CSVy - Competitive Hockey Prediction Toolkit

**Ruby-based CSV preprocessing and feature engineering utility

Advanced Feature Engineering (14+ Features)

Team Strength Index: Composite win rate + goal differential metric
Pythagorean Expectation: Expected wins based on GF²/(GF²+GA²)
Momentum Scores: Recent performance trends (rolling win rates)
Rest Advantage: Days between games + back-to-back penalties
Clutch Factor: Performance in 1-goal games
Home/Away Splits: Location-based win rate analysis
Strength of Schedule: Opponent quality adjustments
Consistency Metrics: Coefficient of variation (low = consistent)
Interaction Features: offense_power (GF × win%), defense_weakness (GA × losses)
Polynomial Features: Non-linear relationships (DIFF², PTS²)
Luck Factor: Actual wins - Pythagorean expected wins
Time Decay Weights: Recent games weighted higher

Mathematical Optimization (5 Algorithms)

Grid Search: Exhaustive search (2,812 total combinations across 5 models)
Random Search: Fast sampling for large spaces
Bayesian Optimization: Gaussian Process with Expected Improvement acquisition
Genetic Algorithm: Evolution with crossover/mutation/selection
Simulated Annealing: Temperature-based exploration/exploitation

Ensemble Methods (Winning Strategy)

Stacking: Meta-learner trained on base model predictions
Blending: Holdout-based meta-model training
Weighted Voting: Optimized weights (inverse RMSE, softmax)
Rank Averaging: Robust to prediction scale differences
Dynamic Weights: Adaptive based on recent performance
Diversity Analysis: Checks error correlation (low = good ensemble)

Model Validation (Competition-Grade)

Time Series CV: Expanding window (no data leakage)
Stratified Splits: Balanced train/test for classification
Bootstrap CI: 1000+ iterations for confidence intervals
Overfitting Detection: Train vs test performance gap monitoring
Calibration Analysis: Binned predictions vs actuals
Learning Curves: Performance vs training size

Quick Start (3 Commands to Win)

# 1. Full competitive preprocessing pipeline
ruby cli.rb competitive-pipeline data/nhl_data.csv

# 2. Generate optimized hyperparameters (Bayesian search)
ruby cli.rb hyperparam-bayesian config/hyperparams/model4_xgboost.yaml --iterations 50

# 3. Optimize ensemble weights from all models
ruby cli.rb ensemble-optimize predictions/ --actuals test.csv -o weights.csv


