# Octolearn v0.7.0 - Implementation Roadmap & Code Examples

**Status:** Ready for development  
**Priority:** High-impact UX improvements  
**Effort:** 2-3 weeks for expert developer  

---

## Overview: What Users Need

**Current Pain Point:**
```python
automl = AutoML(
    use_full_data=False,
    sample_size=500,
    parallel_workers=7,
    show_progress=True,
    generate_shap=True,
    calculate_feature_importance=True,
    generate_recommendations=True,
    detect_outliers=True,
    analyze_interactions=True,
    auto_clean=True,
    train_models=True,
    use_optuna=True,
    use_registry=True,
    n_models=5
)
```

**Beginner's thoughts:** "Which ones do I need to change? What's the difference?"

---

## Implementation 1: Preset Configurations

### File: `octolearn/presets.py` (NEW)

```python
"""
Preset configurations for Octolearn.
Simplifies configuration for common use cases.
"""

# Define presets as dictionaries
_PRESETS = {
    # =========================================================================
    # BEGINNER: Safe, simple, focused on learning
    # =========================================================================
    'beginner': {
        'description': 'Perfect for learning ML. Profiles data, skips expensive analysis.',
        'use_full_data': False,           # Sample for speed
        'sample_size': 500,
        'parallel_workers': 4,            # Reduce CPU load
        'show_progress': True,            # Show what's happening
        'generate_shap': False,           # Skip expensive SHAP
        'calculate_feature_importance': True,
        'generate_recommendations': True,
        'detect_outliers': False,         # Skip outlier analysis
        'analyze_interactions': False,    # Skip interaction analysis
        'auto_clean': True,               # Still clean data
        'train_models': False,            # Just profile & analyze
        'use_optuna': False,
        'use_registry': False,
        'n_models': 0,
    },
    
    # =========================================================================
    # PRODUCTION: Full pipeline, all features enabled
    # =========================================================================
    'production': {
        'description': 'Complete pipeline for production models. All features enabled.',
        'use_full_data': True,            # Use all data for accuracy
        'sample_size': None,              # Ignore sampling
        'parallel_workers': 8,            # Max parallelism
        'show_progress': False,           # Silent in production
        'generate_shap': True,            # Full analysis
        'calculate_feature_importance': True,
        'generate_recommendations': True,
        'detect_outliers': True,          # Detect anomalies
        'analyze_interactions': True,     # Full feature analysis
        'auto_clean': True,               # Full cleaning
        'train_models': True,             # Train all models
        'use_optuna': True,               # Full HPO
        'use_registry': True,             # Version models
        'n_models': 6,                    # All models
    },
    
    # =========================================================================
    # FAST: Speed-optimized for rapid iteration
    # =========================================================================
    'fast': {
        'description': 'Speed-optimized for rapid prototyping and iteration.',
        'use_full_data': False,           # Sample aggressively
        'sample_size': 300,               # Small sample
        'parallel_workers': 4,
        'show_progress': True,
        'generate_shap': False,           # Skip SHAP
        'calculate_feature_importance': True,
        'generate_recommendations': False,
        'detect_outliers': False,         # Skip outlier detection
        'analyze_interactions': False,    # Skip interactions
        'auto_clean': True,               # Still clean
        'train_models': True,             # But train
        'use_optuna': False,              # Skip HPO
        'use_registry': False,            # Skip registry
        'n_models': 3,                    # Fewer models
    },
    
    # =========================================================================
    # PROFILE_ONLY: Dataset analysis without model training
    # =========================================================================
    'profile_only': {
        'description': 'Deep dataset analysis and profiling. No model training.',
        'use_full_data': False,
        'sample_size': 500,
        'parallel_workers': 7,
        'show_progress': True,
        'generate_shap': True,
        'calculate_feature_importance': True,
        'generate_recommendations': True,
        'detect_outliers': True,          # Full analysis
        'analyze_interactions': True,     # Full analysis
        'auto_clean': True,
        'train_models': False,            # No training
        'use_optuna': False,
        'use_registry': False,
        'n_models': 0,
    },
}


def get_preset(preset_name: str) -> dict:
    """
    Get a preset configuration by name.
    
    Parameters
    ----------
    preset_name : str
        Preset name: 'beginner', 'production', 'fast', 'profile_only'
    
    Returns
    -------
    dict
        Configuration dictionary
    
    Raises
    ------
    ValueError
        If preset name not found
    
    Examples
    --------
    >>> preset = get_preset('beginner')
    >>> automl = AutoML(**preset)
    """
    if preset_name not in _PRESETS:
        available = list(_PRESETS.keys())
        raise ValueError(
            f"Preset '{preset_name}' not found. "
            f"Available: {available}"
        )
    
    return _PRESETS[preset_name].copy()


def list_presets() -> dict:
    """
    List all available presets with descriptions.
    
    Returns
    -------
    dict
        Presets with descriptions
    
    Examples
    --------
    >>> presets = list_presets()
    >>> for name, config in presets.items():
    ...     print(f"{name}: {config['description']}")
    """
    return {
        name: config.pop('description', '')
        for name, config in _PRESETS.items()
    }
```

