73 lines
2.9 KiB
Python
73 lines
2.9 KiB
Python
import pandas as pd
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import numpy as np
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# Set random seed for reproducibility
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np.random.seed(42)
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# Define probabilities for each stage transition
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stage_probabilities = {
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'Lead': 0.25,
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'Prospect': 0.20,
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'Applied': 0.25,
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'Admitted': 0.24,
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'Deposited': 0.05,
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'Enrolled': 0.01
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}
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# Ensure the probabilities sum to 1 for initial stage assignment
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total_probability = sum(stage_probabilities.values())
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stage_probabilities_normalized = {k: v / total_probability for k, v in stage_probabilities.items()}
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# Generate synthetic data for enrollment funnel with student IDs
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num_students = 30000
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stages = ['Lead', 'Prospect', 'Applied', 'Admitted', 'Deposited', 'Enrolled']
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majors = ['Science', 'Arts', 'Engineering', 'Business', 'Health Sciences']
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enrollment_funnel = {
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'Student_ID': [f'S{1000+i}' for i in range(num_students)],
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'Stage': np.random.choice(list(stage_probabilities_normalized.keys()), size=num_students,
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p=list(stage_probabilities_normalized.values())),
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'Major': np.random.choice(majors, size=num_students),
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'Average GPA': np.random.uniform(2.5, 4.0, size=num_students),
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'Average SAT Score': np.random.randint(900, 1600, size=num_students),
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'Average ACT Score': np.random.randint(18, 36, size=num_students),
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'FAFSA Submitted (%)': np.random.uniform(40, 90, size=num_students)
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}
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# Create DataFrame
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df_enrollment_funnel = pd.DataFrame(enrollment_funnel)
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# Function to simulate student progression through stages
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def simulate_progression(df):
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progression = df.copy()
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for stage in stages[1:]:
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previous_stage = stages[stages.index(stage) - 1]
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transition_prob = stage_probabilities.get(stage, 0.5)
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in_previous_stage = progression['Stage'] == previous_stage
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progressed = in_previous_stage & (np.random.rand(len(progression)) < transition_prob)
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progression.loc[progressed, 'Stage'] = stage
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return progression
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# Simulate student progression through stages
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df_enrollment_funnel = simulate_progression(df_enrollment_funnel)
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# Filter students who reached the 'Deposited' stage but did not enroll
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deposited_students = df_enrollment_funnel[df_enrollment_funnel['Stage'] == 'Deposited']
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enrolled_students = df_enrollment_funnel[df_enrollment_funnel['Stage'] == 'Enrolled']
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# Identify melted students (Deposited but not Enrolled)
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melted_students = deposited_students[~deposited_students['Student_ID'].isin(enrolled_students['Student_ID'])]
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# Calculate melt rate
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total_deposited = len(deposited_students)
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total_melted = len(melted_students)
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melt_rate = (total_melted / total_deposited) * 100 if total_deposited > 0 else 0
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print(f"Total Deposited Students: {total_deposited}")
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print(f"Total Melted Students: {total_melted}")
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print(f"Melt Rate: {melt_rate:.2f}%")
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# Save melted students to CSV for further analysis
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melted_students.to_csv('melted_students.csv', index=False)
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print("Melt rate calculated and melted students identified.")
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