---

## 📊 Competition Workflow (Step-by-Step)

### Phase 1: Data Preprocessing (CSVy)
```bash
# Run full competitive pipeline (includes all advanced features)
ruby cli.rb competitive-pipeline data/raw_nhl_data.csv -o data/processed

# Output: competitive_features.csv, train.csv, test.csv

Phase 2: Hyperparameter Generation (CSVy)

# Model 2: Linear Regression (120 combinations)
ruby cli.rb hyperparam-grid config/hyperparams/model2_linear_regression.yaml

# Model 3: ELO (648 combinations) - Use Bayesian for smarter search
ruby cli.rb hyperparam-bayesian config/hyperparams/model3_elo.yaml --iterations 30

# Model 4: XGBoost (864 combinations) - Genetic algorithm
ruby cli.rb hyperparam-genetic config/hyperparams/model4_xgboost.yaml --population 50 --generations 20

# Model 4: Random Forest (144 combinations)
ruby cli.rb hyperparam-grid config/hyperparams/model4_random_forest.yaml

# Model 5: Ensemble (36 combinations)
ruby cli.rb hyperparam-grid config/hyperparams/model5_ensemble.yaml

Phase 3: Model Training (DeepNote/Python)

import pandas as pd
from sklearn.ensemble import RandomForestRegressor
from xgboost import XGBRegressor

# Load hyperparameter configs
configs = pd.read_csv('model4_xgboost_genetic_algorithm.csv')

# Train each configuration
for idx, row in configs.iterrows():
    params = row.to_dict()
    experiment_id = params.pop('experiment_id')
    
    # Remove tracking columns
    for col in ['rmse', 'mae', 'r2', 'notes', 'timestamp']:
        params.pop(col, None)
    
    # Train model
    model = XGBRegressor(**params)
    model.fit(X_train, y_train)
    
    # Evaluate
    y_pred = model.predict(X_test)
    rmse = mean_squared_error(y_test, y_pred, squared=False)
    mae = mean_absolute_error(y_test, y_pred)
    r2 = r2_score(y_test, y_pred)
    
    # Save results
    configs.loc[idx, 'rmse'] = rmse
    configs.loc[idx, 'mae'] = mae
    configs.loc[idx, 'r2'] = r2

# Save updated tracking file
configs.to_csv('model4_xgboost_genetic_algorithm.csv', index=False)

Phase 4: Result Analysis (CSVy)

# Find best hyperparameters
ruby cli.rb best-params experiments/xgb_genetic.csv --metric rmse

# Compare top experiments
ruby cli.rb compare-experiments experiments/xgb_genetic.csv 10,25,42,78

# View summary statistics
ruby cli.rb diagnose experiments/xgb_genetic.csv

Phase 5: Ensemble Optimization (CSVy)

# After training all 5 models, save predictions to predictions/
# predictions/baseline_preds.csv
# predictions/linear_preds.csv
# predictions/elo_preds.csv
# predictions/xgboost_preds.csv
# predictions/rf_preds.csv

# Optimize ensemble weights
ruby cli.rb ensemble-optimize predictions/ --actuals data/test_actuals.csv -o optimal_weights.csv

# Check model diversity (should be > 0.5 for good ensemble)
ruby cli.rb diversity-analysis predictions/ data/test_actuals.csv

Phase 6: Final Validation (CSVy)

# Create weighted ensemble predictions (using optimal weights)
# Then validate with bootstrap and calibration

ruby cli.rb validate-model final_ensemble_preds.csv \
  --bootstrap \
  --calibration \
  --actual_col actual \
  --pred_col predicted

Key Commands Reference

Preprocessing & Feature Engineering

Command	Purpose	Example
`competitive-pipeline`	Full preprocessing + 14+ advanced features	`ruby cli.rb competitive-pipeline data/nhl.csv -o data/processed`
`advanced-features`	Add competition features only	`ruby cli.rb advanced-features data/nhl.csv -o data/advanced.csv`
`diagnose`	Data quality analysis	`ruby cli.rb diagnose data/nhl.csv`
`clean`	Remove duplicates & handle missing	`ruby cli.rb clean data/nhl.csv -o data/clean.csv`

Hyperparameter Optimization

Command	Purpose	When to Use
`hyperparam-grid`	Exhaustive search	Small grids (<1000 combinations)
`hyperparam-random`	Random sampling	Large spaces, quick exploration
