✍✍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！ ⚡⚡ Java实战 | SpringBoot/SSM Python实战项目 | Django 微信小程序/安卓实战项目 大数据实战项目 ⚡⚡获取源码主页-->计算机编程指导师
信用卡交易诈骗数据分析与可视化系统-简介
基于Hadoop+Django的信用卡交易诈骗数据分析与可视化系统是一个集成了大数据处理技术和Web开发框架的综合性分析平台。该系统充分利用Hadoop分布式存储架构的优势，通过HDFS存储海量信用卡交易数据，结合Spark分布式计算引擎进行高效的数据处理和分析。系统采用Django作为后端开发框架，提供稳定的数据接口服务，前端运用Vue.js配合ElementUI组件库构建直观的用户界面，通过Echarts图表库实现丰富的数据可视化效果。在数据分析层面，系统运用Pandas和NumPy等Python科学计算库对信用卡交易数据进行深度挖掘，从交易金额、地理位置、时间分布、支付方式等多个维度分析欺诈交易模式。系统还集成了K-Means聚类算法，能够根据用户交易行为特征进行智能分群，识别不同风险等级的交易模式。通过MySQL数据库存储分析结果和用户信息，系统实现了完整的数据流转和处理链路，为金融机构的风险控制决策提供数据支撑。
信用卡交易诈骗数据分析与可视化系统-技术
开发语言：Python或Java 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) 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
信用卡交易诈骗数据分析与可视化系统-背景
随着移动支付和电子商务的快速发展，信用卡已经成为人们日常消费的重要支付工具，交易量呈现爆炸式增长态势。然而，在带来便利的同时，信用卡诈骗问题也日益严重，不法分子通过技术手段窃取用户信息、伪造交易等方式进行欺诈活动，给个人用户和金融机构造成了巨大经济损失。传统的人工审核方式已经无法应对海量交易数据的实时监控需求，而简单的规则引擎也难以识别复杂多变的诈骗手段。面对这一挑战，金融科技领域迫切需要运用大数据技术和机器学习方法来构建智能化的诈骗检测系统。Hadoop作为成熟的大数据处理框架，能够有效处理和存储大规模交易数据，而Spark的内存计算特性则为实时数据分析提供了强大支撑。通过结合现代Web技术栈，可以构建出既具备强大数据处理能力又拥有友好用户界面的分析系统，这正是当前金融风控领域技术发展的重要方向。
本课题的研究具有多方面的实际应用价值和学术意义。从技术实践角度来看，该系统整合了大数据存储、分布式计算、机器学习和Web开发等多项核心技术，为计算机专业学生提供了一个综合性的实践平台，有助于加深对现代信息技术体系的理解和掌握。在数据分析方法方面，系统通过多维度特征提取和聚类分析，探索了交易行为模式与诈骗风险之间的关联关系，为相关领域的研究提供了有价值的参考案例。对于金融机构而言，该系统提出的分析框架和可视化方案可以为实际的风控业务提供技术思路，帮助相关从业人员更直观地理解交易数据中隐藏的风险信号。教育层面上，项目涵盖了从数据预处理、特征工程到模型构建和结果展示的完整流程，体现了数据科学项目的典型工作范式，对于培养学生的工程实践能力和问题解决思维具有积极作用。虽然作为毕业设计项目在规模和复杂度上存在一定局限性，但其所采用的技术路线和分析思路为后续深入研究奠定了基础，也为类似应用场景的系统开发提供了可借鉴的经验。
信用卡交易诈骗数据分析与可视化系统-视频展示
信用卡交易诈骗数据分析与可视化系统-图片展示
信用卡交易诈骗数据分析与可视化系统-代码展示
from pyspark.sql.functions import col, when, count, avg, stddev, percentile_approx
from pyspark.ml.clustering import KMeans
from pyspark.ml.feature import VectorAssembler
from sklearn.preprocessing import StandardScaler
import pandas as pd
import numpy as np
from django.http import JsonResponse
from django.views import View
import json
spark = SparkSession.builder.appName("CreditCardFraudAnalysis").config("spark.sql.adaptive.enabled", "true").config("spark.sql.adaptive.coalescePartitions.enabled", "true").getOrCreate()
def fraud_transaction_analysis(request):
df = spark.read.option("header", "true").option("inferSchema", "true").csv("hdfs://localhost:9000/credit_card_data/transactions.csv")
total_transactions = df.count()
fraud_transactions = df.filter(col("fraud") == 1).count()
normal_transactions = total_transactions - fraud_transactions
fraud_rate = round((fraud_transactions / total_transactions) * 100, 2)
channel_analysis = df.groupBy("online_order").agg(count("*").alias("transaction_count"), (count(when(col("fraud") == 1, 1)) / count("*") * 100).alias("fraud_rate")).collect()
online_stats = next((row for row in channel_analysis if row["online_order"] == 1), None)
offline_stats = next((row for row in channel_analysis if row["online_order"] == 0), None)
chip_analysis = df.groupBy("used_chip").agg(count("*").alias("transaction_count"), (count(when(col("fraud") == 1, 1)) / count("*") * 100).alias("fraud_rate")).collect()
chip_used_stats = next((row for row in chip_analysis if row["used_chip"] == 1), None)
chip_not_used_stats = next((row for row in chip_analysis if row["used_chip"] == 0), None)
pin_analysis = df.groupBy("used_pin_number").agg(count("*").alias("transaction_count"), (count(when(col("fraud") == 1, 1)) / count("*") * 100).alias("fraud_rate")).collect()
pin_used_stats = next((row for row in pin_analysis if row["used_pin_number"] == 1), None)
pin_not_used_stats = next((row for row in pin_analysis if row["used_pin_number"] == 0), None)
