AI Model Recommender - ML Model Selection Guide

Intelligent Problem Type Detection

• Auto-Detection: Automatically identifies problem type based on data structure
• Manual Selection: Choose from Classification, Regression, Clustering, Time Series, or Anomaly Detection
• Context Analysis: Examines column names and data patterns for intelligent suggestions
• Flexible Options: Override auto-detection with manual problem type selection

Comprehensive Model Recommendations

• Model Suggestions: Specific ML models tailored to your problem type and dataset size
• Complexity Assessment: Models rated by complexity, accuracy, and interpretability
• Tool Recommendations: Suggested frameworks and tools based on data characteristics
• Implementation Guidance: Detailed next steps and implementation roadmap

Professional Project Planning

• Effort Estimation: Realistic timeline estimates based on data complexity
• Resource Requirements: Guidance on computational and skill requirements
• Next Steps: Detailed roadmap from data preparation to model deployment
• Best Practices: Industry-standard approaches for your specific use case

Model Suggestions

The system provides model recommendations based on dataset characteristics:

For Small Datasets (<1,000 rows)

• Random Forest: Low complexity, high accuracy, medium interpretability
• Logistic Regression: Low complexity, medium accuracy, high interpretability
• SVM: Medium complexity, high accuracy, low interpretability

For Medium Datasets (1,000-10,000 rows)

• XGBoost: Medium complexity, very high accuracy, medium interpretability
• Neural Networks: High complexity, very high accuracy, low interpretability
• LightGBM: Medium complexity, high accuracy, medium interpretability

For Large Datasets (>10,000 rows)

• Deep Learning: High complexity, very high accuracy, low interpretability
• Ensemble Methods: Medium complexity, very high accuracy, medium interpretability
• AutoML: Low complexity, high accuracy, medium interpretability

Tool Recommendations

Based on dataset size and complexity:

Small to Medium Datasets

• Scikit-learn: Python's premier ML library
• R: Statistical computing and graphics
• Excel Power Query: For simple models and data preparation

Large Datasets

• Python (Pandas): Data manipulation and analysis
• Apache Spark: Big data processing
• H2O.ai: Scalable machine learning platform

Specialized Tools

• Time Series: Prophet, Statsmodels
• Deep Learning: TensorFlow, PyTorch
• AutoML: H2O AutoML, AutoSklearn

Implementation Roadmap

The system provides a detailed next steps guide:

1. Data Quality Improvement (if quality score <70%)
2. Data Splitting: Create train/validation/test sets
3. Exploratory Data Analysis: Understand data patterns
4. Feature Engineering: Create and select relevant features
5. Baseline Model: Start with simple model for comparison
6. Advanced Models: Experiment with recommended models
7. Hyperparameter Tuning: Optimize model performance

Complexity Factors

The system considers multiple factors for effort estimation:

• Dataset Size: Larger datasets require more processing time
• Column Count: More features increase complexity
• Problem Type: Some problems (Time Series, Anomaly Detection) are inherently more complex
• Data Quality: Poor quality data requires additional preprocessing

Effort Categories

• Low (1-2 days): Simple datasets with standard problem types
• Medium (3-5 days): Moderate complexity with some challenges
• High (1-2 weeks): Complex datasets or specialized problem types
• Very High (2+ weeks): Large, complex datasets with multiple challenges

Effective Model Selection

• Start Simple: Begin with recommended baseline models before trying complex approaches
• Consider Interpretability: Balance accuracy with explainability based on business needs
• Validate Thoroughly: Use proper train/validation/test splits for reliable evaluation
• Iterate Gradually: Build complexity incrementally rather than jumping to advanced models

Implementation Strategy

• Follow Roadmap: Use the provided next steps as a structured implementation guide
• Resource Planning: Consider computational requirements and team skills
• Tool Selection: Choose tools based on team expertise and infrastructure
• Timeline Management: Use effort estimates for realistic project planning

Model Characteristics

Each recommended model includes:

• Complexity: Implementation and computational complexity
• Accuracy: Expected performance level
• Interpretability: How easily results can be explained

Selection Criteria

Consider these factors when choosing models:

• Business Requirements: Accuracy vs. interpretability trade-offs
• Technical Constraints: Available computational resources
• Team Expertise: Familiarity with recommended tools and techniques
• Timeline: Available time for implementation and tuning