`hyperparam-bayesian`	Gaussian Process optimization	Expensive models (XGBoost), 20-50 iterations
`hyperparam-genetic`	Evolution-based search	Large spaces, 50+ population, 20+ generations
`hyperparam-annealing`	Simulated annealing	Continuous spaces, good for fine-tuning

Experiment Tracking

Command	Purpose	Example
`add-result`	Record experiment metrics	`ruby cli.rb add-result experiments/grid.csv 42 --rmse 2.34 --mae 1.87 --r2 0.82`
`best-params`	Find optimal hyperparameters	`ruby cli.rb best-params experiments/grid.csv --metric rmse`
`compare-experiments`	Compare specific runs	`ruby cli.rb compare-experiments experiments/grid.csv 10,25,42`

Ensemble & Validation

Command	Purpose	Example
`ensemble-optimize`	Find optimal model weights	`ruby cli.rb ensemble-optimize predictions/ --actuals test.csv -o weights.csv`
`diversity-analysis`	Check ensemble diversity	`ruby cli.rb diversity-analysis predictions/ actuals.csv`
`validate-model`	Bootstrap + calibration	`ruby cli.rb validate-model preds.csv --bootstrap --calibration`

The 5-Model Winning Strategy

Model 1: Baseline (Benchmark)

Purpose: Establish baseline performance
Method: Simple mean/median prediction
Config: config/hyperparams/model1_baseline.yaml
Expected RMSE: 3-4 goals

Model 2: Linear Regression (120 combinations)

Purpose: Capture linear relationships
Features: Ridge/ElasticNet regularization, polynomial features, scaling
Config: config/hyperparams/model2_linear_regression.yaml
Hyperparameters: alpha (0.001-10), l1_ratio (0-1), solver, poly_degree (1-2)
Expected RMSE: 2.5-3.0 goals

Model 3: ELO Rating System (648 combinations)

Purpose: Team strength dynamics
Features: Custom ELO with MOV adjustments, home advantage, rest/b2b
Config: config/hyperparams/model3_elo.yaml
Hyperparameters: k_factor (20-40), home_advantage (50-150), MOV multipliers
Expected RMSE: 2.2-2.7 goals

Model 4: Tree Models (XGBoost: 864, RF: 144)

Purpose: Capture non-linear interactions
Features: Gradient boosting + Random Forest diversity
Configs:
- config/hyperparams/model4_xgboost.yaml
- config/hyperparams/model4_random_forest.yaml
Hyperparameters:
- XGBoost: learning_rate, n_estimators, max_depth, regularization
- RF: n_estimators, max_depth, min_samples, max_features
Expected RMSE: 1.8-2.3 goals (best individual model)

Model 5: Ensemble (36 combinations)

Purpose: Combine all models for maximum accuracy
Methods: Stacking (meta-learner), weighted voting, blending
Config: config/hyperparams/model5_ensemble.yaml
Hyperparameters: weight_method (inverse_rmse/softmax), meta_learner (ridge/elastic_net)
Expected RMSE: 1.5-1.9 goals (🏆 WINNING MODEL)

Winning Tips

1. Feature Engineering is 80% of Success

# Use ALL advanced features
ruby cli.rb competitive-pipeline data/nhl.csv

# Features that win:
# - team_strength_index (composite metric)
# - pythagorean_wins (expected vs actual)
# - momentum_score (hot/cold streaks)
# - clutch_factor (close game performance)
# - rest_days + is_back_to_back
# - home_away_diff

2. Hyperparameter Optimization Strategy

# Start with random search (fast exploration)
ruby cli.rb hyperparam-random config/hyperparams/model4_xgboost.yaml 100

# Then Bayesian optimization (smart exploitation)
ruby cli.rb hyperparam-bayesian config/hyperparams/model4_xgboost.yaml --iterations 50

# For final tuning: genetic algorithm
ruby cli.rb hyperparam-genetic config/hyperparams/model4_xgboost.yaml --population 50 --generations 30

3. Ensemble Diversity is Critical

# Check diversity before ensembling
ruby cli.rb diversity-analysis predictions/ actuals.csv

# Good: diversity_score > 0.5 (models are complementary)
# Bad: diversity_score < 0.3 (models too similar, ensemble won't help)

4. Time Series Validation (No Data Leakage!)

Use time_series_cv_split in validation (expanding window)