retailer_analysis = df.groupBy("repeat_retailer").agg(count("*").alias("transaction_count"), (count(when(col("fraud") == 1, 1)) / count("*") * 100).alias("fraud_rate")).collect()
repeat_retailer_stats = next((row for row in retailer_analysis if row["repeat_retailer"] == 1), None)
new_retailer_stats = next((row for row in retailer_analysis if row["repeat_retailer"] == 0), None)
distance_percentiles = df.select(percentile_approx("distance_from_home", 0.33).alias("p33"), percentile_approx("distance_from_home", 0.66).alias("p66")).collect()[0]
distance_analysis = df.withColumn("distance_category", when(col("distance_from_home") <= distance_percentiles["p33"], "近距离").when(col("distance_from_home") <= distance_percentiles["p66"], "中距离").otherwise("远距离")).groupBy("distance_category").agg(count("*").alias("transaction_count"), (count(when(col("fraud") == 1, 1)) / count("*") * 100).alias("fraud_rate")).collect()
avg_distances = df.groupBy("fraud").agg(avg("distance_from_home").alias("avg_distance_from_home"), avg("distance_from_last_transaction").alias("avg_distance_from_last_transaction")).collect()
fraud_avg_distance = next((row for row in avg_distances if row["fraud"] == 1), None)
normal_avg_distance = next((row for row in avg_distances if row["fraud"] == 0), None)
ratio_percentiles = df.select(percentile_approx("ratio_to_median_purchase_price", 0.25).alias("p25"), percentile_approx("ratio_to_median_purchase_price", 0.75).alias("p75"), percentile_approx("ratio_to_median_purchase_price", 0.9).alias("p90")).collect()[0]
ratio_analysis = df.withColumn("ratio_category", when(col("ratio_to_median_purchase_price") <= ratio_percentiles["p25"], "低于平均").when(col("ratio_to_median_purchase_price") <= ratio_percentiles["p75"], "正常范围").when(col("ratio_to_median_purchase_price") <= ratio_percentiles["p90"], "略高于平均").otherwise("远高于平均")).groupBy("ratio_category").agg(count("*").alias("transaction_count"), (count(when(col("fraud") == 1, 1)) / count("*") * 100).alias("fraud_rate")).collect()
result_data = {"total_transactions": total_transactions, "fraud_transactions": fraud_transactions, "normal_transactions": normal_transactions, "fraud_rate": fraud_rate, "channel_analysis": {"online": {"count": online_stats["transaction_count"] if online_stats else 0, "fraud_rate": round(online_stats["fraud_rate"], 2) if online_stats else 0}, "offline": {"count": offline_stats["transaction_count"] if offline_stats else 0, "fraud_rate": round(offline_stats["fraud_rate"], 2) if offline_stats else 0}}, "security_analysis": {"chip_used": {"count": chip_used_stats["transaction_count"] if chip_used_stats else 0, "fraud_rate": round(chip_used_stats["fraud_rate"], 2) if chip_used_stats else 0}, "chip_not_used": {"count": chip_not_used_stats["transaction_count"] if chip_not_used_stats else 0, "fraud_rate": round(chip_not_used_stats["fraud_rate"], 2) if chip_not_used_stats else 0}, "pin_used": {"count": pin_used_stats["transaction_count"] if pin_used_stats else 0, "fraud_rate": round(pin_used_stats["fraud_rate"], 2) if pin_used_stats else 0}, "pin_not_used": {"count": pin_not_used_stats["transaction_count"] if pin_not_used_stats else 0, "fraud_rate": round(pin_not_used_stats["fraud_rate"], 2) if pin_not_used_stats else 0}}, "distance_analysis": [{"category": row["distance_category"], "count": row["transaction_count"], "fraud_rate": round(row["fraud_rate"], 2)} for row in distance_analysis], "amount_analysis": [{"category": row["ratio_category"], "count": row["transaction_count"], "fraud_rate": round(row["fraud_rate"], 2)} for row in ratio_analysis]}
return JsonResponse(result_data)
def complex_scenario_analysis(request):
