基于大数据的学生创业数据分析系统 | 限时公开！完整学生创业数据分析系统源码：Hadoop+Spark+Vue全套技术栈

💖💖作者：计算机毕业设计江挽 💙💙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 深度学习实战项目

基于大数据的学生创业数据分析系统介绍

《学生创业数据分析系统》是一套基于大数据技术的综合分析平台，采用Hadoop分布式存储架构和Spark内存计算引擎，实现对学生创业相关数据的深度挖掘与智能分析。系统通过Django后端框架构建稳定的服务层，结合MySQL数据库进行结构化数据管理，前端采用Vue框架配合ElementUI组件库和Echarts可视化工具，为用户提供直观友好的交互体验。系统核心功能涵盖创业信息管理、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模块，通过Spark SQL进行大规模数据查询处理，利用Pandas和NumPy进行数据预处理和统计分析，最终在可视化大屏中展现分析结果。整个系统充分发挥了大数据技术在海量数据处理方面的优势，为高校创业指导和学生个人发展规划提供了科学的数据支撑，实现了从数据收集、存储、处理到可视化展示的完整技术链路。

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基于大数据的学生创业数据分析系统代码展示

from pyspark.sql import SparkSession
from pyspark.sql.functions import *
from pyspark.ml.clustering import KMeans
from pyspark.ml.feature import VectorAssembler
from pyspark.ml.evaluation import ClusteringEvaluator
import pandas as pd
import numpy as np
from django.http import JsonResponse
from django.views import View
import json

# 学生综合画像分析核心处理函数
def student_profile_analysis(request):
    spark = SparkSession.builder.appName("StudentProfileAnalysis").getOrCreate()
    df = spark.read.format("jdbc").option("url", "jdbc:mysql://localhost:3306/student_startup").option("dbtable", "student_info").option("user", "root").option("password", "123456").load()
    
    profile_data = df.select("student_id", "gpa", "innovation_count", "competition_score", "internship_duration", "leadership_experience", "social_activity_count").filter(col("student_id").isNotNull())
    
    normalized_gpa = (profile_data.gpa - profile_data.agg({"gpa": "min"}).collect()[0][0]) / (profile_data.agg({"gpa": "max"}).collect()[0][0] - profile_data.agg({"gpa": "min"}).collect()[0][0])
    
    profile_with_score = profile_data.withColumn("academic_score", normalized_gpa * 0.3 + col("innovation_count") * 0.2 + col("competition_score") * 0.25 + col("internship_duration") * 0.1 + col("leadership_experience") * 0.1 + col("social_activity_count") * 0.05)
    
    profile_result = profile_with_score.withColumn("profile_level", when(col("academic_score") > 0.8, "优秀").when(col("academic_score") > 0.6, "良好").when(col("academic_score") > 0.4, "中等").otherwise("待提升"))
    
    capability_analysis = profile_result.withColumn("innovation_ability", when(col("innovation_count") > 3, "强").when(col("innovation_count") > 1, "中").otherwise("弱")).withColumn("practical_ability", when(col("internship_duration") > 180, "强").when(col("internship_duration") > 90, "中").otherwise("弱")).withColumn("leadership_ability", when(col("leadership_experience") > 2, "强").when(col("leadership_experience") > 0, "中").otherwise("弱"))
    
    final_profile = capability_analysis.withColumn("comprehensive_evaluation", concat(col("profile_level"), lit("-"), col("innovation_ability"), lit("创新-"), col("practical_ability"), lit("实践-"), col("leadership_ability"), lit("领导")))
    
    pandas_df = final_profile.toPandas()
    result_data = pandas_df.to_dict('records')
    spark.stop()
    return JsonResponse({"status": "success", "data": result_data, "total": len(result_data)})

# 创业潜力挖掘分析核心处理函数
def startup_potential_mining(request):
    spark = SparkSession.builder.appName("StartupPotentialMining").getOrCreate()
    startup_df = spark.read.format("jdbc").option("url", "jdbc:mysql://localhost:3306/student_startup").option("dbtable", "startup_records").option("user", "root").option("password", "123456").load()
    
    student_df = spark.read.format("jdbc").option("url", "jdbc:mysql://localhost:3306/student_startup").option("dbtable", "student_info").option("user", "root").option("password", "123456").load()
    
    merged_data = student_df.join(startup_df, "student_id", "left_outer").fillna(0)
    
    potential_features = merged_data.withColumn("entrepreneurial_intention", when(col("startup_interest_score") > 7, 1).otherwise(0)).withColumn("resource_availability", (col("family_support_score") + col("financial_condition_score") + col("network_resources_score")) / 3).withColumn("capability_index", (col("gpa") * 0.4 + col("innovation_count") * 0.3 + col("leadership_experience") * 0.3))
    
    risk_assessment = potential_features.withColumn("market_risk_score", when(col("industry_competition_level") > 8, 0.8).when(col("industry_competition_level") > 5, 0.5).otherwise(0.2)).withColumn("personal_risk_score", when(col("risk_tolerance_score") < 4, 0.8).when(col("risk_tolerance_score") < 7, 0.5).otherwise(0.2))
    