### Update: `octolearn/core.py` - Modify `__init__` method

```python
class AutoML:
    def __init__(
        self,
        preset: str = None,  # NEW: Add preset parameter
        use_full_data: bool = False,
        sample_size: int = 500,
        # ... rest of parameters
    ):
        """
        Initialize AutoML pipeline.
        
        Parameters
        ----------
        preset : str, optional
            Use preset configuration: 'beginner', 'production', 'fast', 'profile_only'
            If preset is specified, other parameters are overridden.
        use_full_data : bool
            Use full dataset or sample (ignored if preset specified)
        # ... rest of docstring
        
        Examples
        --------
        # Simple preset usage
        >>> automl = AutoML(preset='beginner')
        >>> automl.fit(X, y)
        
        # Custom configuration (overrides preset defaults)
        >>> automl = AutoML(preset='fast', n_models=4)
        """
        from .presets import get_preset
        
        # Load preset if specified
        if preset is not None:
            preset_config = get_preset(preset)
            
            # Apply preset defaults, allow overrides via function args
            for key, value in preset_config.items():
                if key not in locals() or locals()[key] == None:
                    # Use preset value if parameter not explicitly provided
                    pass
            
            # Store preset name for reference
            self.preset_ = preset
        else:
            self.preset_ = None
        
        # Initialize rest as before
        self.profiler = DataProfiler()
        # ... rest of initialization
```

### Usage Examples

```python
# Beginner: Simple, safe defaults
automl = AutoML(preset='beginner')
automl.fit(X, y)
profile = automl.report()  # Just profiling

# Production: All features
automl = AutoML(preset='production')
automl.fit(X, y)
automl.train_auto_models()
model = automl.get_best_model()

# Fast iteration: Speed-optimized
automl = AutoML(preset='fast')
automl.fit(X, y)

# Custom: Start with fast, modify
automl = AutoML(preset='fast', n_models=4, use_optuna=True)

# List available presets
from octolearn.presets import list_presets
for name, description in list_presets().items():
    print(f"{name}: {description}")
```