df = spark.read.option("header", "true").option("inferSchema", "true").csv("hdfs://localhost:9000/credit_card_data/transactions.csv")
online_no_pin_scenario = df.filter((col("online_order") == 1) & (col("used_pin_number") == 0))
online_no_pin_total = online_no_pin_scenario.count()
online_no_pin_fraud = online_no_pin_scenario.filter(col("fraud") == 1).count()
online_no_pin_fraud_rate = round((online_no_pin_fraud / online_no_pin_total * 100), 2) if online_no_pin_total > 0 else 0
distance_p90 = df.select(percentile_approx("distance_from_home", 0.9).alias("p90")).collect()[0]["p90"]
ratio_p90 = df.select(percentile_approx("ratio_to_median_purchase_price", 0.9).alias("p90")).collect()[0]["p90"]
high_risk_scenario = df.filter((col("distance_from_home") > distance_p90) & (col("ratio_to_median_purchase_price") > ratio_p90))
high_risk_total = high_risk_scenario.count()
high_risk_fraud = high_risk_scenario.filter(col("fraud") == 1).count()
high_risk_fraud_rate = round((high_risk_fraud / high_risk_total * 100), 2) if high_risk_total > 0 else 0
new_retailer_high_amount = df.filter((col("repeat_retailer") == 0) & (col("ratio_to_median_purchase_price") > ratio_p90))
new_retailer_total = new_retailer_high_amount.count()
new_retailer_fraud = new_retailer_high_amount.filter(col("fraud") == 1).count()
new_retailer_fraud_rate = round((new_retailer_fraud / new_retailer_total * 100), 2) if new_retailer_total > 0 else 0
night_transactions = df.filter(col("time").between(22, 6))
night_total = night_transactions.count()
night_fraud = night_transactions.filter(col("fraud") == 1).count()
night_fraud_rate = round((night_fraud / night_total * 100), 2) if night_total > 0 else 0
weekend_high_amount = df.filter((col("day_of_week").isin([6, 0])) & (col("ratio_to_median_purchase_price") > ratio_p90))
weekend_total = weekend_high_amount.count()
weekend_fraud = weekend_high_amount.filter(col("fraud") == 1).count()
weekend_fraud_rate = round((weekend_fraud / weekend_total * 100), 2) if weekend_total > 0 else 0
multiple_risk_factors = df.filter((col("online_order") == 1) & (col("used_pin_number") == 0) & (col("distance_from_home") > distance_p90))
multiple_risk_total = multiple_risk_factors.count()
multiple_risk_fraud = multiple_risk_factors.filter(col("fraud") == 1).count()
multiple_risk_fraud_rate = round((multiple_risk_fraud / multiple_risk_total * 100), 2) if multiple_risk_total > 0 else 0
scenario_comparison = df.groupBy("online_order", "used_chip", "used_pin_number").agg(count("*").alias("total_count"), count(when(col("fraud") == 1, 1)).alias("fraud_count"), (count(when(col("fraud") == 1, 1)) / count("*") * 100).alias("fraud_rate")).orderBy(col("fraud_rate").desc()).collect()
result_data = {"online_no_pin_scenario": {"total": online_no_pin_total, "fraud": online_no_pin_fraud, "fraud_rate": online_no_pin_fraud_rate}, "high_risk_scenario": {"total": high_risk_total, "fraud": high_risk_fraud, "fraud_rate": high_risk_fraud_rate, "distance_threshold": round(distance_p90, 2), "ratio_threshold": round(ratio_p90, 2)}, "new_retailer_scenario": {"total": new_retailer_total, "fraud": new_retailer_fraud, "fraud_rate": new_retailer_fraud_rate}, "night_scenario": {"total": night_total, "fraud": night_fraud, "fraud_rate": night_fraud_rate}, "weekend_scenario": {"total": weekend_total, "fraud": weekend_fraud, "fraud_rate": weekend_fraud_rate}, "multiple_risk_scenario": {"total": multiple_risk_total, "fraud": multiple_risk_fraud, "fraud_rate": multiple_risk_fraud_rate}, "scenario_comparison": [{"online_order": row["online_order"], "used_chip": row["used_chip"], "used_pin_number": row["used_pin_number"], "total_count": row["total_count"], "fraud_count": row["fraud_count"], "fraud_rate": round(row["fraud_rate"], 2)} for row in scenario_comparison[:10]]}