    comprehensive_potential = risk_assessment.withColumn("startup_potential_score", (col("entrepreneurial_intention") * 0.25 + col("resource_availability") * 0.25 + col("capability_index") * 0.3 + (1 - col("market_risk_score")) * 0.1 + (1 - col("personal_risk_score")) * 0.1) * 100)
    
    potential_classification = comprehensive_potential.withColumn("potential_level", when(col("startup_potential_score") > 80, "高潜力").when(col("startup_potential_score") > 60, "中等潜力").when(col("startup_potential_score") > 40, "低潜力").otherwise("不建议创业"))
    
    recommendation_data = potential_classification.withColumn("recommendation", when(col("potential_level") == "高潜力", "建议积极准备创业，可申请创业基金支持").when(col("potential_level") == "中等潜力", "建议先积累经验，参与创业实践项目").when(col("potential_level") == "低潜力", "建议加强能力培养，提升创业素养").otherwise("建议专注学业，暂不考虑创业"))
    
    final_result = recommendation_data.select("student_id", "student_name", "startup_potential_score", "potential_level", "recommendation", "entrepreneurial_intention", "resource_availability", "capability_index").orderBy(desc("startup_potential_score"))
    
    pandas_result = final_result.toPandas()
    result_list = pandas_result.to_dict('records')
    spark.stop()
    return JsonResponse({"status": "success", "data": result_list, "message": "创业潜力挖掘分析完成"})

# 学生群体聚类分析核心处理函数
def student_clustering_analysis(request):
    spark = SparkSession.builder.appName("StudentClusteringAnalysis").getOrCreate()
    student_data = spark.read.format("jdbc").option("url", "jdbc:mysql://localhost:3306/student_startup").option("dbtable", "student_comprehensive_data").option("user", "root").option("password", "123456").load()
    
    feature_data = student_data.select("student_id", "gpa", "innovation_count", "competition_score", "internship_duration", "leadership_experience", "social_activity_count", "startup_interest_score", "risk_tolerance_score").filter(col("student_id").isNotNull())
    
    assembler = VectorAssembler(inputCols=["gpa", "innovation_count", "competition_score", "internship_duration", "leadership_experience", "social_activity_count", "startup_interest_score", "risk_tolerance_score"], outputCol="features")
    feature_vector = assembler.transform(feature_data)
    
    silhouette_scores = []
    for k in range(2, 8):
        kmeans = KMeans(k=k, seed=42, featuresCol="features", predictionCol="cluster")
        model = kmeans.fit(feature_vector)
        predictions = model.transform(feature_vector)
        evaluator = ClusteringEvaluator(featuresCol="features", predictionCol="cluster")
        silhouette = evaluator.evaluate(predictions)
        silhouette_scores.append((k, silhouette))
    
    optimal_k = max(silhouette_scores, key=lambda x: x[1])[0]
    final_kmeans = KMeans(k=optimal_k, seed=42, featuresCol="features", predictionCol="cluster")
    final_model = final_kmeans.fit(feature_vector)
    clustered_data = final_model.transform(feature_vector)
    
    cluster_summary = clustered_data.groupBy("cluster").agg(count("*").alias("student_count"), avg("gpa").alias("avg_gpa"), avg("innovation_count").alias("avg_innovation"), avg("startup_interest_score").alias("avg_startup_interest"), avg("risk_tolerance_score").alias("avg_risk_tolerance"))
    
    cluster_characteristics = cluster_summary.withColumn("cluster_type", when((col("avg_startup_interest") > 7) & (col("avg_innovation") > 2), "创新创业型").when((col("avg_gpa") > 3.5) & (col("avg_startup_interest") < 5), "学术专注型").when((col("avg_risk_tolerance") > 7) & (col("avg_startup_interest") > 6), "风险偏好型").when((col("avg_gpa") > 3.0) & (col("avg_innovation") < 2), "稳健发展型").otherwise("待发展型"))
    
    detailed_clusters = clustered_data.join(cluster_characteristics.select("cluster", "cluster_type"), "cluster")
    
    cluster_recommendations = detailed_clusters.withColumn("development_suggestion", when(col("cluster_type") == "创新创业型", "建议参与高层次创业比赛，申请创业基金支持").when(col("cluster_type") == "学术专注型", "建议专注学术研究，考虑技术转化创业").when(col("cluster_type") == "风险偏好型", "建议参与高风险高回报项目，加强市场分析能力").when(col("cluster_type") == "稳健发展型", "建议从小项目开始，积累创业经验").otherwise("建议加强基础能力培养，提升综合素质"))
    
    final_clustering_result = cluster_recommendations.select("student_id", "cluster", "cluster_type", "development_suggestion", "gpa", "innovation_count", "startup_interest_score").orderBy("cluster", desc("startup_interest_score"))
    
    pandas_clustering = final_clustering_result.toPandas()
    clustering_result = pandas_clustering.to_dict('records')
    spark.stop()
    return JsonResponse({"status": "success", "data": clustering_result, "clusters": optimal_k, "message": "学生群体聚类分析完成"})

基于大数据的学生创业数据分析系统文档展示

💖💖作者：计算机毕业设计江挽 💙💙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 深度学习实战项目