Never use random splits for time series data
Recent games should be test set, not training

5. Calibration Matters

# Check if predictions are well-calibrated
ruby cli.rb validate-model preds.csv --calibration

# Well-calibrated: mean_calibration_error < 0.1
# Poorly calibrated: predictions systematically over/under

6. Bootstrap for Confidence

# Know your uncertainty
ruby cli.rb validate-model preds.csv --bootstrap

# Tight CI (< 0.2): High confidence
# Wide CI (> 0.5): Model unstable

7. Monitor for Overfitting

Train RMSE - Test RMSE should be < 0.3
If gap > 0.5: Reduce model complexity, add regularization
Use learning curves to diagnose

Project Structure

CSVy/
├── lib/                              # Core libraries
│   ├── advanced_features.rb          # 14+ competition features
│   ├── model_validator.rb            # CV, bootstrap, calibration
│   ├── ensemble_builder.rb           # Stacking, blending, optimization
│   ├── hyperparameter_manager.rb     # 5 optimization algorithms
│   ├── time_series_features.rb       # Rolling, EWMA, lag
│   ├── csv_cleaner.rb                # Data cleaning
│   ├── data_preprocessor.rb          # Normalization, encoding
│   ├── csv_diagnostics.rb            # Quality analysis
│   └── html_reporter.rb              # Diagnostic reports
├── config/hyperparams/               # Model configurations
│   ├── model1_baseline.yaml          # No hyperparams
│   ├── model2_linear_regression.yaml # 120 combinations
│   ├── model3_elo.yaml               # 648 combinations
│   ├── model4_xgboost.yaml           # 864 combinations
│   ├── model4_random_forest.yaml     # 144 combinations
│   └── model5_ensemble.yaml          # 36 combinations
├── scripts/
│   ├── competitive_pipeline.rb       # Full preprocessing pipeline
│   └── preprocess_hockey.sh          # Batch preprocessing
├── data/
│   ├── sample_nhl_standings.csv      # Test data
│   └── processed/                    # Output directory
├── experiments/                      # Tracking files
├── cli.rb                            # 40+ CLI commands
├── README.md                         # This file
└── Gemfile                           # Dependencies

Complete Example (Start to Finish)

# ===== PHASE 1: PREPROCESSING =====
ruby cli.rb competitive-pipeline data/nhl_season_2024.csv -o data/processed
# Output: competitive_features.csv (with 14+ advanced features)

# ===== PHASE 2: HYPERPARAMETER GENERATION =====
# Generate configs for all 5 models
ruby cli.rb hyperparam-grid config/hyperparams/model2_linear_regression.yaml
ruby cli.rb hyperparam-bayesian config/hyperparams/model3_elo.yaml --iterations 30
ruby cli.rb hyperparam-genetic config/hyperparams/model4_xgboost.yaml --population 50 --generations 20
ruby cli.rb hyperparam-grid config/hyperparams/model4_random_forest.yaml
ruby cli.rb hyperparam-grid config/hyperparams/model5_ensemble.yaml

# ===== PHASE 3: PUSH TO GITHUB =====
git add .
git commit -m "Add competitive features and hyperparameter grids"
git push origin main

# ===== PHASE 4: TRAIN IN DEEPNOTE (Python) =====
# (See Phase 3 example above)
# Train all models, record rmse/mae/r2 in tracking CSVs

# ===== PHASE 5: PULL RESULTS =====
git pull origin main

# ===== PHASE 6: FIND BEST PARAMS =====
ruby cli.rb best-params experiments/xgb_genetic.csv --metric rmse
ruby cli.rb best-params experiments/rf_grid.csv --metric rmse
ruby cli.rb best-params experiments/elo_bayesian.csv --metric rmse

# ===== PHASE 7: ENSEMBLE OPTIMIZATION =====
# After generating predictions from all 5 models
ruby cli.rb ensemble-optimize predictions/ --actuals data/test_actuals.csv -o optimal_weights.csv
ruby cli.rb diversity-analysis predictions/ data/test_actuals.csv

# ===== PHASE 8: FINAL VALIDATION =====
ruby cli.rb validate-model final_ensemble_preds.csv --bootstrap --calibration

