💖💖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
基于数据挖掘的高考志愿推荐系统的设计与实现介绍
高考志愿推荐系统是一个基于大数据技术的智能化教育辅助平台，采用Hadoop分布式存储架构和Spark内存计算引擎作为核心技术支撑。系统通过收集和分析海量的高校信息、专业数据、历年录取分数线等多维度数据，为高考学生提供科学合理的志愿填报建议。平台集成了学生信息管理、高校信息查询、专业信息浏览、志愿智能推荐、分数预测分析等核心功能模块，形成了完整的志愿填报生态系统。技术架构上采用前后端分离设计，前端使用Vue+ElementUI构建用户界面，结合Echarts实现数据可视化展示，后端基于Django框架提供RESTful API服务，数据存储采用MySQL关系型数据库。系统的核心亮点在于运用Spark SQL进行大规模数据处理和机器学习算法实现智能推荐，通过Pandas和NumPy进行数据清洗和统计分析，能够根据学生的分数情况、兴趣偏好、地域要求等因素，结合历年录取数据和专业就业前景，为每位用户量身定制最优的志愿填报方案，有效提升了志愿填报的科学性和准确性。
基于数据挖掘的高考志愿推荐系统的设计与实现演示视频
基于数据挖掘的高考志愿推荐系统的设计与实现演示图片
基于数据挖掘的高考志愿推荐系统的设计与实现代码展示
from pyspark.sql import SparkSession
from pyspark.sql.functions import col, avg, count, desc, when, regexp_replace
from pyspark.ml.feature import VectorAssembler
from pyspark.ml.regression import LinearRegression
from django.http import JsonResponse
import pandas as pd
import numpy as np
spark = SparkSession.builder.appName("GaoKaoVolunteerRecommendation").config("spark.sql.adaptive.enabled", "true").config("spark.sql.adaptive.coalescePartitions.enabled", "true").getOrCreate()
def score_prediction_analysis(request):
student_id = request.GET.get('student_id')
target_score = float(request.GET.get('target_score', 0))
province = request.GET.get('province', '')
subject_type = request.GET.get('subject_type', '')
historical_data = spark.read.format("jdbc").option("url", "jdbc:mysql://localhost:3306/gaokao_db").option("dbtable", "historical_scores").option("user", "root").option("password", "password").load()
filtered_data = historical_data.filter((col("province") == province) & (col("subject_type") == subject_type))
score_stats = filtered_data.groupBy("university_name", "major_name").agg(avg("admission_score").alias("avg_score"), count("*").alias("record_count")).filter(col("record_count") >= 3)
probability_data = score_stats.withColumn("admission_probability", when(col("avg_score") <= target_score - 30, 0.9).when(col("avg_score") <= target_score - 15, 0.7).when(col("avg_score") <= target_score, 0.5).when(col("avg_score") <= target_score + 15, 0.3).otherwise(0.1))
safe_choices = probability_data.filter(col("admission_probability") >= 0.7).orderBy(desc("avg_score")).limit(10)
moderate_choices = probability_data.filter((col("admission_probability") >= 0.4) & (col("admission_probability") < 0.7)).orderBy(desc("avg_score")).limit(10)
reach_choices = probability_data.filter(col("admission_probability") < 0.4).orderBy(desc("avg_score")).limit(5)
feature_assembler = VectorAssembler(inputCols=["avg_score", "record_count"], outputCol="features")
ml_data = feature_assembler.transform(score_stats).select("features", col("avg_score").alias("label"))
lr_model = LinearRegression(featuresCol="features", labelCol="label")
trained_model = lr_model.fit(ml_data)
predictions = trained_model.transform(ml_data)
result_data = {"safe_choices": safe_choices.toPandas().to_dict('records'), "moderate_choices": moderate_choices.toPandas().to_dict('records'), "reach_choices": reach_choices.toPandas().to_dict('records'), "prediction_accuracy": trained_model.summary.r2}
return JsonResponse(result_data)
def intelligent_volunteer_recommendation(request):
student_id = request.GET.get('student_id')
estimated_score = float(request.GET.get('estimated_score', 0))
preferred_city = request.GET.get('preferred_city', '')
preferred_major_category = request.GET.get('preferred_major_category', '')
university_data = spark.read.format("jdbc").option("url", "jdbc:mysql://localhost:3306/gaokao_db").option("dbtable", "university_info").option("user", "root").option("password", "password").load()
major_data = spark.read.format("jdbc").option("url", "jdbc:mysql://localhost:3306/gaokao_db").option("dbtable", "major_info").option("user", "root").option("password", "password").load()
admission_data = spark.read.format("jdbc").option("url", "jdbc:mysql://localhost:3306/gaokao_db").option("dbtable", "admission_records").option("user", "root").option("password", "password").load()
combined_data = university_data.join(major_data, university_data.university_id == major_data.university_id, "inner").join(admission_data, (university_data.university_id == admission_data.university_id) & (major_data.major_id == admission_data.major_id), "inner")
filtered_recommendations = combined_data.filter((col("university_city").contains(preferred_city) if preferred_city else col("university_city").isNotNull()) & (col("major_category").contains(preferred_major_category) if preferred_major_category else col("major_category").isNotNull()))
