💖💖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
基于大数据的手机详细信息数据分析系统介绍
手机数据分析系统是一款基于Spark+Python完美融合的大数据项目，专门针对手机详细信息进行深度数据挖掘与智能分析。系统采用Hadoop分布式存储架构结合Spark计算引擎，实现对海量手机数据的高效处理与实时分析。前端采用Vue+ElementUI+Echarts技术栈打造直观的数据可视化界面，后端基于Django框架构建稳定的API服务，数据存储依托MySQL数据库确保数据安全性。系统核心功能涵盖手机信息管理、手机品牌策略分析、整体市场格局分析、用户群体画像分析、硬件价格关联分析以及历年技术趋势分析等模块，通过Spark SQL和Pandas、NumPy等数据科学库实现复杂的统计计算与机器学习算法，为用户提供全方位的手机市场洞察与商业智能决策支持，是一个集数据采集、存储、计算、分析、可视化于一体的完整大数据解决方案。
基于大数据的手机详细信息数据分析系统演示视频
基于大数据的手机详细信息数据分析系统演示图片
基于大数据的手机详细信息数据分析系统代码展示
from pyspark.sql import SparkSession
from pyspark.sql.functions import col, avg, count, sum, max, min, when, desc, asc
from pyspark.ml.feature import StringIndexer, VectorAssembler
from pyspark.ml.clustering import KMeans
from django.http import JsonResponse
import pandas as pd
import numpy as np
import json
spark = SparkSession.builder.appName("PhoneDataAnalysis").master("local[*]").getOrCreate()
def phone_brand_strategy_analysis(request):
phone_df = spark.read.format("jdbc").options(driver="com.mysql.cj.jdbc.Driver", url="jdbc:mysql://localhost:3306/phone_db", dbtable="phone_info", user="root", password="123456").load()
brand_analysis = phone_df.groupBy("brand").agg(count("*").alias("phone_count"), avg("price").alias("avg_price"), avg("screen_size").alias("avg_screen"), avg("battery_capacity").alias("avg_battery"), avg("ram_size").alias("avg_ram"), max("price").alias("max_price"), min("price").alias("min_price"))
brand_market_share = phone_df.groupBy("brand").agg(count("*").alias("count")).withColumn("market_share", col("count") * 100.0 / phone_df.count())
price_range_analysis = phone_df.withColumn("price_range", when(col("price") < 1000, "低端").when(col("price") < 3000, "中端").otherwise("高端")).groupBy("brand", "price_range").count()
performance_score = phone_df.withColumn("performance_score", col("cpu_score") * 0.3 + col("gpu_score") * 0.2 + col("ram_size") * 0.3 + col("storage_size") * 0.2).groupBy("brand").agg(avg("performance_score").alias("avg_performance"))
brand_innovation = phone_df.filter(col("release_year") >= 2022).groupBy("brand").agg(count("*").alias("new_models"), avg("camera_pixels").alias("avg_camera"), count(when(col("has_5g") == True, 1)).alias("5g_models"))
competitive_analysis = phone_df.join(brand_analysis.select("brand", "avg_price"), "brand").withColumn("price_competitiveness", when(col("price") < col("avg_price"), "具有价格优势").otherwise("价格偏高"))
brand_loyalty = phone_df.groupBy("brand", "user_rating").count().withColumn("satisfaction_level", when(col("user_rating") >= 4.5, "高满意度").when(col("user_rating") >= 4.0, "中等满意度").otherwise("低满意度"))
feature_preference = phone_df.groupBy("brand").agg(avg("screen_size").alias("preferred_screen"), avg("battery_capacity").alias("preferred_battery"), avg("camera_pixels").alias("preferred_camera"))
brand_positioning = phone_df.withColumn("positioning", when((col("price") > 4000) & (col("cpu_score") > 800), "旗舰").when((col("price") > 2000) & (col("price") <= 4000), "中高端").otherwise("入门")).groupBy("brand", "positioning").count()
trend_analysis = phone_df.groupBy("brand", "release_year").agg(count("*").alias("yearly_models"), avg("price").alias("yearly_avg_price")).orderBy("brand", "release_year")
result_data = {"brand_analysis": [row.asDict() for row in brand_analysis.collect()], "market_share": [row.asDict() for row in brand_market_share.collect()], "price_range": [row.asDict() for row in price_range_analysis.collect()], "performance": [row.asDict() for row in performance_score.collect()], "innovation": [row.asDict() for row in brand_innovation.collect()]}
return JsonResponse({"code": 200, "message": "品牌策略分析完成", "data": result_data})
def market_structure_analysis(request):
phone_df = spark.read.format("jdbc").options(driver="com.mysql.cj.jdbc.Driver", url="jdbc:mysql://localhost:3306/phone_db", dbtable="phone_info", user="root", password="123456").load()
