QuantPath

MFE admission prediction and program ranking — data-driven school selection for Master of Financial Engineering programs.

Data

• 6,984 admission records from GradCafe and QuantNet (accepted / rejected / waitlisted)
• 28 MFE programs with prerequisites, deadlines, acceptance rates, salary outcomes
• 21 trained logistic regression models (one per program with $\geq 30$ samples)

Methodology

Admission prediction: per-program logistic regression on GPA + GRE Quant with bias correction. Raw training data has survivor bias (self-reported), so the model replaces the biased intercept with $\text{logit}(r)$ where $r$ is the official acceptance rate, preserving learned feature slopes.

Profile evaluation: 5-dimension weighted scoring (Math 30%, Statistics 20%, CS 20%, Finance/Econ 15%, GPA 15%) across 36 sub-factors extracted from coursework. Flags gaps ($< 6.0$) and strengths ($\geq 9.0$).

School ranking: reach / target / safety classification using LR probability thresholds (reach $< 40%$, target $40$–$70%$, safety $\geq 70%$) with a 100-point composite fit score (GPA closeness, prerequisite match, acceptance feasibility, academic profile).

Usage

pip install -e .

# Evaluate profile across 5 dimensions
quantpath evaluate --profile profiles/my_profile.yaml

# Rank 28 programs as reach / target / safety
quantpath rank --profile profiles/my_profile.yaml

# Build optimized application list (2-4 reach, 3-4 target, 1-2 safety)
quantpath list --profile profiles/my_profile.yaml

# Check prerequisite match for a specific program
quantpath match --profile profiles/my_profile.yaml --program baruch-mfe

License

MIT