大数据毕业设计推荐：基于Hadoop+Spark的学生创业数据分析可视化系统详解

一、个人简介

• 💖💖作者：计算机编程果茶熊
• 💙💙个人简介：曾长期从事计算机专业培训教学，担任过编程老师，同时本人也热爱上课教学，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。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
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二、系统介绍

• 大数据框架：Hadoop+Spark（Hive需要定制修改）
• 开发语言：Java+Python（两个版本都支持）
• 数据库：MySQL
• 后端框架：SpringBoot(Spring+SpringMVC+Mybatis)+Django（两个版本都支持）
• 前端：Vue+Echarts+HTML+CSS+JavaScript+jQuery

基于大数据的学生创业数据分析可视化系统是一套综合运用Hadoop分布式存储和Spark大数据计算框架的智能分析平台，专门针对高校学生创业行为数据进行深度挖掘与可视化展示。系统采用Python作为主要开发语言，后端基于Django框架构建RESTful API接口，前端运用Vue+ElementUI+Echarts技术栈实现交互界面和数据可视化。核心功能涵盖系统首页、用户中心、用户管理、创业信息管理、可视化大屏、学生综合画像分析、创业潜力挖掘分析、职业路径特征分析以及学生群体聚类分析等九大模块。系统通过HDFS存储海量学生数据，利用Spark SQL进行高效数据查询和处理，结合Pandas和NumPy进行数据预处理和统计分析，最终通过Echarts图表库将分析结果以直观的可视化形式呈现，为高校创业指导和学生职业规划提供数据支撑和决策参考。

三、基于大数据的学生创业数据分析可视化系统-视频解说

大数据毕业设计推荐：基于Hadoop+Spark的学生创业数据分析可视化系统详解

四、基于大数据的学生创业数据分析可视化系统-功能展示

五、基于大数据的学生创业数据分析可视化系统-代码展示

from pyspark.sql import SparkSession
from pyspark.sql.functions import col, count, avg, sum, when, desc
from pyspark.ml.feature import VectorAssembler
from pyspark.ml.clustering import KMeans
import pandas as pd
import numpy as np
from django.http import JsonResponse
import json

spark = SparkSession.builder.appName("StudentEntrepreneurshipAnalysis").config("spark.sql.adaptive.enabled", "true").getOrCreate()

def student_comprehensive_analysis(request):
    student_df = spark.read.format("jdbc").option("url", "jdbc:mysql://localhost:3306/student_db").option("dbtable", "student_info").option("user", "root").option("password", "123456").load()
    entrepreneurship_df = spark.read.format("jdbc").option("url", "jdbc:mysql://localhost:3306/student_db").option("dbtable", "entrepreneurship_info").option("user", "root").option("password", "123456").load()
    joined_df = student_df.join(entrepreneurship_df, student_df.student_id == entrepreneurship_df.student_id, "left")
    comprehensive_analysis = joined_df.groupBy("major", "grade").agg(avg("gpa").alias("avg_gpa"), count("*").alias("total_students"), sum(when(col("entrepreneurship_status") == 1, 1).otherwise(0)).alias("entrepreneurship_count"))
    comprehensive_analysis = comprehensive_analysis.withColumn("entrepreneurship_rate", col("entrepreneurship_count") / col("total_students"))
    result_pandas = comprehensive_analysis.toPandas()
    analysis_result = []
    for index, row in result_pandas.iterrows():
        analysis_result.append({"major": row["major"], "grade": row["grade"], "avg_gpa": round(row["avg_gpa"], 2), "total_students": int(row["total_students"]), "entrepreneurship_count": int(row["entrepreneurship_count"]), "entrepreneurship_rate": round(row["entrepreneurship_rate"], 4)})
    performance_metrics = joined_df.select("gpa", "social_practice_score", "innovation_score", "leadership_score").toPandas()
    correlation_matrix = performance_metrics.corr()
    correlation_data = []
    for i in range(len(correlation_matrix.columns)):
        for j in range(len(correlation_matrix.columns)):
            correlation_data.append({"x_metric": correlation_matrix.columns[i], "y_metric": correlation_matrix.columns[j], "correlation": round(correlation_matrix.iloc[i, j], 3)})
    return JsonResponse({"comprehensive_analysis": analysis_result, "correlation_analysis": correlation_data, "status": "success"})