Expected Performance

Model	RMSE	MAE	R²	Notes
Baseline	3.5	2.8	0.40	Benchmark
Linear Regression	2.7	2.1	0.65	With poly features
ELO Rating	2.4	1.9	0.72	With MOV adjustments
XGBoost	2.0	1.6	0.82	Best individual
Random Forest	2.2	1.7	0.78	Good diversity
Ensemble	1.7	1.3	0.87	🏆 WINNING

Performance improves by 50% from baseline to ensemble!

Requirements

Ruby 2.7+
Thor gem (gem install thor)
Standard library: CSV, Logger, FileUtils, Date

bundle install

Integration with DeepNote

Setup

Create DeepNote project
Connect GitHub repository
Set up automatic sync (webhook)

Workflow

# Local (CSVy)
ruby cli.rb competitive-pipeline data/nhl.csv
git push

# DeepNote (auto-pulls from GitHub)
# Train models in Python
git push  # Push results back

# Local (CSVy)
git pull
ruby cli.rb best-params experiments/grid.csv --metric rmse

Built to Win

CSVy contains everything needed to dominate hockey prediction competitions:

40+ CLI commands
14+ advanced features (momentum, clutch, pythagorean, strength index)
5 optimization algorithms (Grid, Random, Bayesian, Genetic, Annealing)
Ensemble methods (Stacking, Blending, Weighted voting)
Model validation (Bootstrap CI, Calibration, Time series CV)
6 hyperparameter configs (2,812 total combinations)
Complete preprocessing pipeline
Diversity analysis (Ensure complementary models)
Overfitting detection (Train vs test monitoring)

What Sets This Apart:

Mathematical rigor: Bayesian optimization, Gaussian Processes, Expected Improvement
Domain expertise: Hockey-specific features (rest days, b2b, clutch, home/away)
Ensemble intelligence: Optimal weight optimization, diversity checks
No data leakage: Time series CV with expanding window
Production-ready: Auto-directory creation, comprehensive logging

Usage Questions?

# List all commands
ruby cli.rb help

# Help for specific command
ruby cli.rb help competitive-pipeline
ruby cli.rb help hyperparam-bayesian
ruby cli.rb help ensemble-optimize

Let's win this!

x_standardized = (x - μ) / σ
where μ = mean, σ = standard deviation

Centers data around 0 with std dev of 1
Example: [10, 20, 30] → [-1.22, 0.0, 1.22]

Data Quality

Missing Value Handling: Multiple strategies
- mean: Fill with column average
- median: Fill with middle value (robust to outliers)
- mode: Fill with most frequent value
- forward_fill: Propagate last valid value
- backward_fill: Use next valid value
Outlier Detection
- IQR Method: outlier if x < Q1 - 1.5×IQR or x > Q3 + 1.5×IQR
  - Q1 = 25th percentile, Q3 = 75th percentile
  - IQR = Q3 - Q1 (Interquartile Range)
- Z-Score Method: outlier if |z| > 3
  - Removes values more than 3 standard deviations from mean
Binning: Convert continuous → categorical
- Example: Age [0-18, 19-35, 36-60, 61+] → ['child', 'young', 'adult', 'senior']

Data Analysis & Validation

Statistical Measures

Descriptive Statistics:

Mean (μ) = Σx / n
Median = middle value when sorted
Mode = most frequent value
Std Dev (σ) = √(Σ(x - μ)² / n)
Variance (σ²) = Σ(x - μ)² / n

Quartiles & Percentiles:
- Q1 (25th percentile): 25% of data below
- Q2 (50th percentile): Median
- Q3 (75th percentile): 75% of data below
Data Validation: Comprehensive quality checks
- Empty rows detection
- Duplicate identification
- Missing value analysis
- Data type inference
Data Profiling: Column-level metrics
- Cardinality: unique_values / total_values
- Missing rate: missing_count / total_count × 100%
- Distribution analysis
Integrity Checks: Custom validation rules
- Range validation: min ≤ value ≤ max
- Pattern matching: Regex validation
- Uniqueness constraints
- Enum validation

Advanced Features

Daru Integration: Dataframe operations (filter, sort, group, aggregate)
SQLite Database: Import/export CSV data, run SQL queries
CLI Interface: User-friendly command-line interface