score_based_filter = filtered_recommendations.filter((col("min_admission_score") <= estimated_score + 20) & (col("min_admission_score") >= estimated_score - 50))
weighted_scores = score_based_filter.withColumn("recommendation_score", (col("university_ranking") * 0.3 + col("major_employment_rate") * 0.4 + col("university_reputation") * 0.3))
prioritized_recommendations = weighted_scores.withColumn("risk_level", when(col("min_admission_score") <= estimated_score - 20, "安全").when(col("min_admission_score") <= estimated_score - 5, "稳妥").when(col("min_admission_score") <= estimated_score + 10, "冲刺").otherwise("高风险"))
final_recommendations = prioritized_recommendations.select("university_name", "major_name", "min_admission_score", "recommendation_score", "risk_level", "university_city", "major_employment_rate").orderBy(desc("recommendation_score")).limit(20)
risk_distribution = final_recommendations.groupBy("risk_level").count().toPandas()
compatibility_analysis = final_recommendations.withColumn("compatibility_score", when(col("risk_level") == "安全", 85).when(col("risk_level") == "稳妥", 75).when(col("risk_level") == "冲刺", 60).otherwise(40))
result_recommendations = {"recommendations": final_recommendations.toPandas().to_dict('records'), "risk_distribution": risk_distribution.to_dict('records'), "total_matches": final_recommendations.count()}
return JsonResponse(result_recommendations)
def university_major_data_analysis(request):
analysis_type = request.GET.get('analysis_type', 'overview')
target_province = request.GET.get('province', '')
university_level = request.GET.get('university_level', '')
university_df = spark.read.format("jdbc").option("url", "jdbc:mysql://localhost:3306/gaokao_db").option("dbtable", "university_comprehensive").option("user", "root").option("password", "password").load()
major_df = spark.read.format("jdbc").option("url", "jdbc:mysql://localhost:3306/gaokao_db").option("dbtable", "major_comprehensive").option("user", "root").option("password", "password").load()
enrollment_df = spark.read.format("jdbc").option("url", "jdbc:mysql://localhost:3306/gaokao_db").option("dbtable", "enrollment_statistics").option("user", "root").option("password", "password").load()
comprehensive_data = university_df.join(major_df, university_df.university_id == major_df.university_id, "inner").join(enrollment_df, (university_df.university_id == enrollment_df.university_id) & (major_df.major_id == enrollment_df.major_id), "left")
province_filtered = comprehensive_data.filter(col("university_province") == target_province if target_province else col("university_province").isNotNull())
level_filtered = province_filtered.filter(col("university_type").contains(university_level) if university_level else col("university_type").isNotNull())
university_statistics = level_filtered.groupBy("university_name", "university_type").agg(count("major_id").alias("major_count"), avg("average_employment_rate").alias("avg_employment"), avg("average_salary").alias("avg_salary"))
major_popularity = level_filtered.groupBy("major_category", "major_name").agg(count("university_id").alias("offering_universities"), avg("enrollment_quota").alias("avg_quota"), avg("competition_ratio").alias("avg_competition"))
trending_analysis = major_popularity.withColumn("popularity_score", col("offering_universities") * 0.4 + col("avg_quota") * 0.3 + col("avg_competition") * 0.3).orderBy(desc("popularity_score"))
employment_prospects = level_filtered.groupBy("major_category").agg(avg("average_employment_rate").alias("category_employment"), avg("average_salary").alias("category_salary"), count("*").alias("sample_size")).filter(col("sample_size") >= 5)
regional_distribution = level_filtered.groupBy("university_province", "university_type").agg(count("university_id").alias("university_count"), avg("university_ranking").alias("avg_ranking"))
comprehensive_insights = {"university_overview": university_statistics.toPandas().to_dict('records'), "major_trends": trending_analysis.limit(15).toPandas().to_dict('records'), "employment_analysis": employment_prospects.toPandas().to_dict('records'), "regional_stats": regional_distribution.toPandas().to_dict('records')}
return JsonResponse(comprehensive_insights)
基于数据挖掘的高考志愿推荐系统的设计与实现文档展示
💖💖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
