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基于大数据的学生辍学风险因素数据分析与可视化系统-功能介绍

基于大数据的学生辍学风险因素数据分析与可视化系统是一个运用Hadoop+Spark大数据技术栈构建的教育数据分析平台。该系统采用Python作为主要开发语言，后端框架使用Django，前端通过Vue+ElementUI+Echarts实现数据可视化展示。系统核心功能围绕学生辍学风险预测展开，通过Spark SQL对海量学生数据进行深度挖掘，从学生人口统计学特征、学业背景、在校表现、财务状况以及综合风险因素等五个维度进行全方位分析。系统运用Pandas和NumPy进行数据预处理，利用HDFS进行分布式存储，通过机器学习算法识别辍学关键影响因素，生成多维度分析报告和可视化图表。平台能够实现学生群体画像聚类分析、风险因素重要性排序、成绩变化趋势监测等功能，为教育管理者提供科学的决策支持工具，帮助及早发现潜在辍学风险学生并制定针对性干预措施。

基于大数据的学生辍学风险因素数据分析与可视化系统-选题背景意义

选题背景
随着高等教育规模不断扩大，学生辍学问题逐渐成为各高校关注的重点。传统的学生管理方式主要依靠人工统计和经验判断，难以准确识别辍学风险因素，缺乏科学的预警机制。当前教育信息化进程加速，各高校积累了大量学生数据，包括学业成绩、出勤记录、家庭背景、经济状况等多维信息，但这些数据往往分散存储，未能充分发挥其价值。大数据技术的快速发展为教育数据分析提供了新的解决思路，Hadoop和Spark等分布式计算框架能够高效处理海量教育数据，机器学习算法可以从复杂数据中挖掘隐含规律。在此背景下，构建一个基于大数据技术的学生辍学风险分析系统，能够帮助教育管理者更好地理解学生群体特征，识别影响学生学业完成的关键因素，为制定个性化教育策略提供数据支撑。
选题意义
本课题的实际意义体现在多个方面。对高校管理层而言，系统能够提供量化的辍学风险评估工具，帮助及早发现有辍学倾向的学生，通过数据驱动的方式优化资源配置和管理策略。对学生工作者来说，多维度的风险因素分析有助于理解不同学生群体的特点和需求，制定更有针对性的帮扶措施，提高学生管理工作的精准度和有效性。从技术角度看，该系统展示了大数据技术在教育领域的应用潜力，为相关技术研究提供了实践案例。对学生个体而言，虽然这只是一个毕业设计项目，但其分析结果可以为学生自我认知和学业规划提供一定参考。同时，本课题作为大数据技术与教育场景结合的探索，能够为后续相关研究提供思路和方法借鉴。当然，作为毕业设计，其主要价值还是在于技术实现和学术训练，为个人专业能力提升奠定基础。

基于大数据的学生辍学风险因素数据分析与可视化系统-技术选型

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

基于大数据的学生辍学风险因素数据分析与可视化系统-视频展示

基于大数据的学生辍学风险因素数据分析与可视化系统-图片展示

基于大数据的学生辍学风险因素数据分析与可视化系统-代码展示

from pyspark.sql import SparkSession
from pyspark.ml.feature import VectorAssembler, StringIndexer
from pyspark.ml.classification import DecisionTreeClassifier, RandomForestClassifier
from pyspark.ml.evaluation import MulticlassClassificationEvaluator
from pyspark.sql.functions import col, when, count, avg, desc, asc
from pyspark.ml.stat import Correlation
import pandas as pd
import numpy as np

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

def analyze_gender_academic_status(df):
    gender_analysis = df.groupBy("Gender", "Target").count().withColumnRenamed("count", "student_count")
    gender_pivot = gender_analysis.groupBy("Gender").pivot("Target").sum("student_count").fillna(0)
    gender_with_total = gender_pivot.withColumn("total_students", col("Dropout") + col("Graduate") + col("Enrolled"))
    gender_with_rates = gender_with_total.withColumn("dropout_rate", (col("Dropout") / col("total_students")) * 100).withColumn("graduate_rate", (col("Graduate") / col("total_students")) * 100).withColumn("enrolled_rate", (col("Enrolled") / col("total_students")) * 100)
    result_df = gender_with_rates.select("Gender", "Dropout", "Graduate", "Enrolled", "total_students", "dropout_rate", "graduate_rate", "enrolled_rate")
    pandas_result = result_df.toPandas()
    analysis_summary = {}
    for row in pandas_result.itertuples():
        gender = row.Gender
        analysis_summary[gender] = {"dropout_count": int(row.Dropout), "graduate_count": int(row.Graduate), "enrolled_count": int(row.Enrolled), "total_count": int(row.total_students), "dropout_percentage": round(row.dropout_rate, 2), "graduate_percentage": round(row.graduate_rate, 2), "enrolled_percentage": round(row.enrolled_rate, 2)}
    return analysis_summary

