前言
💖💖作者：计算机程序员小杨 💙💙个人简介：我是一名计算机相关专业的从业者，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。热爱技术，喜欢钻研新工具和框架，也乐于通过代码解决实际问题，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！ 💕💕文末获取源码联系 计算机程序员小杨 💜💜 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 计算机毕业设计选题 💜💜
一.开发工具简介
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
二.系统内容简介
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套专门针对教育领域学生流失预警的智能分析平台。该系统采用Hadoop分布式存储架构和Spark大数据处理引擎，能够对海量学生数据进行深度挖掘和实时分析，通过多维度因素建模识别潜在辍学风险学生。系统整合了学生背景信息、学业成绩表现、社会经济状况等多个数据源，运用Python数据科学库进行统计分析，结合Django后端框架提供稳定的API服务。前端采用Vue.js响应式框架配合ElementUI组件库，通过Echarts图表库实现丰富的数据可视化效果，为教育管理者提供直观的风险预警大屏。系统具备完整的用户管理、数据录入、风险评估、因素分析和可视化展示功能模块，支持实时数据处理和历史趋势分析，为教育决策提供科学的数据支撑。
三.系统功能演示
数据分析毕设推荐：学生辍学风险预测系统Vue+Echarts前端展示效果|毕设|计算机毕设|程序开发|项目实战
四.系统界面展示
五.系统源码展示
from pyspark.sql import SparkSession
from pyspark.sql.functions import col, when, count, avg, stddev, percentile_approx
from pyspark.ml.feature import VectorAssembler, StringIndexer
from pyspark.ml.classification import RandomForestClassifier
from pyspark.ml.evaluation import BinaryClassificationEvaluator
import pandas as pd
import numpy as np
from sklearn.preprocessing import StandardScaler
from sklearn.cluster import KMeans
import matplotlib.pyplot as plt
import seaborn as sns
spark = SparkSession.builder.appName("StudentDropoutRiskAnalysis").config("spark.sql.adaptive.enabled", "true").config("spark.sql.adaptive.coalescePartitions.enabled", "true").getOrCreate()
def analyze_student_background_factors():
student_df = spark.read.option("header", "true").option("inferSchema", "true").csv("hdfs://localhost:9000/student_data/background_info.csv")
risk_scores = student_df.withColumn("family_risk_score",
when(col("family_income") < 30000, 5)
.when(col("family_income") < 50000, 3)
.otherwise(1))
risk_scores = risk_scores.withColumn("education_risk_score",
when(col("parent_education") == "primary", 4)
.when(col("parent_education") == "secondary", 2)
.otherwise(0))
risk_scores = risk_scores.withColumn("location_risk_score",
when(col("hometown_type") == "rural", 3)
.when(col("hometown_type") == "urban", 1)
.otherwise(2))
risk_scores = risk_scores.withColumn("total_background_risk",
col("family_risk_score") + col("education_risk_score") + col("location_risk_score"))
background_stats = risk_scores.groupBy("student_grade", "major").agg(
avg("total_background_risk").alias("avg_risk_score"),
count("student_id").alias("student_count"),
stddev("total_background_risk").alias("risk_stddev"))
high_risk_students = risk_scores.filter(col("total_background_risk") > 8)
risk_distribution = risk_scores.groupBy("total_background_risk").count().orderBy("total_background_risk")
correlation_analysis = risk_scores.select("family_income", "parent_education_years", "total_background_risk")
result_data = {
'background_stats': background_stats.toPandas(),
'high_risk_count': high_risk_students.count(),
'risk_distribution': risk_distribution.toPandas(),
'total_students': student_df.count()
}
return result_data
def analyze_academic_performance():
performance_df = spark.read.option("header", "true").option("inferSchema", "true").csv("hdfs://localhost:9000/student_data/academic_records.csv")
gpa_analysis = performance_df.withColumn("gpa_risk_level",
when(col("cumulative_gpa") < 2.0, "high_risk")
.when(col("cumulative_gpa") < 2.5, "medium_risk")
.otherwise("low_risk"))
course_failure_stats = performance_df.withColumn("failure_count",