overall_market = phone_df.agg(count("*").alias("total_models"), avg("price").alias("market_avg_price"), sum("sales_volume").alias("total_sales"), avg("user_rating").alias("overall_rating"))
price_distribution = phone_df.withColumn("price_segment", when(col("price") < 1000, "0-1000").when(col("price") < 2000, "1000-2000").when(col("price") < 3000, "2000-3000").when(col("price") < 4000, "3000-4000").when(col("price") < 5000, "4000-5000").otherwise("5000+")).groupBy("price_segment").agg(count("*").alias("model_count"), sum("sales_volume").alias("segment_sales"))
brand_concentration = phone_df.groupBy("brand").agg(sum("sales_volume").alias("brand_sales")).withColumn("sales_share", col("brand_sales") * 100.0 / phone_df.agg(sum("sales_volume")).collect()[0][0]).orderBy(desc("sales_share"))
technology_adoption = phone_df.agg(count(when(col("has_5g") == True, 1)).alias("5g_models"), count(when(col("has_wireless_charging") == True, 1)).alias("wireless_charging_models"), count(when(col("has_fast_charging") == True, 1)).alias("fast_charging_models"), count(when(col("screen_type") == "OLED", 1)).alias("oled_models"))
regional_analysis = phone_df.groupBy("target_region").agg(count("*").alias("region_models"), avg("price").alias("region_avg_price"), sum("sales_volume").alias("region_sales"))
seasonal_trends = phone_df.groupBy("release_month").agg(count("*").alias("monthly_releases"), avg("price").alias("monthly_avg_price")).orderBy("release_month")
competitive_landscape = phone_df.select("brand", "price", "cpu_score", "camera_pixels", "battery_capacity").rdd.map(lambda row: (row["brand"], float(row["price"]), float(row["cpu_score"]), float(row["camera_pixels"]), float(row["battery_capacity"]))).toDF(["brand", "price", "cpu_score", "camera_pixels", "battery_capacity"])
market_maturity = phone_df.groupBy("release_year").agg(count("*").alias("yearly_releases"), avg("price").alias("yearly_avg_price"), avg("cpu_score").alias("yearly_performance")).orderBy("release_year")
consumer_segments = phone_df.withColumn("consumer_segment", when((col("price") < 1500) & (col("battery_capacity") > 4000), "实用型").when((col("price") > 3000) & (col("camera_pixels") > 48), "高端用户").when((col("cpu_score") > 700) & (col("ram_size") >= 8), "性能用户").otherwise("普通用户")).groupBy("consumer_segment").agg(count("*").alias("segment_count"), avg("user_rating").alias("segment_satisfaction"))
market_gaps = phone_df.withColumn("feature_combination", when((col("price") < 2000) & (col("camera_pixels") > 64) & (col("battery_capacity") > 4500), "高性价比拍照").when((col("price") < 1500) & (col("cpu_score") > 600), "高性价比性能").otherwise("常规组合")).groupBy("feature_combination").count()
result_data = {"overall_market": [row.asDict() for row in overall_market.collect()], "price_distribution": [row.asDict() for row in price_distribution.collect()], "brand_concentration": [row.asDict() for row in brand_concentration.collect()], "technology_adoption": [row.asDict() for row in technology_adoption.collect()], "regional_analysis": [row.asDict() for row in regional_analysis.collect()], "consumer_segments": [row.asDict() for row in consumer_segments.collect()]}
return JsonResponse({"code": 200, "message": "市场格局分析完成", "data": result_data})
def user_portrait_analysis(request):
phone_df = spark.read.format("jdbc").options(driver="com.mysql.cj.jdbc.Driver", url="jdbc:mysql://localhost:3306/phone_db", dbtable="phone_info", user="root", password="123456").load()
user_df = spark.read.format("jdbc").options(driver="com.mysql.cj.jdbc.Driver", url="jdbc:mysql://localhost:3306/phone_db", dbtable="user_info", user="root", password="123456").load()
purchase_df = spark.read.format("jdbc").options(driver="com.mysql.cj.jdbc.Driver", url="jdbc:mysql://localhost:3306/phone_db", dbtable="purchase_records", user="root", password="123456").load()