Comprehensive Testing: Full RSpec test suite with 90%+ coverage
Rake Tasks: Automated workflows and examples

Tech Stack

Language: Ruby
CSV Handling: Ruby CSV (Standard Library)
Data Manipulation: Daru (Optional, for advanced operations)
CLI Framework: Thor
Testing: RSpec
Environment Management: Dotenv
Database (Optional): SQLite3

Installation

Prerequisites

Ruby 2.7 or higher
Bundler

Setup

Clone the repository:

git clone https://github.com/smiles0527/CSVy.git
cd CSVy

Install dependencies:

gem install bundler
bundle install

Try with sample hockey data:

ruby cli.rb info data/sample_employees.csv
ruby cli.rb validate data/sample_students_dirty.csv

Usage

Quick Start

# Install dependencies
bundle install

# Run examples to see features in action
rake examples

# Or run individual examples
rake example_validate
rake example_clean

Command Line Interface

Data Cleaning

# Clean player statistics with missing values
ruby cli.rb clean data/sample_students_dirty.csv

Data Preprocessing

# One-hot encode player positions
ruby cli.rb encode data/sample_employees.csv position -t onehot

# Normalize goals scored across season
ruby cli.rb normalize data/sample_employees.csv goals -m minmax

# Standardize plus/minus ratings
ruby cli.rb normalize data/sample_employees.csv plus_minus -m zscore

Data Validation & Analysis

# Validate player data quality
ruby cli.rb validate data/sample_employees.csv

# Generate statistics for team performance
ruby cli.rb stats data/sample_products.csv

# Profile game results dataset
ruby cli.rb profile data/sample_weather.csv

File Operations

# Merge player stats from multiple seasons
ruby cli.rb merge season1.csv season2.csv -o combined_stats.csv

# Display team standings information
ruby cli.rb info data/sample_products.csv

Database Operations

# Import player data to database
ruby cli.rb db-import data/sample_employees.csv players

# Import team standings
ruby cli.rb db-import data/sample_products.csv teams

# Query top scorers
ruby cli.rb db-query "SELECT * FROM players WHERE goals > 50 ORDER BY goals DESC"

# List all hockey data tables
ruby cli.rb db-tables

Programmatic Usage

require_relative 'lib/csv_processor'
require_relative 'lib/csv_cleaner'
require_relative 'lib/csv_merger'

# Clean a CSV file
CSVProcessor.clean('data/input.csv')

# Merge CSV files
CSVProcessor.merge('data/file1.csv', 'data/file2.csv', 'output.csv')

# Advanced cleaning
cleaner = CSVCleaner.new('data/input.csv')
cleaned_data = cleaner.clean_data
cleaner.normalize_column(cleaned_data, 'age')
cleaner.save_to_csv(cleaned_data, 'cleaned_output.csv')

# Advanced merging
merger = CSVMerger.new
merged = merger.join_on_column('file1.csv', 'file2.csv', key_column: 'id')
merger.save_to_csv(merged, 'joined.csv')

Testing

Run the test suite:

bundle exec rspec

Run specific test file:

bundle exec rspec spec/csv_processor_spec.rb

Run with verbose output:

bundle exec rspec --format documentation

Project Structure

CSVy/
├── lib/
│   ├── csv_processor.rb       # Main CSV processing orchestrator
│   ├── csv_cleaner.rb         # Data cleaning functions
│   ├── csv_merger.rb          # CSV merging operations
│   ├── data_preprocessor.rb   # One-hot encoding, normalization, outlier removal
│   ├── data_validator.rb      # Validation, statistics, profiling
│   ├── dataframe_handler.rb   # Daru dataframe operations
│   └── database_manager.rb    # SQLite database integration
├── spec/
│   ├── csv_processor_spec.rb
│   ├── csv_cleaner_spec.rb
│   ├── csv_merger_spec.rb
│   ├── data_preprocessor_spec.rb
│   ├── data_validator_spec.rb
│   └── spec_helper.rb
├── data/                       # Sample CSV files included
│   ├── sample_employees.csv
│   ├── sample_products.csv
│   ├── sample_weather.csv
│   └── sample_students_dirty.csv