when(col("failed_courses") == 0, 0)
.when(col("failed_courses") <= 2, 1)
.when(col("failed_courses") <= 4, 2)
.otherwise(3))
attendance_risk = performance_df.withColumn("attendance_risk",
when(col("attendance_rate") < 0.7, 4)
.when(col("attendance_rate") < 0.8, 3)
.when(col("attendance_rate") < 0.9, 2)
.otherwise(1))
academic_risk_scores = attendance_risk.withColumn("academic_risk_total",
col("failure_count") * 2 + col("attendance_risk") +
when(col("cumulative_gpa") < 2.0, 5)
.when(col("cumulative_gpa") < 2.5, 3)
.otherwise(0))
performance_trends = academic_risk_scores.groupBy("semester", "major").agg(
avg("cumulative_gpa").alias("avg_gpa"),
avg("attendance_rate").alias("avg_attendance"),
count("student_id").alias("student_count"))
critical_students = academic_risk_scores.filter(
(col("academic_risk_total") >= 8) |
(col("cumulative_gpa") < 2.0) |
(col("attendance_rate") < 0.7))
grade_distribution = academic_risk_scores.groupBy("gpa_risk_level").agg(
count("student_id").alias("count"),
avg("academic_risk_total").alias("avg_risk_score"))
result_data = {
'performance_trends': performance_trends.toPandas(),
'critical_students': critical_students.toPandas(),
'grade_distribution': grade_distribution.toPandas(),
'risk_summary': academic_risk_scores.describe().toPandas()
}
return result_data
def analyze_key_dropout_factors():
combined_df = spark.read.option("header", "true").option("inferSchema", "true").csv("hdfs://localhost:9000/student_data/combined_student_data.csv")
feature_cols = ["gpa", "attendance_rate", "family_income", "failed_courses", "social_activities", "part_time_work_hours"]
assembler = VectorAssembler(inputCols=feature_cols, outputCol="features")
feature_data = assembler.transform(combined_df)
indexer = StringIndexer(inputCol="dropout_status", outputCol="label")
indexed_data = indexer.fit(feature_data).transform(feature_data)
train_data, test_data = indexed_data.randomSplit([0.8, 0.2], seed=42)
rf_classifier = RandomForestClassifier(featuresCol="features", labelCol="label", numTrees=100, maxDepth=10)
rf_model = rf_classifier.fit(train_data)
predictions = rf_model.transform(test_data)
evaluator = BinaryClassificationEvaluator(labelCol="label", rawPredictionCol="rawPrediction")
accuracy = evaluator.evaluate(predictions)
feature_importance = rf_model.featureImportances.toArray()
importance_df = pd.DataFrame({
'feature': feature_cols,
'importance': feature_importance
}).sort_values('importance', ascending=False)
high_risk_prediction = rf_model.transform(feature_data).filter(col("prediction") == 1.0)
risk_factors_analysis = combined_df.groupBy("major", "grade_level").agg(
avg("gpa").alias("avg_gpa"),
avg("attendance_rate").alias("avg_attendance"),
count(when(col("dropout_status") == "yes", 1)).alias("dropout_count"),
count("student_id").alias("total_count"))
dropout_rate_by_factor = risk_factors_analysis.withColumn("dropout_rate",
col("dropout_count") / col("total_count"))
correlation_matrix = combined_df.select(feature_cols).toPandas().corr()
result_data = {
'model_accuracy': accuracy,
'feature_importance': importance_df,
'high_risk_students': high_risk_prediction.count(),
'dropout_rate_analysis': dropout_rate_by_factor.toPandas(),
'correlation_matrix': correlation_matrix,
'predictions_sample': predictions.select("student_id", "prediction", "probability").limit(100).toPandas()
}
return result_data
六.系统文档展示
结束
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