user_phone_data = user_df.join(purchase_df, "user_id").join(phone_df, "phone_id")
age_preference = user_phone_data.withColumn("age_group", when(col("age") < 25, "18-25").when(col("age") < 35, "25-35").when(col("age") < 45, "35-45").otherwise("45+")).groupBy("age_group").agg(avg("price").alias("preferred_price"), avg("screen_size").alias("preferred_screen"), count("*").alias("group_count"))
gender_analysis = user_phone_data.groupBy("gender").agg(avg("price").alias("avg_spending"), avg("camera_pixels").alias("camera_preference"), count(when(col("color").isin("pink", "white", "gold"), 1)).alias("color_preference_count"))
income_correlation = user_phone_data.withColumn("income_level", when(col("monthly_income") < 5000, "低收入").when(col("monthly_income") < 10000, "中收入").when(col("monthly_income") < 20000, "中高收入").otherwise("高收入")).groupBy("income_level").agg(avg("price").alias("spending_power"), avg("cpu_score").alias("performance_preference"), count("*").alias("level_count"))
usage_pattern = user_phone_data.withColumn("usage_type", when(col("daily_usage_hours") > 8, "重度用户").when(col("daily_usage_hours") > 4, "中度用户").otherwise("轻度用户")).groupBy("usage_type").agg(avg("battery_capacity").alias("battery_requirement"), avg("ram_size").alias("memory_requirement"), avg("storage_size").alias("storage_requirement"))
brand_loyalty_analysis = user_phone_data.groupBy("user_id").agg(count("*").alias("purchase_count"), countDistinct("brand").alias("brand_diversity")).withColumn("loyalty_type", when(col("brand_diversity") == 1, "品牌忠诚").when(col("brand_diversity") <= 2, "相对忠诚").otherwise("品牌切换")).groupBy("loyalty_type").count()
feature_assembler = VectorAssembler(inputCols=["age", "monthly_income", "daily_usage_hours"], outputCol="features")
user_features = feature_assembler.transform(user_phone_data.select("user_id", "age", "monthly_income", "daily_usage_hours"))
kmeans = KMeans(k=4, seed=42, featuresCol="features")
kmeans_model = kmeans.fit(user_features)
clustered_users = kmeans_model.transform(user_features)
cluster_analysis = clustered_users.join(user_phone_data, "user_id").groupBy("prediction").agg(avg("age").alias("avg_age"), avg("monthly_income").alias("avg_income"), avg("price").alias("avg_phone_price"), count("*").alias("cluster_size"))
regional_preference = user_phone_data.groupBy("city").agg(avg("price").alias("city_avg_price"), countDistinct("brand").alias("brand_diversity"), count("*").alias("user_count")).orderBy(desc("user_count"))
purchase_timing = user_phone_data.groupBy("purchase_month").agg(count("*").alias("monthly_purchases"), avg("price").alias("monthly_avg_price")).orderBy("purchase_month")
satisfaction_analysis = user_phone_data.groupBy("user_rating").agg(count("*").alias("rating_count"), avg("price").alias("price_for_rating"), avg("usage_duration").alias("avg_usage_duration"))
behavioral_segments = user_phone_data.withColumn("behavior_type", when((col("camera_usage_frequency") > 80) & (col("photography_apps_count") > 5), "摄影爱好者").when((col("gaming_hours") > 3) & (col("cpu_score") > 700), "游戏玩家").when((col("business_apps_count") > 10) & (col("price") > 3000), "商务用户").otherwise("普通用户")).groupBy("behavior_type").agg(count("*").alias("segment_count"), avg("satisfaction_score").alias("avg_satisfaction"))
result_data = {"age_preference": [row.asDict() for row in age_preference.collect()], "gender_analysis": [row.asDict() for row in gender_analysis.collect()], "income_correlation": [row.asDict() for row in income_correlation.collect()], "usage_pattern": [row.asDict() for row in usage_pattern.collect()], "cluster_analysis": [row.asDict() for row in cluster_analysis.collect()], "behavioral_segments": [row.asDict() for row in behavioral_segments.collect()]}
return JsonResponse({"code": 200, "message": "用户群体画像分析完成", "data": result_data})
基于大数据的手机详细信息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