---

## Implementation 2: Better Error Messages & Validation

### File: `octolearn/validation.py` (NEW)

```python
"""
Data validation and user guidance.
Provides helpful error messages and warnings.
"""

import pandas as pd
import numpy as np
from typing import Tuple, List
import warnings

from .utils.helpers import setup_logger

logger = setup_logger(__name__)


class DataValidator:
    """Validates input data and provides user guidance."""
    
    @staticmethod
    def validate_data(
        X: pd.DataFrame,
        y: pd.Series
    ) -> Tuple[List[str], List[str]]:
        """
        Validate X and y data.
        
        Returns
        -------
        errors : list
            Critical errors (stop execution)
        warnings : list
            Warnings (continue but inform)
        """
        errors = []
        warnings = []
        
        # =====================================================================
        # CRITICAL ERRORS (Stop execution)
        # =====================================================================
        
        # Size checks
        if X.shape[0] < 10:
            errors.append(
                "❌ Dataset too small (< 10 rows). "
                "Need at least 10 rows for meaningful analysis. "
                "Consider: collecting more data or using a larger dataset."
            )
        
        if X.shape[1] < 2:
            errors.append(
                "❌ Not enough features (< 2). "
                "Need at least 2 features for training. "
                "Consider: adding more predictive variables."
            )
        
        # NaN checks
        if X.isnull().all().any():
            errors.append(
                "❌ Some columns are entirely missing (all NaN). "
                f"Columns: {X.columns[X.isnull().all()].tolist()}. "
                "Consider: removing these columns or filling them."
            )
        
        if y.isnull().all():
            errors.append(
                "❌ Target variable (y) is entirely missing (all NaN). "
                "Can't train model without target values."
            )
        
        # Index alignment
        if X.shape[0] != y.shape[0]:
            errors.append(
                f"❌ X and y have different lengths. "
                f"X.shape[0]={X.shape[0]}, y.shape[0]={y.shape[0]}. "
                "Ensure X and y have same number of rows."
            )
        
        # =====================================================================
        # WARNINGS (Continue but inform user)
        # =====================================================================
        
        # Size warnings
        if X.shape[0] < 100:
            warnings.append(
                f"⚠️  Small dataset ({X.shape[0]} rows). "
                "Cross-validation results may be unreliable. "
                "Consider: collecting 100+ rows for stable results."
            )
        
        if X.shape[0] > 1_000_000:
            warnings.append(
                f"⚠️  Large dataset ({X.shape[0]:,} rows). "
                "Processing may be slow. "
                "Consider: enable Dask with use_dask=True or sample data."
            )
        
        # Missing data warnings
        missing_pct = X.isnull().sum().sum() / (X.shape[0] * X.shape[1]) * 100
        if missing_pct > 30:
            warnings.append(
                f"⚠️  High missing data ({missing_pct:.1f}%). "
                "Model accuracy may suffer. "
                "Consider: imputation strategy or removing incomplete rows."
            )
        
        # Duplicate warnings
        duplicates = X.duplicated().sum()
        if duplicates > X.shape[0] * 0.1:  # >10%
            warnings.append(
                f"⚠️  {duplicates} duplicate rows ({duplicates/X.shape[0]*100:.1f}%). "
                "These will be removed during cleaning. "
                "Consider: investigating why duplicates exist."
            )
        
        # Class imbalance warnings
        if pd.api.types.is_categorical_dtype(y) or y.dtype == 'object':
            min_class = y.value_counts().min()
            class_ratio = min_class / len(y)
            
            if class_ratio < 0.05:  # <5% minority
                warnings.append(
                    f"⚠️  Severe class imbalance ({class_ratio*100:.1f}% minority). "
                    "Model may struggle with minority class. "
                    "Consider: SMOTE, class weights, or collecting more data."
                )
            elif class_ratio < 0.2:  # <20% minority
                warnings.append(
                    f"⚠️  Class imbalance ({class_ratio*100:.1f}% minority). "
                    "Consider: using stratified cross-validation or class weights."
                )
        
        # Feature type warnings
        numeric_cols = X.select_dtypes(include=[np.number]).shape[1]
        categorical_cols = X.select_dtypes(include=['object']).shape[1]
        
        if categorical_cols == 0:
            warnings.append(
                "💡 Tip: No categorical features detected. "
                "If you have non-numeric features, ensure they are properly encoded."
            )
        
        # Feature names
        if [col for col in X.columns if col is None]:
            warnings.append(
                "⚠️  Some columns have None or empty names. "
                "Consider: naming all columns (X.columns = [...])"
            )
        
        return errors, warnings


def validate_and_report(X: pd.DataFrame, y: pd.Series) -> bool:
    """
    Validate data and report errors/warnings to user.
    
    Returns
    -------
    bool
        True if validation passed (errors=0), False otherwise
    """
    errors, warnings = DataValidator.validate_data(X, y)
    
    # Report errors
    if errors:
        print("=" * 70)
        print("❌ DATA VALIDATION ERRORS")
        print("=" * 70)
        for error in errors:
            print(f"\n{error}\n")
        return False
    
    # Report warnings
    if warnings:
        print("=" * 70)
        print("⚠️  DATA VALIDATION WARNINGS")
        print("=" * 70)
        for i, warning in enumerate(warnings, 1):
            print(f"\n{i}. {warning}\n")
    
    return True
```

### Update: `octolearn/core.py` - Add validation to fit method

```python
def fit(self, X: pd.DataFrame, y: pd.Series):
    """Fit the complete AutoML pipeline."""
    from .validation import validate_and_report
    
    # Validate data
    if not validate_and_report(X, y):
        raise ValueError("Data validation failed. Please fix errors above.")
    
    # Continue with fitting...
    validate_dataframe(X, "X")
    validate_series(y, "y")
    # ... rest of fit method
```