def predict_dropout_risk_factors(df):
    categorical_cols = ["Gender", "Marital status", "Application mode", "Course", "Previous qualification", "Mother's qualification", "Father's qualification", "Mother's occupation", "Father's occupation", "Daytime/evening attendance", "Debtor", "Tuition fees up to date", "Scholarship holder"]
    numerical_cols = ["Age at enrollment", "Curricular units 1st sem (enrolled)", "Curricular units 1st sem (approved)", "Curricular units 1st sem (grade)", "Curricular units 1st sem (without evaluations)", "Curricular units 2nd sem (enrolled)", "Curricular units 2nd sem (approved)", "Curricular units 2nd sem (grade)", "Curricular units 2nd sem (without evaluations)", "Unemployment rate", "Inflation rate", "GDP"]
    indexers = [StringIndexer(inputCol=col, outputCol=col+"_indexed", handleInvalid="keep") for col in categorical_cols]
    indexed_df = df
    for indexer in indexers:
        indexed_df = indexer.fit(indexed_df).transform(indexed_df)
    target_indexer = StringIndexer(inputCol="Target", outputCol="Target_indexed", handleInvalid="keep")
    indexed_df = target_indexer.fit(indexed_df).transform(indexed_df)
    feature_cols = [col+"_indexed" for col in categorical_cols] + numerical_cols
    assembler = VectorAssembler(inputCols=feature_cols, outputCol="features")
    feature_df = assembler.transform(indexed_df)
    train_data, test_data = feature_df.randomSplit([0.8, 0.2], seed=42)
    rf_classifier = RandomForestClassifier(featuresCol="features", labelCol="Target_indexed", numTrees=100, seed=42)
    rf_model = rf_classifier.fit(train_data)
    feature_importance = rf_model.featureImportances.toArray()
    importance_dict = {}
    for i, importance in enumerate(feature_importance):
        feature_name = feature_cols[i]
        importance_dict[feature_name] = float(importance)
    sorted_importance = sorted(importance_dict.items(), key=lambda x: x[1], reverse=True)
    top_10_features = sorted_importance[:10]
    predictions = rf_model.transform(test_data)
    evaluator = MulticlassClassificationEvaluator(labelCol="Target_indexed", predictionCol="prediction", metricName="accuracy")
    accuracy = evaluator.evaluate(predictions)
    return {"top_risk_factors": top_10_features, "model_accuracy": round(accuracy, 4), "total_features": len(feature_cols)}

def analyze_academic_performance_trends(df):
    academic_df = df.select("Curricular units 1st sem (grade)", "Curricular units 2nd sem (grade)", "Target").filter((col("Curricular units 1st sem (grade)") > 0) & (col("Curricular units 2nd sem (grade)") > 0))
    academic_with_change = academic_df.withColumn("grade_change", col("Curricular units 2nd sem (grade)") - col("Curricular units 1st sem (grade)"))
    academic_with_trend = academic_with_change.withColumn("trend_category", when(col("grade_change") > 2, "Significant Improvement").when(col("grade_change") > 0, "Slight Improvement").when(col("grade_change") == 0, "No Change").when(col("grade_change") > -2, "Slight Decline").otherwise("Significant Decline"))
    trend_analysis = academic_with_trend.groupBy("trend_category", "Target").count().withColumnRenamed("count", "student_count")
    trend_pivot = trend_analysis.groupBy("trend_category").pivot("Target").sum("student_count").fillna(0)
    trend_with_total = trend_pivot.withColumn("total_students", col("Dropout") + col("Graduate") + col("Enrolled"))
    trend_with_rates = trend_with_total.withColumn("dropout_rate", (col("Dropout") / col("total_students")) * 100)
    avg_grades_by_target = academic_df.groupBy("Target").agg(avg("Curricular units 1st sem (grade)").alias("avg_1st_sem"), avg("Curricular units 2nd sem (grade)").alias("avg_2nd_sem"))
    correlation_matrix = Correlation.corr(academic_df.select("Curricular units 1st sem (grade)", "Curricular units 2nd sem (grade)")).head()
    correlation_value = correlation_matrix[0].toArray()[0][1]
    trend_result = trend_with_rates.select("trend_category", "Dropout", "Graduate", "Enrolled", "total_students", "dropout_rate").toPandas()
    avg_result = avg_grades_by_target.toPandas()
    performance_summary = {"trend_analysis": {}, "average_grades": {}, "grade_correlation": round(correlation_value, 4)}
    for row in trend_result.itertuples():
        category = row.trend_category
        performance_summary["trend_analysis"][category] = {"dropout_count": int(row.Dropout), "graduate_count": int(row.Graduate), "enrolled_count": int(row.Enrolled), "total_count": int(row.total_students), "dropout_rate": round(row.dropout_rate, 2)}
    for row in avg_result.itertuples():
        target = row.Target
        performance_summary["average_grades"][target] = {"avg_first_semester": round(row.avg_1st_sem, 2), "avg_second_semester": round(row.avg_2nd_sem, 2)}
    return performance_summary

基于大数据的学生辍学风险因素数据分析与可视化系统-结语

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