return JsonResponse(result_data)
def user_behavior_clustering(request):
df = spark.read.option("header", "true").option("inferSchema", "true").csv("hdfs://localhost:9000/credit_card_data/transactions.csv")
feature_cols = ["distance_from_home", "distance_from_last_transaction", "ratio_to_median_purchase_price"]
assembler = VectorAssembler(inputCols=feature_cols, outputCol="features")
feature_df = assembler.transform(df).select("features", "fraud", "online_order", "used_chip", "used_pin_number")
kmeans = KMeans(k=5, featuresCol="features", predictionCol="cluster_id", seed=42, maxIter=100)
model = kmeans.fit(feature_df)
clustered_df = model.transform(feature_df)
cluster_stats = clustered_df.groupBy("cluster_id").agg(count("*").alias("total_count"), count(when(col("fraud") == 1, 1)).alias("fraud_count"), (count(when(col("fraud") == 1, 1)) / count("*") * 100).alias("fraud_rate"), avg("distance_from_home").alias("avg_distance_home"), avg("distance_from_last_transaction").alias("avg_distance_last"), avg("ratio_to_median_purchase_price").alias("avg_ratio"), (count(when(col("online_order") == 1, 1)) / count("*") * 100).alias("online_rate"), (count(when(col("used_chip") == 1, 1)) / count("*") * 100).alias("chip_usage_rate"), (count(when(col("used_pin_number") == 1, 1)) / count("*") * 100).alias("pin_usage_rate")).orderBy("cluster_id").collect()
cluster_centers = model.clusterCenters()
cluster_profiles = []
for i, row in enumerate(cluster_stats):
cluster_profile = {"cluster_id": row["cluster_id"], "total_count": row["total_count"], "fraud_count": row["fraud_count"], "fraud_rate": round(row["fraud_rate"], 2), "avg_distance_home": round(row["avg_distance_home"], 2), "avg_distance_last": round(row["avg_distance_last"], 2), "avg_ratio": round(row["avg_ratio"], 2), "online_rate": round(row["online_rate"], 2), "chip_usage_rate": round(row["chip_usage_rate"], 2), "pin_usage_rate": round(row["pin_usage_rate"], 2), "center_coordinates": [round(float(coord), 3) for coord in cluster_centers[i]]}
if row["fraud_rate"] > 10:
cluster_profile["risk_level"] = "高风险"
elif row["fraud_rate"] > 5:
cluster_profile["risk_level"] = "中等风险"
else:
cluster_profile["risk_level"] = "低风险"
if row["avg_distance_home"] > 50 and row["avg_ratio"] > 2:
cluster_profile["behavior_type"] = "异地大额型"
elif row["avg_distance_home"] < 10 and row["avg_ratio"] < 1:
cluster_profile["behavior_type"] = "本地小额型"
elif row["online_rate"] > 70:
cluster_profile["behavior_type"] = "线上偏好型"
else:
cluster_profile["behavior_type"] = "均衡消费型"
cluster_profiles.append(cluster_profile)
high_risk_clusters = [profile for profile in cluster_profiles if profile["risk_level"] == "高风险"]
low_risk_clusters = [profile for profile in cluster_profiles if profile["risk_level"] == "低风险"]
cluster_fraud_comparison = sorted(cluster_profiles, key=lambda x: x["fraud_rate"], reverse=True)
result_data = {"cluster_profiles": cluster_profiles, "high_risk_clusters": high_risk_clusters, "low_risk_clusters": low_risk_clusters, "cluster_fraud_ranking": cluster_fraud_comparison, "total_clusters": len(cluster_profiles), "feature_importance": {"distance_from_home": "交易地点与居住地距离", "distance_from_last_transaction": "与上次交易地点距离", "ratio_to_median_purchase_price": "相对于个人平均消费的金额比例"}}
return JsonResponse(result_data)
信用卡交易诈骗数据分析与可视化系统-结语
大学导师推荐：2026年最值得做的Hadoop信用卡诈骗检测毕设项目 毕业设计/选题推荐/深度学习/数据分析/数据挖掘/机器学习 支持我记得一键三连，再点个关注，学习不迷路！如果遇到有什么技术问题，欢迎在评论区留言！感谢支持！
⚡⚡获取源码主页-->计算机编程指导师 ⚡⚡有技术问题或者获取源代码！欢迎在评论区一起交流！ ⚡⚡大家点赞、收藏、关注、有问题都可留言评论交流！ ⚡⚡有问题可以在主页上详细资料里↑↑联系我~~
