Python大数据项目推荐：旅游用户行为数据分析系统毕设指南|系统设计

前言

• 💖💖作者：计算机程序员小杨
• 💙💙个人简介：我是一名计算机相关专业的从业者，擅长Java、微信小程序、Python、Golang、安卓Android等多个IT方向。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。热爱技术，喜欢钻研新工具和框架，也乐于通过代码解决实际问题，大家有技术代码这一块的问题可以问我！
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一.开发工具简介

• 大数据框架：Hadoop+Spark（本次没用Hive，支持定制）
• 开发语言：Python+Java（两个版本都支持）
• 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

二.系统内容简介

基于大数据的旅游网站用户行为数据分析系统是一个运用现代大数据技术构建的智能分析平台，该系统采用Hadoop分布式存储架构和Spark计算引擎作为核心技术支撑，结合Python编程语言的数据处理优势，实现对旅游网站用户行为的深度挖掘与分析。系统前端采用Vue框架配合ElementUI组件库构建用户界面，通过Echarts图表库实现数据的可视化展示，后端基于Django框架提供稳定的API服务，数据存储采用MySQL关系型数据库确保数据安全性。系统主要功能涵盖用户基础特征分析、用户分群与行为模式识别、用户互动行为追踪、社交网络影响力评估等核心模块，通过Spark SQL进行大规模数据查询优化，利用Pandas和NumPy进行精确的数据科学计算，最终通过可视化大屏为旅游企业提供直观的数据洞察，帮助企业了解用户偏好、优化产品策略、提升用户体验，为旅游行业的数字化转型提供技术支持。

三.系统功能演示

Python大数据项目推荐：旅游用户行为数据分析系统毕设指南|系统设计

四.系统界面展示

五.系统源码展示

from pyspark.sql import SparkSession
from pyspark.sql.functions import col, count, avg, sum, desc, asc, when, regexp_replace, split
from pyspark.sql.types import IntegerType, StringType
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("TourismUserBehaviorAnalysis").config("spark.sql.adaptive.enabled", "true").config("spark.sql.adaptive.coalescePartitions.enabled", "true").getOrCreate()

@csrf_exempt
def user_basic_feature_analysis(request):
    user_behavior_df = spark.read.option("header", "true").option("inferSchema", "true").csv("hdfs://localhost:9000/tourism_data/user_behavior.csv")
    user_info_df = spark.read.option("header", "true").option("inferSchema", "true").csv("hdfs://localhost:9000/tourism_data/user_info.csv")
    merged_df = user_behavior_df.join(user_info_df, on="user_id", how="inner")
    age_distribution = merged_df.groupBy("age_group").agg(count("user_id").alias("user_count"), avg("session_duration").alias("avg_session_time")).orderBy(desc("user_count"))
    gender_behavior = merged_df.groupBy("gender").agg(count("user_id").alias("total_users"), avg("page_views").alias("avg_page_views"), sum("purchase_amount").alias("total_purchase")).orderBy("gender")
    city_analysis = merged_df.groupBy("city").agg(count("user_id").alias("user_count"), avg("visit_frequency").alias("avg_visit_freq")).filter(col("user_count") > 10).orderBy(desc("user_count"))
    device_preference = merged_df.groupBy("device_type").agg(count("user_id").alias("device_users"), avg("bounce_rate").alias("avg_bounce_rate")).orderBy(desc("device_users"))
    registration_trend = merged_df.groupBy("registration_month").agg(count("user_id").alias("new_users")).orderBy("registration_month")
    user_activity_level = merged_df.withColumn("activity_level", when(col("total_clicks") > 100, "高活跃").when(col("total_clicks") > 50, "中活跃").otherwise("低活跃")).groupBy("activity_level").agg(count("user_id").alias("user_count"), avg("conversion_rate").alias("avg_conversion"))
    age_pandas = age_distribution.toPandas()
    gender_pandas = gender_behavior.toPandas()
    city_pandas = city_analysis.toPandas()
    device_pandas = device_preference.toPandas()
    trend_pandas = registration_trend.toPandas()
    activity_pandas = user_activity_level.toPandas()
    result_data = {"age_distribution": age_pandas.to_dict('records'), "gender_behavior": gender_pandas.to_dict('records'), "city_analysis": city_pandas.to_dict('records'), "device_preference": device_pandas.to_dict('records'), "registration_trend": trend_pandas.to_dict('records'), "activity_levels": activity_pandas.to_dict('records')}
    return JsonResponse({"status": "success", "data": result_data})

