计算机毕 设 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。
大家都可点赞、收藏、关注、有问题都可留言评论交流
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！你也可以在个人主页上↑↑联系我~~
⚡⚡获取源码主页-->：计算机毕设指导师
一手房数据分析系统- 简介
基于Django+Spark的深圳一手房成交数据分析系统是一个集成大数据处理技术与Web应用开发的房地产市场分析平台。系统采用Hadoop生态系统作为底层分布式存储架构，通过HDFS实现海量房产交易数据的可靠存储，结合Apache Spark强大的内存计算能力进行高效数据处理和分析。后端基于Python Django框架构建RESTful API服务，前端采用Vue.js结合ElementUI组件库设计交互界面，通过ECharts实现数据可视化展示。系统核心功能涵盖深圳房产市场时间趋势分析、各行政区市场空间对比、不同用途房产结构分析、供需关系评估以及价格驱动因素探索等五大维度。通过Spark SQL进行复杂数据查询和聚合计算，结合Pandas、NumPy等数据科学库实现统计分析算法，为用户提供全方位的房地产市场洞察。系统支持月度成交量走势、区域价格对比、户型面积分布、库存压力评估等多种分析视角，帮助用户深入理解深圳房地产市场的运行规律和发展趋势。
一手房数据分析系统-技术
开发语言：java或Python
数据库：MySQL
系统架构：B/S
前端：Vue+ElementUI+HTML+CSS+JavaScript+jQuery+Echarts
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)
一手房数据分析系统- 背景
随着深圳作为中国经济特区的快速发展，房地产市场已成为城市经济的重要组成部分。深圳房价的波动不仅直接影响居民的置业决策，也反映着城市发展的脉搏和经济活力。传统的房地产市场分析多依赖于简单的统计方法和小规模数据样本，难以全面反映市场的复杂变化规律。面对日益增长的房产交易数据量和多维度分析需求，传统的数据处理方式显得力不从心。大数据技术的成熟为房地产市场分析带来了新的机遇，Hadoop和Spark等分布式计算框架能够高效处理海量房产交易记录，挖掘隐藏在数据背后的市场趋势和规律。深圳作为国内房地产市场的风向标城市，其一手房成交数据具有典型的代表性和研究价值，通过对这些数据的深度分析，能够为市场参与者提供有价值的决策参考。
构建基于大数据技术的房产数据分析系统，能够为房地产市场的科学分析提供技术支撑。通过运用Spark等现代数据处理技术，系统能够快速处理和分析大规模房产交易数据，提升数据分析的效率和准确性。从实际应用角度来看，系统为购房者提供了区域房价对比、市场趋势分析等实用功能，帮助普通消费者在复杂的房地产市场中做出相对理性的决策。对于房地产从业人员而言，系统提供的市场供需分析、去化率评估等功能，可以作为业务决策的数据支持工具。从技术发展意义上，项目展示了大数据技术在房地产领域的具体应用，为相关技术的推广和应用提供了实践案例。对于学术研究来说，系统产生的分析结果可以为房地产市场研究提供数据基础，虽然作为毕业设计项目在规模和深度上有一定局限性，但在技术实现和应用场景结合方面仍具有一定的参考价值。
一手房数据分析系统-视频展示
一手房数据分析系统-图片展示
一手房数据分析系统-代码展示
from pyspark.sql.functions import col, sum, avg, count, when, month, year, dayofweek
from pyspark.sql.types import DoubleType, IntegerType
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("ShenZhenHouseAnalysis").config("spark.sql.adaptive.enabled", "true").config("spark.sql.adaptive.coalescePartitions.enabled", "true").getOrCreate()
@csrf_exempt
def market_trend_analysis(request):
df = spark.read.format("jdbc").option("url", "jdbc:mysql://localhost:3306/house_db").option("driver", "com.mysql.cj.jdbc.Driver").option("dbtable", "house_transaction").option("user", "root").option("password", "password").load()
df.createOrReplaceTempView("transactions")
monthly_data = spark.sql("SELECT YEAR(transaction_date) as year, MONTH(transaction_date) as month, SUM(transaction_count) as total_count, AVG(avg_price) as avg_price FROM transactions WHERE district = '全市' GROUP BY YEAR(transaction_date), MONTH(transaction_date) ORDER BY year, month")
residential_vs_commercial = spark.sql("SELECT YEAR(transaction_date) as year, MONTH(transaction_date) as month, property_type, SUM(transaction_count) as count FROM transactions WHERE district = '全市' GROUP BY YEAR(transaction_date), MONTH(transaction_date), property_type ORDER BY year, month")
inventory_trend = spark.sql("SELECT YEAR(transaction_date) as year, MONTH(transaction_date) as month, AVG(available_area) as avg_inventory FROM transactions WHERE district = '全市' GROUP BY YEAR(transaction_date), MONTH(transaction_date) ORDER BY year, month")
weekly_activity = spark.sql("SELECT DAYOFWEEK(transaction_date) as weekday, SUM(transaction_count) as total_count FROM transactions WHERE district != '全市' GROUP BY DAYOFWEEK(transaction_date) ORDER BY weekday")
monthly_df = monthly_data.toPandas()
residential_df = residential_vs_commercial.toPandas()
inventory_df = inventory_trend.toPandas()
weekly_df = weekly_activity.toPandas()
