动服管理、商品信息管理、商品类型管理、购物预测管理、充值记录管理、订单管理等功能模块，实现对淘宝用户购物行为的全方位监控和分析。系统不仅能够处理大规模用户购物数据，还能通过机器学习算法预测用户未来的购物行为模式，为电商平台的精准营销和库存管理提供数据支撑，展现了大数据技术在电商领域的实际应用价值。
淘宝用户购物可视化与行为预测系统演示视频
淘宝用户购物可视化与行为预测系统演示图片
淘宝用户购物可视化与行为预测系统代码展示
from pyspark.sql import SparkSession
from pyspark.sql.functions import col, count, sum, avg, max, min, when, desc, asc
from pyspark.ml.feature import VectorAssembler
from pyspark.ml.regression import LinearRegression
from pyspark.ml.evaluation import RegressionEvaluator
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("TaobaoUserShoppingAnalysis").config("spark.sql.adaptive.enabled", "true").config("spark.sql.adaptive.coalescePartitions.enabled", "true").getOrCreate()
def shopping_behavior_prediction(request):
user_shopping_df = spark.read.format("jdbc").option("url", "jdbc:mysql://localhost:3306/taobao_db").option("driver", "com.mysql.cj.jdbc.Driver").option("dbtable", "shopping_logs").option("user", "root").option("password", "password").load()
user_features_df = user_shopping_df.groupBy("user_id").agg(count("order_id").alias("total_orders"), sum("order_amount").alias("total_amount"), avg("order_amount").alias("avg_amount"), max("order_date").alias("last_order_date"), count(when(col("product_category") == "运动服", 1)).alias("sportswear_orders"))
feature_columns = ["total_orders", "total_amount", "avg_amount", "sportswear_orders"]
assembler = VectorAssembler(inputCols=feature_columns, outputCol="features")
feature_data = assembler.transform(user_features_df)
train_data, test_data = feature_data.randomSplit([0.8, 0.2], seed=42)
lr = LinearRegression(featuresCol="features", labelCol="total_amount", predictionCol="predicted_amount")
lr_model = lr.fit(train_data)
predictions = lr_model.transform(test_data)
evaluator = RegressionEvaluator(labelCol="total_amount", predictionCol="predicted_amount", metricName="rmse")
rmse = evaluator.evaluate(predictions)
prediction_results = predictions.select("user_id", "total_amount", "predicted_amount").collect()
result_list = [{"user_id": row.user_id, "actual_amount": float(row.total_amount), "predicted_amount": float(row.predicted_amount)} for row in prediction_results]
model_metrics = {"rmse": float(rmse), "training_samples": train_data.count(), "test_samples": test_data.count()}
response_data = {"status": "success", "predictions": result_list[:100], "model_metrics": model_metrics, "message": "购物行为预测分析完成"}
return JsonResponse(response_data, safe=False)
def shopping_data_visualization(request):
shopping_df = spark.read.format("jdbc").option("url", "jdbc:mysql://localhost:3306/taobao_db").option("driver", "com.mysql.cj.jdbc.Driver").option("dbtable", "shopping_logs").option("user", "root").option("password", "password").load()
daily_sales = shopping_df.groupBy("order_date").agg(sum("order_amount").alias("daily_amount"), count("order_id").alias("daily_orders")).orderBy("order_date")
category_analysis = shopping_df.groupBy("product_category").agg(sum("order_amount").alias("category_amount"), count("order_id").alias("category_orders"), avg("order_amount").alias("avg_category_amount")).orderBy(desc("category_amount"))
user_ranking = shopping_df.groupBy("user_id").agg(sum("order_amount").alias("user_total_amount"), count("order_id").alias("user_total_orders")).orderBy(desc("user_total_amount"))
top_products = shopping_df.groupBy("product_id", "product_name").agg(sum("quantity").alias("total_quantity"), sum("order_amount").alias("product_revenue")).orderBy(desc("total_quantity"))
monthly_trend = shopping_df.withColumn("order_month", col("order_date").substr(1, 7)).groupBy("order_month").agg(sum("order_amount").alias("monthly_amount"), count("order_id").alias("monthly_orders")).orderBy("order_month")
daily_data = [{"date": row.order_date, "amount": float(row.daily_amount), "orders": row.daily_orders} for row in daily_sales.collect()]
category_data = [{"category": row.product_category, "amount": float(row.category_amount), "orders": row.category_orders, "avg_amount": float(row.avg_category_amount)} for row in category_analysis.collect()]
user_data = [{"user_id": row.user_id, "total_amount": float(row.user_total_amount), "total_orders": row.user_total_orders} for row in user_ranking.limit(50).collect()]