@csrf_exempt  
def user_clustering_behavior_analysis(request):
    behavior_df = spark.read.option("header", "true").option("inferSchema", "true").csv("hdfs://localhost:9000/tourism_data/user_behavior_detail.csv")
    user_features = behavior_df.groupBy("user_id").agg(avg("session_duration").alias("avg_session"), sum("page_views").alias("total_views"), count("visit_date").alias("visit_frequency"), avg("purchase_amount").alias("avg_purchase"), sum("search_count").alias("total_searches"))
    feature_pandas = user_features.toPandas()
    feature_matrix = feature_pandas[['avg_session', 'total_views', 'visit_frequency', 'avg_purchase', 'total_searches']].fillna(0)
    normalized_features = (feature_matrix - feature_matrix.mean()) / feature_matrix.std()
    from sklearn.cluster import KMeans
    kmeans = KMeans(n_clusters=4, random_state=42, n_init=10)
    feature_pandas['cluster'] = kmeans.fit_predict(normalized_features)
    cluster_spark_df = spark.createDataFrame(feature_pandas)
    cluster_stats = cluster_spark_df.groupBy("cluster").agg(count("user_id").alias("cluster_size"), avg("avg_session").alias("avg_session_time"), avg("total_views").alias("avg_page_views"), avg("visit_frequency").alias("avg_visits"), avg("avg_purchase").alias("avg_spending")).orderBy("cluster")
    behavior_patterns = behavior_df.join(cluster_spark_df.select("user_id", "cluster"), on="user_id").groupBy("cluster", "behavior_type").agg(count("user_id").alias("behavior_count")).orderBy("cluster", desc("behavior_count"))
    time_pattern = behavior_df.join(cluster_spark_df.select("user_id", "cluster"), on="user_id").groupBy("cluster", "hour_of_day").agg(count("user_id").alias("activity_count")).orderBy("cluster", "hour_of_day")
    cluster_labels = cluster_spark_df.withColumn("cluster_label", when(col("cluster") == 0, "价格敏感型").when(col("cluster") == 1, "高价值客户").when(col("cluster") == 2, "浏览型用户").otherwise("潜力客户"))
    cluster_stats_pandas = cluster_stats.toPandas()
    behavior_patterns_pandas = behavior_patterns.toPandas()
    time_pattern_pandas = time_pattern.toPandas()
    labels_pandas = cluster_labels.toPandas()
    clustering_result = {"cluster_statistics": cluster_stats_pandas.to_dict('records'), "behavior_patterns": behavior_patterns_pandas.to_dict('records'), "time_patterns": time_pattern_pandas.to_dict('records'), "cluster_labels": labels_pandas.to_dict('records')}
    return JsonResponse({"status": "success", "data": clustering_result})

@csrf_exempt
def user_interaction_behavior_analysis(request):
    interaction_df = spark.read.option("header", "true").option("inferSchema", "true").csv("hdfs://localhost:9000/tourism_data/user_interactions.csv")
    click_analysis = interaction_df.filter(col("interaction_type") == "click").groupBy("page_category").agg(count("interaction_id").alias("click_count"), count("user_id").alias("unique_users")).orderBy(desc("click_count"))
    search_behavior = interaction_df.filter(col("interaction_type") == "search").groupBy("search_keyword").agg(count("interaction_id").alias("search_frequency")).filter(col("search_frequency") > 5).orderBy(desc("search_frequency"))
    favorite_analysis = interaction_df.filter(col("interaction_type") == "favorite").groupBy("content_type").agg(count("interaction_id").alias("favorite_count"), count("user_id").alias("users_favorited")).orderBy(desc("favorite_count"))
    comment_engagement = interaction_df.filter(col("interaction_type") == "comment").groupBy("user_id").agg(count("interaction_id").alias("comment_count"), avg("interaction_duration").alias("avg_comment_time")).filter(col("comment_count") > 3).orderBy(desc("comment_count"))
    share_analysis = interaction_df.filter(col("interaction_type") == "share").groupBy("share_platform").agg(count("interaction_id").alias("share_count"), count("user_id").alias("sharing_users")).orderBy(desc("share_count"))
    interaction_sequence = interaction_df.orderBy("user_id", "interaction_timestamp").select("user_id", "interaction_type", "page_category", "interaction_timestamp")
    user_journey = interaction_sequence.groupBy("user_id").agg(count("interaction_type").alias("total_interactions"), collect_list("interaction_type").alias("interaction_path"))
    conversion_funnel = interaction_df.groupBy("user_id").pivot("interaction_type").agg(count("interaction_id")).fillna(0)
    funnel_stats = conversion_funnel.agg(sum("click").alias("total_clicks"), sum("view").alias("total_views"), sum("favorite").alias("total_favorites"), sum("purchase").alias("total_purchases"))
    engagement_score = interaction_df.groupBy("user_id").agg((sum(when(col("interaction_type") == "click", 1).otherwise(0)) * 1 + sum(when(col("interaction_type") == "favorite", 3).otherwise(0)) + sum(when(col("interaction_type") == "comment", 5).otherwise(0)) + sum(when(col("interaction_type") == "share", 4).otherwise(0))).alias("engagement_score")).orderBy(desc("engagement_score"))
    click_pandas = click_analysis.toPandas()
    search_pandas = search_behavior.toPandas()
    favorite_pandas = favorite_analysis.toPandas()
    comment_pandas = comment_engagement.toPandas()
    share_pandas = share_analysis.toPandas()
    funnel_pandas = funnel_stats.toPandas()
    engagement_pandas = engagement_score.toPandas()
    interaction_result = {"click_analysis": click_pandas.to_dict('records'), "search_behavior": search_pandas.to_dict('records'), "favorite_analysis": favorite_pandas.to_dict('records'), "comment_engagement": comment_pandas.to_dict('records'), "share_analysis": share_pandas.to_dict('records'), "conversion_funnel": funnel_pandas.to_dict('records'), "engagement_scores": engagement_pandas.head(100).to_dict('records')}
    return JsonResponse({"status": "success", "data": interaction_result})

六.系统文档展示

结束

• 💛💛想说的话：感谢大家的关注与支持！
• 💕💕文末获取源码联系 计算机程序员小杨
• 💜💜
• 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
• 计算机毕业设计选题
• 💜💜