monthly_df['price_change'] = monthly_df['avg_price'].pct_change() * 100
monthly_df['volume_ma3'] = monthly_df['total_count'].rolling(window=3).mean()
residential_pivot = residential_df.pivot(index=['year', 'month'], columns='property_type', values='count').fillna(0)
inventory_df['inventory_change'] = inventory_df['avg_inventory'].diff()
weekly_df['activity_ratio'] = weekly_df['total_count'] / weekly_df['total_count'].sum() * 100
result = {
'monthly_trend': monthly_df.to_dict('records'),
'residential_commercial': residential_pivot.reset_index().to_dict('records'),
'inventory_analysis': inventory_df.to_dict('records'),
'weekly_pattern': weekly_df.to_dict('records')
}
return JsonResponse(result)
@csrf_exempt
def district_comparison_analysis(request):
df = spark.read.format("jdbc").option("url", "jdbc:mysql://localhost:3306/house_db").option("driver", "com.mysql.cj.jdbc.Driver").option("dbtable", "house_transaction").option("user", "root").option("password", "password").load()
df.createOrReplaceTempView("transactions")
district_volume = spark.sql("SELECT district, SUM(transaction_count) as total_volume FROM transactions WHERE district != '全市' GROUP BY district ORDER BY total_volume DESC")
district_price = spark.sql("SELECT district, SUM(transaction_count * avg_price * transaction_area) / SUM(transaction_count * transaction_area) as weighted_avg_price FROM transactions WHERE district != '全市' AND property_type = '住宅' GROUP BY district ORDER BY weighted_avg_price DESC")
district_unit_size = spark.sql("SELECT district, AVG(transaction_area / transaction_count) as avg_unit_size FROM transactions WHERE district != '全市' AND property_type = '住宅' AND transaction_count > 0 GROUP BY district ORDER BY avg_unit_size DESC")
district_inventory = spark.sql("SELECT district, AVG(available_units) as avg_inventory_pressure FROM transactions WHERE district != '全市' GROUP BY district ORDER BY avg_inventory_pressure DESC")
volume_df = district_volume.toPandas()
price_df = district_price.toPandas()
unit_size_df = district_unit_size.toPandas()
inventory_df = district_inventory.toPandas()
volume_df['volume_rank'] = volume_df['total_volume'].rank(ascending=False, method='dense')
price_df['price_level'] = pd.cut(price_df['weighted_avg_price'], bins=5, labels=['低价区', '中低价区', '中价区', '中高价区', '高价区'])
unit_size_df['size_category'] = np.where(unit_size_df['avg_unit_size'] < 70, '小户型主导', np.where(unit_size_df['avg_unit_size'] < 100, '中户型主导', '大户型主导'))
inventory_df['pressure_level'] = pd.cut(inventory_df['avg_inventory_pressure'], bins=3, labels=['低压力', '中等压力', '高压力'])
merged_analysis = volume_df.merge(price_df, on='district', how='outer').merge(unit_size_df, on='district', how='outer').merge(inventory_df, on='district', how='outer')
market_heat_index = (merged_analysis['total_volume'] / merged_analysis['total_volume'].max() * 0.4 + merged_analysis['weighted_avg_price'] / merged_analysis['weighted_avg_price'].max() * 0.3 + (merged_analysis['avg_inventory_pressure'].max() - merged_analysis['avg_inventory_pressure']) / merged_analysis['avg_inventory_pressure'].max() * 0.3) * 100
merged_analysis['market_heat_index'] = market_heat_index.fillna(0)
result = {
'district_ranking': merged_analysis.sort_values('market_heat_index', ascending=False).to_dict('records'),
'volume_analysis': volume_df.head(10).to_dict('records'),
'price_distribution': price_df.to_dict('records'),
