💖💖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
基于大数据的客户购物订单数据分析系统介绍
客户购物订单数据分析系统是一套基于Hadoop+Spark大数据架构的综合性数据分析平台，专门针对电商购物订单数据进行深度挖掘和智能分析。系统采用Python作为主要开发语言，后端框架使用Django构建RESTful API接口，前端采用Vue+ElementUI+Echarts技术栈实现数据可视化展示。系统核心依托Hadoop分布式文件系统（HDFS）进行海量订单数据存储，利用Spark强大的内存计算能力和Spark SQL进行复杂数据查询与分析处理，结合Pandas和NumPy进行数据清洗和统计运算。功能模块涵盖购物订单数据管理、产品关联规则分析、客户价值分层分析、销售市场分布分析、整体运营绩效分析以及产品销售盈利分析等核心业务场景，为企业提供从数据采集、存储、处理到可视化展示的完整解决方案，帮助商家深入了解客户消费行为特征，优化产品推荐策略，提升运营效率和盈利能力。
基于大数据的客户购物订单数据分析系统演示视频
基于大数据的客户购物订单数据分析系统演示图片
基于大数据的客户购物订单数据分析系统代码展示
from pyspark.sql import SparkSession
from pyspark.sql.functions import *
from pyspark.ml.fpm import FPGrowth
from pyspark.ml.feature import Bucketizer
import pandas as pd
import numpy as np
spark = SparkSession.builder.appName("CustomerOrderAnalysis").config("spark.sql.adaptive.enabled", "true").config("spark.sql.adaptive.coalescePartitions.enabled", "true").getOrCreate()
def analyze_product_association_rules(order_data_path):
orders_df = spark.read.option("header", "true").csv(order_data_path)
orders_df = orders_df.select("order_id", "product_id", "product_name", "quantity", "price")
orders_df = orders_df.filter(col("quantity") > 0)
basket_df = orders_df.groupBy("order_id").agg(collect_list("product_id").alias("items"))
basket_df = basket_df.filter(size(col("items")) >= 2)
fpgrowth = FPGrowth(itemsCol="items", minSupport=0.01, minConfidence=0.1)
model = fpgrowth.fit(basket_df)
frequent_itemsets = model.freqItemsets
frequent_itemsets = frequent_itemsets.filter(size(col("items")) >= 2)
association_rules = model.associationRules
association_rules = association_rules.filter(col("confidence") >= 0.3)
association_rules = association_rules.withColumn("lift", col("confidence") /
(frequent_itemsets.filter(array_contains(col("items"), association_rules.select("consequent").collect()[0][0][0])).select("freq").collect()[0][0] / basket_df.count()))
strong_rules = association_rules.filter(col("lift") > 1.2).orderBy(desc("confidence"), desc("lift"))
product_support = frequent_itemsets.select(explode(col("items")).alias("product_id"), col("freq")).groupBy("product_id").agg(sum("freq").alias("total_support"))
result_rules = strong_rules.join(product_support, strong_rules.antecedent[0] == product_support.product_id, "left")
result_rules = result_rules.select("antecedent", "consequent", "confidence", "lift", "total_support")
return result_rules.collect()
def analyze_customer_value_segmentation(customer_order_data):
customer_df = spark.read.option("header", "true").csv(customer_order_data)
customer_df = customer_df.select("customer_id", "order_date", "total_amount", "order_id")
customer_df = customer_df.withColumn("order_date", to_date(col("order_date"), "yyyy-MM-dd"))
max_date = customer_df.select(max("order_date")).collect()[0][0]
customer_metrics = customer_df.groupBy("customer_id").agg(
datediff(lit(max_date), max("order_date")).alias("recency"),
count("order_id").alias("frequency"),
sum("total_amount").alias("monetary")
)
recency_splits = [-float('inf'), 30, 90, 365, float('inf')]
frequency_splits = [-float('inf'), 2, 5, 10, float('inf')]
monetary_splits = [-float('inf'), 500, 1500, 5000, float('inf')]
recency_bucketizer = Bucketizer(splits=recency_splits, inputCol="recency", outputCol="recency_score")
frequency_bucketizer = Bucketizer(splits=frequency_splits, inputCol="frequency", outputCol="frequency_score")
monetary_bucketizer = Bucketizer(splits=monetary_splits, inputCol="monetary", outputCol="monetary_score")
customer_scored = recency_bucketizer.transform(customer_metrics)
customer_scored = frequency_bucketizer.transform(customer_scored)
customer_scored = monetary_bucketizer.transform(customer_scored)
customer_scored = customer_scored.withColumn("rfm_score",
(4 - col("recency_score")) + col("frequency_score") + col("monetary_score"))
customer_segments = customer_scored.withColumn("customer_segment",
when(col("rfm_score") >= 8, "高价值客户")
.when(col("rfm_score") >= 6, "中价值客户")
.when(col("rfm_score") >= 4, "潜力客户")
.otherwise("流失风险客户"))
segment_summary = customer_segments.groupBy("customer_segment").agg(
count("customer_id").alias("customer_count"),
avg("monetary").alias("avg_monetary"),
avg("frequency").alias("avg_frequency"),
avg("recency").alias("avg_recency")
)
return segment_summary.collect()
def analyze_sales_market_distribution(sales_data_path):
sales_df = spark.read.option("header", "true").csv(sales_data_path)
sales_df = sales_df.select("region", "city", "product_category", "sales_amount", "order_count", "order_date")
sales_df = sales_df.withColumn("order_date", to_date(col("order_date"), "yyyy-MM-dd"))
sales_df = sales_df.withColumn("year_month", date_format(col("order_date"), "yyyy-MM"))
regional_analysis = sales_df.groupBy("region").agg(
sum("sales_amount").alias("total_sales"),
sum("order_count").alias("total_orders"),
countDistinct("city").alias("city_count"),
avg("sales_amount").alias("avg_order_value")
)
city_ranking = sales_df.groupBy("region", "city").agg(
sum("sales_amount").alias("city_sales"),
sum("order_count").alias("city_orders")
).withColumn("sales_rank", row_number().over(
Window.partitionBy("region").orderBy(desc("city_sales"))))
top_cities = city_ranking.filter(col("sales_rank") <= 5)
category_regional_performance = sales_df.groupBy("region", "product_category").agg(
sum("sales_amount").alias("category_sales"),
sum("order_count").alias("category_orders")
).withColumn("category_rank", row_number().over(
Window.partitionBy("region").orderBy(desc("category_sales"))))
monthly_trend = sales_df.groupBy("region", "year_month").agg(
sum("sales_amount").alias("monthly_sales"),
sum("order_count").alias("monthly_orders")
).withColumn("growth_rate",
(col("monthly_sales") - lag("monthly_sales").over(
Window.partitionBy("region").orderBy("year_month"))) /
lag("monthly_sales").over(Window.partitionBy("region").orderBy("year_month")) * 100)
market_concentration = sales_df.groupBy("region").agg(
sum("sales_amount").alias("region_total")
).withColumn("market_share", col("region_total") / sum("region_total").over(Window.partitionBy()))
distribution_result = {
"regional_summary": regional_analysis.collect(),
"top_cities": top_cities.collect(),
"category_performance": category_regional_performance.filter(col("category_rank") <= 3).collect(),
"monthly_trends": monthly_trend.collect(),
"market_share": market_concentration.collect()
}
return distribution_result
基于大数据的客户购物订单数据分析系统文档展示
💖💖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
