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edu-rank-prediction

This project predicts the rankings of universities based on various features.

Problem Description

• Rank Categories:
  • 1-2092: Total number of universities in the ranking.
  • 1500+: Universities ranked 1-1500 are categorized as top universities.
  • 1500-: Universities ranked beyond 1500.

Model: Random Forest

The Random Forest model was trained with the following hyperparameters:

{
   'n_estimators': 1000,
    'min_samples_split': 4,
    'min_samples_leaf': 2,
    'max_features': 8,
    'max_depth': 100,
    'bootstrap': True,
    'random_state': 42,
    'verbose': 1,
    'class_weight': 'balanced'
}

Model Performance

• Accuracy: 72.90%

Classification Report

Model Performance Metrics

Rank Category	Precision	Recall	F1-Score	Support
1-300	0.96	0.94	0.95	90
301-400	0.84	0.90	0.87	90
401-700	0.74	0.66	0.69	90
701-900	0.64	0.68	0.66	90
901-1100	0.67	0.60	0.63	90
1101-1200	0.75	0.84	0.79	90
1201-1300	0.78	0.62	0.69	90
1301-1400	0.69	0.71	0.70	90
1401-1500	0.65	0.82	0.73	90
1501-1600	0.76	0.72	0.74	90
1601-1700	0.70	0.68	0.69	90
1701-1800	0.74	0.71	0.72	90
1801+	0.60	0.59	0.59	90

Overall Performance

Metric	Value
Accuracy	0.73
Macro Avg	0.73
Weighted Avg	0.73

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Insights

• High Precision and Recall in Top Ranks: The model performs exceptionally well in the 1-300 category, with an F1-score of 0.95. This indicates strong reliability for top-ranking instances.
• Middle Ranks Performance: Categories such as 1101-1200 and 1401-1500 show balanced precision and recall, suggesting consistent performance.
• Lower Ranks (1801+): Precision and recall drop significantly for lower-ranked categories, with an F1-score of only 0.59. This could indicate difficulty in distinguishing these instances or data imbalance.
• Macro vs. Weighted Avg: Similar values for macro and weighted averages show that the dataset is relatively balanced in terms of category distribution.
• Areas for Improvement: Focus on enhancing performance in the 401-700 and 1801+ categories by exploring better feature engineering, resampling, or advanced models.