├── cli.rb                      # Command-line interface
├── Rakefile                    # Rake tasks for automation
├── Gemfile                     # Ruby dependencies
├── .gitignore
├── .env.example
├── USAGE_GUIDE.md             # Comprehensive usage examples
└── README.md

Development

Running the app in development

ruby cli.rb [command] [options]

Adding new features

Create new methods in appropriate lib files
Add corresponding tests in spec/ directory
Update CLI commands in cli.rb if needed
Run tests to ensure everything works

Dependencies

csv - Built-in CSV handling
thor - CLI framework
daru - Dataframe operations (optional)
rspec - Testing framework
dotenv - Environment variable management
sqlite3 - Database support (optional)

Contributing

Contributions are welcome! Please follow these steps:

Fork the repository
Create a feature branch (git checkout -b feature/amazing-feature)
Commit your changes (git commit -m 'Add amazing feature')
Push to the branch (git push origin feature/amazing-feature)
Open a Pull Request

License

This project is open source and available under the MIT License.

Author

smiles0527

Acknowledgments

Ruby CSV Standard Library
Thor CLI Framework
RSpec Testing Framework
The Ruby community

Support

For issues, questions, or contributions, please open an issue on GitHub.

CSVy - Professional CSV processing and organization tool for Ruby.

Name		Name	Last commit message	Last commit date
Latest commit History 56 Commits
.github/workflows		.github/workflows
config/hyperparams		config/hyperparams
data		data
docs		docs
experiments		experiments
lib		lib
python		python
scripts		scripts
spec		spec
.env.example		.env.example
.gitignore		.gitignore
.rspec		.rspec
Gemfile		Gemfile
KNOWN_ISSUES.md		KNOWN_ISSUES.md
NEURAL_NETWORK_GUIDE.md		NEURAL_NETWORK_GUIDE.md
README.md		README.md
Rakefile		Rakefile
STRUCTURE.md		STRUCTURE.md
TODO.md		TODO.md
WINNING_QUICK_START.md		WINNING_QUICK_START.md
cli.rb		cli.rb

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CSVy - Competitive Hockey Prediction Toolkit

Advanced Feature Engineering (14+ Features)

Mathematical Optimization (5 Algorithms)

Ensemble Methods (Winning Strategy)

Model Validation (Competition-Grade)

Quick Start (3 Commands to Win)

Phase 2: Hyperparameter Generation (CSVy)

Phase 3: Model Training (DeepNote/Python)

Phase 4: Result Analysis (CSVy)

Phase 5: Ensemble Optimization (CSVy)

Phase 6: Final Validation (CSVy)

Key Commands Reference

Preprocessing & Feature Engineering

Hyperparameter Optimization

Experiment Tracking

Ensemble & Validation

The 5-Model Winning Strategy

Model 1: Baseline (Benchmark)

Model 2: Linear Regression (120 combinations)

Model 3: ELO Rating System (648 combinations)

Model 4: Tree Models (XGBoost: 864, RF: 144)

Model 5: Ensemble (36 combinations)

Winning Tips

1. Feature Engineering is 80% of Success

2. Hyperparameter Optimization Strategy

3. Ensemble Diversity is Critical

4. Time Series Validation (No Data Leakage!)

5. Calibration Matters

6. Bootstrap for Confidence

7. Monitor for Overfitting

Project Structure

Complete Example (Start to Finish)

Expected Performance

Requirements

Integration with DeepNote

Setup

Workflow

Built to Win

What Sets This Apart:

Usage Questions?

Data Quality

Data Analysis & Validation

Statistical Measures

Advanced Features

Tech Stack

Installation

Prerequisites

Setup

Usage

Quick Start

Command Line Interface

Data Cleaning

Data Preprocessing

Data Validation & Analysis

File Operations

Database Operations

Programmatic Usage

Testing

Project Structure

Development

Running the app in development

Adding new features

Dependencies

Contributing

License

Author

Acknowledgments

Support

About

Resources

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