'unit_size_preference': unit_size_df.to_dict('records')
}
return JsonResponse(result)
@csrf_exempt
def supply_demand_analysis(request):
df = spark.read.format("jdbc").option("url", "jdbc:mysql://localhost:3306/house_db").option("driver", "com.mysql.cj.jdbc.Driver").option("dbtable", "house_transaction").option("user", "root").option("password", "password").load()
df.createOrReplaceTempView("transactions")
district_supply_demand = spark.sql("SELECT district, SUM(transaction_count) as total_demand, AVG(available_units) as avg_supply FROM transactions WHERE district != '全市' GROUP BY district")
residential_cycle = spark.sql("SELECT YEAR(transaction_date) as year, MONTH(transaction_date) as month, AVG(available_units) as monthly_supply, AVG(transaction_count) as monthly_demand FROM transactions WHERE district = '全市' AND property_type = '住宅' GROUP BY YEAR(transaction_date), MONTH(transaction_date) ORDER BY year, month")
price_segment_analysis = spark.sql("SELECT CASE WHEN avg_price < 30000 THEN '<3万' WHEN avg_price < 50000 THEN '3-5万' WHEN avg_price < 80000 THEN '5-8万' ELSE '>8万' END as price_range, SUM(transaction_count) as demand_count, SUM(available_units) as supply_count FROM transactions WHERE district != '全市' GROUP BY CASE WHEN avg_price < 30000 THEN '<3万' WHEN avg_price < 50000 THEN '3-5万' WHEN avg_price < 80000 THEN '5-8万' ELSE '>8万' END")
district_turnover_rate = spark.sql("SELECT district, SUM(transaction_count) / SUM(available_units) as turnover_rate FROM transactions WHERE district != '全市' AND available_units > 0 GROUP BY district ORDER BY turnover_rate DESC")
supply_demand_df = district_supply_demand.toPandas()
cycle_df = residential_cycle.toPandas()
price_segment_df = price_segment_analysis.toPandas()
turnover_df = district_turnover_rate.toPandas()
supply_demand_df['supply_demand_ratio'] = supply_demand_df['avg_supply'] / supply_demand_df['total_demand']
supply_demand_df['market_status'] = np.where(supply_demand_df['supply_demand_ratio'] > 1.5, '供过于求', np.where(supply_demand_df['supply_demand_ratio'] > 0.8, '供需平衡', '供不应求'))
cycle_df['digest_months'] = np.where(cycle_df['monthly_demand'] > 0, cycle_df['monthly_supply'] / cycle_df['monthly_demand'], 0)
cycle_df['risk_level'] = np.where(cycle_df['digest_months'] > 12, '高风险', np.where(cycle_df['digest_months'] > 6, '中等风险', '低风险'))
price_segment_df['segment_ratio'] = price_segment_df['demand_count'] / price_segment_df['supply_count']
price_segment_df['market_opportunity'] = np.where(price_segment_df['segment_ratio'] > 0.8, '热销价位', np.where(price_segment_df['segment_ratio'] > 0.3, '正常价位', '滞销价位'))
turnover_df['efficiency_grade'] = pd.cut(turnover_df['turnover_rate'], bins=4, labels=['低效', '一般', '良好', '优秀'])
correlation_matrix = supply_demand_df[['total_demand', 'avg_supply', 'supply_demand_ratio']].corr()
result = {
'supply_demand_balance': supply_demand_df.to_dict('records'),
'digest_cycle_analysis': cycle_df.to_dict('records'),
'price_segment_opportunities': price_segment_df.to_dict('records'),
'district_efficiency_ranking': turnover_df.to_dict('records'),
'correlation_analysis': correlation_matrix.to_dict()
}
return JsonResponse(result)
一手房数据分析系统-结语
大数据毕业设计选题：深圳房价趋势分析系统Spark SQL开发教程 毕业设计/选题推荐/深度学习/数据分析/机器学习/数据挖掘/随机森林
计算机毕设不知道选什么？基于Spark+Django的深圳一手房数据分析系统解决技术难题
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-->：计算机毕设指导师
⚡⚡如果遇到具体的技术问题或计算机毕设方面需求！你也可以在个人主页上↑↑联系我~~
