README.md

💕 Relationship Longevity Predictor

An ensemble ML model that predicts relationship compatibility and longevity based on personal and professional profiles of two individuals.

This is a predictability engine — not a matchmaking system. Given two individuals' personal attributes, values, interests, and personality traits, it predicts how likely their relationship is to succeed.

Model Architecture

Ensemble of 3 gradient-boosted tree models with 113 engineered dyadic features:

Model	Weight	AUC-ROC	F1
XGBoost	0.40	0.8852	0.6013
LightGBM	0.35	0.8912	0.6351
CatBoost	0.25	0.8661	0.5974
Ensemble	—	0.8842	0.6198

Best single model: LightGBM (AUC-ROC = 0.891, F1 = 0.635)

Performance Metrics (5-Fold Cross-Validation)

Metric	Value
AUC-ROC	0.891
AUC-PR	0.699
Accuracy	85.3%
F1 Score	0.635
Precision	56.8%
Recall	72.0%
Brier Score	0.101

What the Model Learns

Top Predictive Features (from SHAP analysis)

1. Attractiveness Perception Product — Mutual physical attraction between partners
2. Probability Partner Wants to Date — Perceived reciprocal interest
3. Humor Perception Product — Shared sense of humor (mutual ratings)
4. Total Self-Awareness Gap — How accurately people perceive themselves vs how partners see them
5. Interest Correlation — Overlap in hobbies and interests
6. Shared Interests Score — Partner's rating of shared interests
7. Interest Diversity — Breadth of the dater's interests
8. Confidence Calibration — How well people predict their own attractiveness to others
9. Intelligence Value Fulfillment — Whether the partner meets intelligence expectations
10. Expectation Meets Reality — Gap between expected and actual satisfaction

Key Insights

• Mutual attraction matters most — but it's the product (both people finding each other attractive) that predicts success, not just one-sided attraction
• Humor compatibility ranks #3 — couples who both rate each other as funny are much more likely to match
• Self-awareness is a strong predictor — people who accurately assess how others see them tend to form better partnerships
• Shared interests matter significantly — the correlation between interests is more predictive than any single interest
• Value alignment (what you care about vs what your partner delivers) drives long-term compatibility

Feature Engineering

113 features in 10 categories, engineered from raw dyadic profiles:

Category	Features	Description
Perception Gap	5	How you rate your partner vs how they rate you (per trait)
Mutual Scores	5	Average of both partners' ratings (per trait)
Perception Products	5	Multiplicative interaction of mutual ratings
Value Fulfillment	5	Does your partner deliver what you value most?
Self-Awareness	5	Self-perception vs partner perception gap
Age Features	4	Gap, gap², is_older, combined age
Interest Features	5	Diversity, intensity, range, correlation
Importance Alignment	8	Do both people value the same traits?
Expectation Features	2	Expectation calibration, meets-reality score
Demographics	4	Race match, gender, same race importance
Raw Profiles	~65	Original personality ratings, interests, preferences

Training Data

Fisman Speed Dating Experiment (mstz/speeddating)

• 1,048 speed-dating encounters between participants
• Columbia Business School, 2002-2004
• 17.7% positive match rate (class imbalance handled via scale_pos_weight + balanced weighting)

Usage

import joblib
import json
import numpy as np
from catboost import CatBoostClassifier

# Load models
xgb = joblib.load("xgboost_model.joblib")
lgb = joblib.load("lightgbm_model.joblib")
cat = CatBoostClassifier()
cat.load_model("catboost_model.cbm")
feature_cols = joblib.load("feature_columns.joblib")

with open("ensemble_config.json") as f:
    config = json.load(f)

# Prepare feature vector (113 features — see feature_columns.joblib)
# features = pd.DataFrame([your_feature_vector], columns=feature_cols)

# Predict
xgb_prob = xgb.predict_proba(features)[:, 1]
lgb_prob = lgb.predict_proba(features)[:, 1]
cat_prob = cat.predict_proba(features)[:, 1]

# Ensemble
score = 0.4 * xgb_prob + 0.35 * lgb_prob + 0.25 * cat_prob

# Interpret
if score >= 0.7:
    print("High Compatibility ❤️")
elif score >= 0.4:
    print("Moderate Compatibility 💛")
else:
    print("Low Compatibility 💔")

Visualizations

ROC Curves

Feature Importance

SHAP Summary

SHAP Dependence (Top 6 Features)

Confusion Matrix

Prediction Distribution

Literature Basis

Paper	Contribution
Grinsztajn et al. (NeurIPS 2022) — "Why do tree-based models still outperform deep learning on tabular data?"	Validated XGBoost/LightGBM as SOTA for tabular data with <100K rows
Fisman et al. (QJE 2006) — "Gender Differences in Mate Selection"	Original speed dating experiment; ~70% accuracy with logistic regression
Gorishniy et al. (NeurIPS 2021) — "Revisiting Deep Learning Models for Tabular Data"	FT-Transformer architecture for tabular; confirmed tree superiority on small datasets
Savcisens et al. (Nature Human Behaviour 2024) — "Using Sequences of Life-events to Predict Human Lives"	life2vec — longitudinal life-event prediction; architecture reusable for dyadic temporal modeling

Limitations

• Short-term proxy: The training data captures initial match decisions (4-minute speed dates), not long-term relationship outcomes. The model predicts initial compatibility, which is a proxy for — but not equivalent to — relationship longevity.
• Sample demographics: Columbia University students (2002-2004) — may not generalize to all demographics/cultures.
• Static features only: No temporal/interaction data. Adding communication patterns, life events, or behavioral signals would significantly improve longevity prediction (see life2vec approach).
• Class imbalance: 17.7% match rate means the model is well-calibrated for rejection but less certain for positive predictions.

Files

File	Description
xgboost_model.joblib	XGBoost classifier (2000 trees)
lightgbm_model.joblib	LightGBM classifier (2000 trees)
catboost_model.cbm	CatBoost classifier (2000 iterations)
ensemble_config.json	Ensemble weights, threshold, feature list, metrics
feature_columns.joblib	Ordered list of 113 feature column names
race_encoder.joblib	LabelEncoder for race categories
evaluation_results.csv	Full evaluation metrics table
feature_importance.csv	Feature importance rankings from XGB + LGB
predictor.py	Prediction interface class
train_relationship_predictor.py	Full training script (reproducible)
figures/	All visualizations (ROC, SHAP, confusion matrix, etc.)

License

Research use. Based on publicly available academic dataset.

---

Built with XGBoost, LightGBM, CatBoost, SHAP, and scikit-learn.