---

## Implementation 3: Better Progress Logging

### Update: `octolearn/core.py` - Enhanced logging

```python
def fit(self, X: pd.DataFrame, y: pd.Series):
    """Fit the complete AutoML pipeline with detailed logging."""
    
    if self.show_progress:
        print("\n" + "=" * 70)
        print("🐙 Octolearn - Automated Machine Learning Pipeline")
        print("=" * 70)
        print(f"Version: {__version__}")
        print(f"Input data: {X.shape[0]} rows × {X.shape[1]} columns")
        print(f"Task: Analyzing...")
        print("=" * 70)
    
    # Store original data
    self.X_original_ = X.copy()
    self.y_original_ = y.copy()
    
    # Sample if necessary
    if not self.use_full_data and X.shape[0] > self.sample_size:
        if self.show_progress:
            print(f"\n📊 Sampling {self.sample_size} rows from {X.shape[0]} total")
        X_sampled = X.sample(n=self.sample_size, random_state=42)
        y_sampled = y.loc[X_sampled.index]
    else:
        X_sampled = X
        y_sampled = y
    
    self.X_ = X_sampled
    self.y_ = y_sampled
    
    # =========================================================================
    # PHASE 1: DATASET PROFILING
    # =========================================================================
    if self.show_progress:
        print(f"\n📈 PHASE 1: Dataset Profiling")
        print(f"   Processing {self.X_.shape[0]} rows...")
    
    self.profile_ = self.profiler.profile(self.X_, self.y_)
    
    if self.show_progress:
        print(f"   ✓ Detected {len(self.profile_['numeric_features'])} numeric features")
        print(f"   ✓ Detected {len(self.profile_['categorical_features'])} categorical features")
        print(f"   ✓ Missing values: {self.profile_['missing_values']}")
        print(f"   ✓ Duplicate rows: {self.profile_['duplicates']}")
        print(f"   ✓ Task type: {self.profile_['task_type']}")
    
    # =========================================================================
    # PHASE 2: EDA & RISK ASSESSMENT  
    # =========================================================================
    if self.show_progress:
        print(f"\n🔍 PHASE 2: Data Analysis & Risk Assessment")
    
    # Risk scoring
    risk_scorer = RiskScorer(self.profile_)
    self.risk_score_ = risk_scorer.score()
    
    if self.show_progress:
        print(f"   ✓ Risk Score: {self.risk_score_['score']:.0f}/100 ({self.risk_score_['category']})")
    
    # Outlier detection
    if self.detect_outliers:
        if self.show_progress:
            print(f"   ✓ Detecting outliers...")
        detector = OutlierDetector(self.X_)
        self.outlier_results_ = detector.detect()
    
    # Feature importance
    if self.calculate_feature_importance:
        if self.show_progress:
            print(f"   ✓ Calculating feature importance...")
        importance_engine = BaselineImportance(self.X_, self.y_, self.profile_['task_type'])
        self.feature_importance_ = importance_engine.score()
    
    # =========================================================================
    # PHASE 3: AUTO-CLEANING
    # =========================================================================
    if self.show_progress:
        print(f"\n🧹 PHASE 3: Automatic Data Cleaning")
        print(f"   Starting with {self.X_.shape}")
    
    if self.auto_clean:
        cleaner = AutoCleaner(self.X_, self.y_, self.profile_)
        self.X_, self.y_, self.cleaning_log_ = cleaner.clean()
        
        if self.show_progress:
            print(f"   ✓ Finished with {self.X_.shape}")
            print(f"   ✓ Removed {self.X_original_.shape[0] - self.X_.shape[0]} rows")
            print(f"   ✓ Removed {self.X_original_.shape[1] - self.X_.shape[1]} columns")
    
    # =========================================================================
    # PHASE 4: MODEL TRAINING (optional)
    # =========================================================================
    if self.train_models:
        if self.show_progress:
            print(f"\n🤖 PHASE 4: Model Training & Optimization")
            print(f"   Training up to {self.n_models} models with Optuna...")
    
    # ... rest of fit method

    if self.show_progress:
        print("\n" + "=" * 70)
        print("✅ PIPELINE COMPLETE!")
        print("=" * 70)
        print(f"Profile: automl.report()")
        print(f"Risk Score: automl.get_risk_score()")
        print(f"Best Model: automl.get_best_model()")
        print("=" * 70 + "\n")
```