product_data = [{"product_id": row.product_id, "product_name": row.product_name, "quantity": row.total_quantity, "revenue": float(row.product_revenue)} for row in top_products.limit(30).collect()]
monthly_data = [{"month": row.order_month, "amount": float(row.monthly_amount), "orders": row.monthly_orders} for row in monthly_trend.collect()]
visualization_result = {"daily_sales": daily_data, "category_analysis": category_data, "user_ranking": user_data, "top_products": product_data, "monthly_trend": monthly_data}
return JsonResponse({"status": "success", "data": visualization_result, "message": "购物数据可视化分析完成"}, safe=False)
def user_shopping_analysis(request):
if request.method == 'POST':
request_data = json.loads(request.body)
user_id = request_data.get('user_id')
analysis_type = request_data.get('analysis_type', 'comprehensive')
shopping_logs_df = spark.read.format("jdbc").option("url", "jdbc:mysql://localhost:3306/taobao_db").option("driver", "com.mysql.cj.jdbc.Driver").option("dbtable", "shopping_logs").option("user", "root").option("password", "password").load()
if user_id:
user_data = shopping_logs_df.filter(col("user_id") == user_id)
else:
user_data = shopping_logs_df
user_summary = user_data.groupBy("user_id").agg(count("order_id").alias("total_orders"), sum("order_amount").alias("total_spending"), avg("order_amount").alias("avg_order_value"), max("order_date").alias("last_purchase_date"), min("order_date").alias("first_purchase_date"))
category_preference = user_data.groupBy("user_id", "product_category").agg(count("order_id").alias("category_orders"), sum("order_amount").alias("category_spending")).orderBy("user_id", desc("category_spending"))
purchase_frequency = user_data.withColumn("order_month", col("order_date").substr(1, 7)).groupBy("user_id", "order_month").agg(count("order_id").alias("monthly_orders"), sum("order_amount").alias("monthly_spending")).orderBy("user_id", "order_month")
product_affinity = user_data.groupBy("user_id", "product_id", "product_name").agg(sum("quantity").alias("total_quantity"), sum("order_amount").alias("product_spending")).orderBy("user_id", desc("product_spending"))
time_pattern = user_data.groupBy("user_id").agg(count(when(col("order_date").like("%-01-%") | col("order_date").like("%-02-%") | col("order_date").like("%-12-%"), 1)).alias("winter_orders"), count(when(col("order_date").like("%-03-%") | col("order_date").like("%-04-%") | col("order_date").like("%-05-%"), 1)).alias("spring_orders"), count(when(col("order_date").like("%-06-%") | col("order_date").like("%-07-%") | col("order_date").like("%-08-%"), 1)).alias("summer_orders"), count(when(col("order_date").like("%-09-%") | col("order_date").like("%-10-%") | col("order_date").like("%-11-%"), 1)).alias("autumn_orders"))
summary_data = [{"user_id": row.user_id, "total_orders": row.total_orders, "total_spending": float(row.total_spending), "avg_order_value": float(row.avg_order_value), "last_purchase": row.last_purchase_date, "first_purchase": row.first_purchase_date} for row in user_summary.collect()]
preference_data = [{"user_id": row.user_id, "category": row.product_category, "orders": row.category_orders, "spending": float(row.category_spending)} for row in category_preference.collect()]
frequency_data = [{"user_id": row.user_id, "month": row.order_month, "orders": row.monthly_orders, "spending": float(row.monthly_spending)} for row in purchase_frequency.collect()]
affinity_data = [{"user_id": row.user_id, "product_id": row.product_id, "product_name": row.product_name, "quantity": row.total_quantity, "spending": float(row.product_spending)} for row in product_affinity.collect()]
pattern_data = [{"user_id": row.user_id, "winter": row.winter_orders, "spring": row.spring_orders, "summer": row.summer_orders, "autumn": row.autumn_orders} for row in time_pattern.collect()]
analysis_result = {"user_summary": summary_data, "category_preference": preference_data, "purchase_frequency": frequency_data, "product_affinity": affinity_data, "seasonal_pattern": pattern_data}
return JsonResponse({"status": "success", "analysis_result": analysis_result, "message": "用户购物行为分析完成"}, safe=False)
淘宝用户购物可视化与行为预测系统文档展示
💖💖作者：计算机毕业设计杰瑞 💙💙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 深度学校实战项目 计算机毕业设计选题推荐
