66 lines
2.3 KiB
Python
66 lines
2.3 KiB
Python
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import random
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import pandas as pd
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from faker import Faker
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fake = Faker()
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def generate_student_data(num_students):
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data = []
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for _ in range(num_students):
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# Generate basic student data
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student_id = fake.unique.uuid4()
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student_type = random.choice(['First-time Freshmen', 'Transfer Student', 'Graduate Student', 'Audit Student'])
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major = fake.word().capitalize()
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gpa = round(random.uniform(2.0, 4.0), 2)
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sat_score = random.randint(800, 1600)
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act_score = random.randint(1, 36)
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state = fake.state()
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zip_code = fake.zipcode()
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country = fake.country()
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first_generation = random.choice([True, False])
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need_rank = random.randint(1, 6)
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efc = random.randint(0, 100000) # Expected Family Contribution
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financial_aid_package = random.randint(1000, 50000) # Financial aid package amount
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# Published tuition price for calculation of discount rate
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published_tuition_price = random.randint(30000, 70000)
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discount_rate = round((financial_aid_package / published_tuition_price) * 100, 2)
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# Enrollment funnel stages
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funnel_stage = random.choice(['Lead', 'Prospect', 'Applied', 'Admitted', 'Waitlisted', 'Denied', 'Deposited', 'Enrolled'])
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# Generate synthetic data point
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student_data = {
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'Student ID': student_id,
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'Student Type': student_type,
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'Major': major,
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'GPA': gpa,
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'SAT Score': sat_score,
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'ACT Score': act_score,
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'State': state,
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'Zip Code': zip_code,
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'Country': country,
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'First Generation': first_generation,
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'Need Rank': need_rank,
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'EFC': efc,
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'Financial Aid Package': financial_aid_package,
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'Published Tuition Price': published_tuition_price,
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'Discount Rate': discount_rate,
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'Funnel Stage': funnel_stage
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}
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data.append(student_data)
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return pd.DataFrame(data)
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# Generate synthetic data for 1000 students
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num_students = 1000
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student_df = generate_student_data(num_students)
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# Save the data to a CSV file
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student_df.to_csv('synthetic_student_data.csv', index=False)
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# Display the first few rows of the generated data
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print(student_df.head())
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