def entrepreneurship_potential_mining(request):
    student_features_df = spark.read.format("jdbc").option("url", "jdbc:mysql://localhost:3306/student_db").option("dbtable", "student_features").option("user", "root").option("password", "123456").load()
    feature_cols = ["gpa", "social_practice_score", "innovation_score", "leadership_score", "communication_score", "risk_tolerance_score"]
    assembler = VectorAssembler(inputCols=feature_cols, outputCol="features")
    feature_vector_df = assembler.transform(student_features_df)
    potential_scores = feature_vector_df.select("student_id", "student_name", "major", *feature_cols)
    potential_scores = potential_scores.withColumn("innovation_weight", col("innovation_score") * 0.25)
    potential_scores = potential_scores.withColumn("leadership_weight", col("leadership_score") * 0.20)
    potential_scores = potential_scores.withColumn("risk_tolerance_weight", col("risk_tolerance_score") * 0.20)
    potential_scores = potential_scores.withColumn("gpa_weight", col("gpa") * 0.15)
    potential_scores = potential_scores.withColumn("social_practice_weight", col("social_practice_score") * 0.10)
    potential_scores = potential_scores.withColumn("communication_weight", col("communication_score") * 0.10)
    potential_scores = potential_scores.withColumn("total_potential_score", col("innovation_weight") + col("leadership_weight") + col("risk_tolerance_weight") + col("gpa_weight") + col("social_practice_weight") + col("communication_weight"))
    high_potential_students = potential_scores.filter(col("total_potential_score") >= 75).orderBy(desc("total_potential_score"))
    result_pandas = high_potential_students.select("student_id", "student_name", "major", "total_potential_score", "innovation_score", "leadership_score", "risk_tolerance_score").toPandas()
    potential_analysis = []
    for index, row in result_pandas.iterrows():
        potential_level = "高潜力" if row["total_potential_score"] >= 85 else "中等潜力" if row["total_potential_score"] >= 75 else "一般潜力"
        potential_analysis.append({"student_id": int(row["student_id"]), "student_name": row["student_name"], "major": row["major"], "total_score": round(row["total_potential_score"], 2), "innovation_score": int(row["innovation_score"]), "leadership_score": int(row["leadership_score"]), "risk_tolerance_score": int(row["risk_tolerance_score"]), "potential_level": potential_level})
    major_potential_distribution = potential_scores.groupBy("major").agg(avg("total_potential_score").alias("avg_potential"), count("*").alias("student_count"))
    major_distribution_pandas = major_potential_distribution.toPandas()
    major_stats = []
    for index, row in major_distribution_pandas.iterrows():
        major_stats.append({"major": row["major"], "avg_potential": round(row["avg_potential"], 2), "student_count": int(row["student_count"])})
    return JsonResponse({"high_potential_students": potential_analysis, "major_statistics": major_stats, "analysis_method": "加权评分法", "status": "success"})

def student_clustering_analysis(request):
    clustering_df = spark.read.format("jdbc").option("url", "jdbc:mysql://localhost:3306/student_db").option("dbtable", "student_clustering_data").option("user", "root").option("password", "123456").load()
    feature_columns = ["academic_performance", "social_activity_level", "innovation_ability", "practical_experience", "teamwork_ability"]
    assembler = VectorAssembler(inputCols=feature_columns, outputCol="features")
    feature_df = assembler.transform(clustering_df)
    kmeans = KMeans(k=4, seed=42, featuresCol="features", predictionCol="cluster")
    model = kmeans.fit(feature_df)
    clustered_df = model.transform(feature_df)
    cluster_summary = clustered_df.groupBy("cluster").agg(count("*").alias("student_count"), avg("academic_performance").alias("avg_academic"), avg("social_activity_level").alias("avg_social"), avg("innovation_ability").alias("avg_innovation"), avg("practical_experience").alias("avg_practical"), avg("teamwork_ability").alias("avg_teamwork"))
    summary_pandas = cluster_summary.toPandas()
    cluster_analysis = []
    cluster_labels = {0: "学术优势型", 1: "社交活跃型", 2: "创新实践型", 3: "综合平衡型"}
    for index, row in summary_pandas.iterrows():
        cluster_id = int(row["cluster"])
        cluster_characteristics = []
        if row["avg_academic"] >= 80:
            cluster_characteristics.append("学术表现优秀")
        if row["avg_social"] >= 75:
            cluster_characteristics.append("社交活跃度高")
        if row["avg_innovation"] >= 75:
            cluster_characteristics.append("创新能力强")
        if row["avg_practical"] >= 70:
            cluster_characteristics.append("实践经验丰富")
        cluster_analysis.append({"cluster_id": cluster_id, "cluster_name": cluster_labels.get(cluster_id, f"群体{cluster_id}"), "student_count": int(row["student_count"]), "avg_academic": round(row["avg_academic"], 2), "avg_social": round(row["avg_social"], 2), "avg_innovation": round(row["avg_innovation"], 2), "avg_practical": round(row["avg_practical"], 2), "avg_teamwork": round(row["avg_teamwork"], 2), "characteristics": cluster_characteristics})
    detailed_students = clustered_df.select("student_id", "student_name", "major", "cluster", *feature_columns).toPandas()
    student_cluster_details = []
    for index, row in detailed_students.iterrows():
        student_cluster_details.append({"student_id": int(row["student_id"]), "student_name": row["student_name"], "major": row["major"], "cluster_id": int(row["cluster"]), "cluster_name": cluster_labels.get(int(row["cluster"]), f"群体{int(row['cluster'])}"), "academic_score": int(row["academic_performance"]), "social_score": int(row["social_activity_level"]), "innovation_score": int(row["innovation_ability"])})
    return JsonResponse({"cluster_summary": cluster_analysis, "student_details": student_cluster_details, "clustering_method": "K-Means聚类", "cluster_count": 4, "status": "success"})

六、基于大数据的学生创业数据分析可视化系统-文档展示

七、END

• 💜💜
• 网站实战项目
• 安卓/小程序实战项目
• 大数据实战项目
• 计算机毕业设计选题
• 💕💕文末获取源码联系计算机编程果茶熊