A program to allocate courses to student as per their preferences and availability.

allocateCourse.py

#This will left many unallocated student as there is no upper cap defiend for cce student on CSE and ECE courses.

import numpy as np

studentPreferenceFile = open("generated.csv", "r")


#creating objects of student choice
studentPreferenceFileData = studentPreferenceFile.readlines()

studentPreference = [] #of objects

for line in studentPreferenceFileData:
	line = line.strip()
	studentDataArray = line.split(",")
	studentDataObject = {"rollNumber": studentDataArray[0], "branch": studentDataArray[1], "courseCount": int(studentDataArray[2]) ,"preferrence" : studentDataArray[3:], "alloted" : []}
	studentPreference.append(studentDataObject)



#creating objects of course seat availability
courseAvailabilityFile = open("course.csv", "r")
courseAvailabilityFileData = courseAvailabilityFile.readlines()

courseAvailability = {}

for line in courseAvailabilityFileData:
	line = line.strip()
	courseDataArray = line.split(",")
	courseAvailability[courseDataArray[0]] = {"name": courseDataArray[1], "seats": int(courseDataArray[2])}


allotmentList = []

file = open("allotmentList.csv", "w")

for x in xrange(0,8):

	np.random.shuffle(studentPreference)

	for student in studentPreference:
		# print "coursecount: " + str(student["courseCount"])
		# print "x: " + str(x)
		# print len(student["preferrence"])
		# print "--------------------"
		if student["courseCount"] > 0 and len(student["preferrence"]) >= (x+1) and courseAvailability[student["preferrence"][x]]["seats"] > 0:
			# print courseAvailability[student["preferrence"][x]]["seats"]
			# print "x-----x"
			courseAvailability[student["preferrence"][x]]["seats"] -= 1
			student["alloted"].append(student["preferrence"][x])
			student["courseCount"] -= 1
			if student["courseCount"] == 0:
				allotmentList.append(student)
				studentPreference.remove(student)

print len(studentPreference)

A course allocation program that assigns students to courses based on their preferences and availability would greatly enhance the education system. By considering students' interests and scheduling constraints, such a system can optimize learning experiences and reduce conflicts. For example, integrating online resources like https://learnerstv.org/Free-Chemistry-video-lecture-courses.htm can supplement course materials and provide additional learning opportunities. Implementing AI-driven algorithms can ensure fair distribution, minimize scheduling conflicts, and improve overall student satisfaction. This approach promotes efficiency and personalized learning, ultimately benefiting both students and educational institutions.