74 lines
3.2 KiB
Python
74 lines
3.2 KiB
Python
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import pandas as pd
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import numpy as np
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from faker import Faker
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# Initialize Faker for generating fake data
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fake = Faker()
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# Set random seed for reproducibility
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np.random.seed(42)
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# Generate synthetic data for small private universities
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private_universities = {
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'University': [f'Private University {i}' for i in range(1, 21)],
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'Funding from Tuition and Donations (%)': [100] * 20,
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'Board Size': np.random.randint(10, 51, size=20),
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'President Reports To': ['Board of Trustees'] * 20,
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'Enrollment': np.random.randint(1000, 20001, size=20),
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'Average Class Size': np.random.randint(10, 30, size=20),
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'Student-Faculty Ratio': np.round(np.random.uniform(8, 15, size=20), 1),
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'Endowment per Student ($)': np.random.randint(10000, 100000, size=20),
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'Retention Rate (%)': np.round(np.random.uniform(70, 95, size=20), 1)
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}
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# Generate synthetic data for public universities
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public_universities = {
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'University': [f'Public University {i}' for i in range(1, 21)],
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'Funding from State (%)': np.random.randint(20, 51, size=20),
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'Board Size': np.random.randint(9, 16, size=20),
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'President Reports To': ['State’s Regents'] * 20,
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'Enrollment': np.random.randint(20000, 70001, size=20),
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'State Funding Stability': np.random.choice(['Stable', 'Variable'], size=20),
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'Average Class Size': np.random.randint(30, 50, size=20),
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'Student-Faculty Ratio': np.round(np.random.uniform(15, 25, size=20), 1),
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'Endowment per Student ($)': np.random.randint(5000, 50000, size=20),
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'Retention Rate (%)': np.round(np.random.uniform(60, 85, size=20), 1)
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}
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# Generate synthetic data for enrollment management
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enrollment_management = {
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'Term': ['Fall', 'Spring', 'Summer'] * 20,
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'Cohort Size': np.random.randint(50, 500, size=60),
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'Financial Aid Packages': np.random.randint(5000, 25000, size=60),
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'Discount Rate (%)': np.random.uniform(30, 60, size=60),
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'First-time Freshmen (%)': np.random.uniform(40, 70, size=60),
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'Transfer Students (%)': np.random.uniform(10, 30, size=60),
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'Graduate Students (%)': np.random.uniform(5, 20, size=60),
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'Audit Students (%)': np.random.uniform(1, 5, size=60)
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}
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# Generate synthetic data for enrollment funnel
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enrollment_funnel = {
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'Stage': ['Lead', 'Prospect', 'Applied', 'Admitted', 'Deposited', 'Enrolled'],
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'Number of Students': np.random.randint(100, 5000, size=6),
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'Major': np.random.choice(['Science', 'Arts', 'Engineering', 'Business', 'Health Sciences'], size=6),
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'Average GPA': np.random.uniform(2.5, 4.0, size=6),
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'Average SAT Score': np.random.randint(900, 1600, size=6),
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'Average ACT Score': np.random.randint(18, 36, size=6),
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'FAFSA Submitted (%)': np.random.uniform(40, 90, size=6)
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}
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# Create DataFrames
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df_private = pd.DataFrame(private_universities)
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df_public = pd.DataFrame(public_universities)
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df_enrollment_management = pd.DataFrame(enrollment_management)
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df_enrollment_funnel = pd.DataFrame(enrollment_funnel)
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# Save to CSV files
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df_private.to_csv('private_universities.csv', index=False)
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df_public.to_csv('public_universities.csv', index=False)
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df_enrollment_management.to_csv('enrollment_management.csv', index=False)
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df_enrollment_funnel.to_csv('enrollment_funnel.csv', index=False)
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print("Synthetic data generated and saved to CSV files.")
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