---

## Implementation 4: Add Convenience Methods

### File: Update `octolearn/core.py` - Add new methods

```python
class AutoML:
    """Main AutoML orchestrator with convenience methods."""
    
    @classmethod
    def beginner(cls, **kwargs):
        """Create AutoML configured for beginners."""
        from .presets import get_preset
        config = get_preset('beginner')
        config.update(kwargs)  # Allow overrides
        return cls(**config)
    
    @classmethod
    def production(cls, **kwargs):
        """Create AutoML configured for production."""
        from .presets import get_preset
        config = get_preset('production')
        config.update(kwargs)
        return cls(**config)
    
    @classmethod
    def fast(cls, **kwargs):
        """Create AutoML configured for fast iteration."""
        from .presets import get_preset
        config = get_preset('fast')
        config.update(kwargs)
        return cls(**config)
    
    def summary(self):
        """Print a summary of analysis results."""
        print("\n" + "=" * 70)
        print("Octolearn Pipeline Summary")
        print("=" * 70)
        
        # Profile summary
        if self.profile_:
            print(f"\n📊 Dataset Profile:")
            print(f"  Rows: {self.profile_['n_rows']}")
            print(f"  Columns: {self.profile_['n_columns']}")
            print(f"  Task: {self.profile_['task_type']}")
            print(f"  Numeric: {len(self.profile_['numeric_features'])}")
            print(f"  Categorical: {len(self.profile_['categorical_features'])}")
        
        # Risk score
        if self.risk_score_:
            print(f"\n⚠️  Risk Assessment:")
            print(f"  Score: {self.risk_score_['score']:.0f}/100")
            print(f"  Category: {self.risk_score_['category']}")
        
        # Models
        if self.trained_models_:
            print(f"\n🤖 Model Results:")
            print(f"  Best: {self.best_model_}")
            print(f"  Models trained: {len(self.trained_models_)}")
        
        print("\n" + "=" * 70 + "\n")
    
    def to_dict(self) -> dict:
        """Export all results as dictionary."""
        return {
            'profile': self.profile_,
            'risk_score': self.risk_score_,
            'outliers': self.outlier_results_,
            'feature_importance': self.feature_importance_,
            'cleaning_log': self.cleaning_log_,
            'best_model': self.best_model_,
            'trained_models': self.trained_models_,
        }
    
    def to_json(self, filepath: str):
        """Export results to JSON file."""
        import json
        import pickle
        
        results = self.to_dict()
        
        # Convert non-serializable objects
        serializable = {
            'profile': results['profile'],
            'risk_score': results['risk_score'],
            'feature_importance': results['feature_importance'],
            'cleaning_log': results['cleaning_log_'],
        }
        
        with open(filepath, 'w') as f:
            json.dump(serializable, f, indent=2, default=str)
        
        print(f"✅ Results exported to {filepath}")
```

### Usage:

```python
# Convenience constructors
automl = AutoML.beginner()
automl = AutoML.production()
automl = AutoML.fast()

# Summary output
automl.fit(X, y)
automl.summary()  # Prints nicely formatted summary

# Export results
results = automl.to_dict()
automl.to_json('results.json')  # Save for analysis
```

---

## Implementation 5: Example Scripts to Create

### Create: `examples/01_iris_classification.py`

```python
"""
Simple classification example with Iris dataset.
Perfect for beginners learning Octolearn.
"""

from octolearn import AutoML
from sklearn.datasets import load_iris
import pandas as pd

# Load data
iris = load_iris(as_frame=True)
X = iris.data
y = iris.target

print("🔗 Octolearn Example 1: Iris Classification")
print("=" * 60)

# Create AutoML with beginner preset
automl = AutoML(preset='beginner')

# Fit pipeline
print("\n📊 Fitting Octolearn pipeline...")
automl.fit(X, y)

# Get results
print("\n📈 Dataset Profile:")
profile = automl.report()
print(f"  Task: {profile['task_type']}")
print(f"  Rows: {profile['n_rows']}")
print(f"  Columns: {profile['n_columns']}")

# Risk assessment
print("\n⚠️ Risk Score:")
risk = automl.get_risk_score()
print(f"  Score: {risk['score']:.0f}/100")
print(f"  Category: {risk['category']}")

# Feature importance
print("\n⭐ Feature Importance:")
importance = automl.get_feature_importance()
for feat, score in list(importance.items())[:3]:
    print(f"  {feat}: {score:.4f}")

print("\n✅ Done!")
```

### Create: `examples/02_housing_regression.py`

```python
"""
Regression example with housing price prediction.
Demonstrates handling continuous targets.
"""

from octolearn import AutoML
from sklearn.datasets import load_diabetes
import pandas as pd

# Load data
diabetes = load_diabetes(as_frame=True)
X = diabetes.data
y = diabetes.target

print("🔗 Octolearn Example 2: Housing Regression")
print("=" * 60)

# Use production preset
automl = AutoML(preset='production')

# Fit pipeline (all phases)
print("\n🚀 Fitting complete pipeline...")
automl.fit(X, y)

# Train models
print("🤖 Training models...")
results = automl.train_auto_models()

# Get best model
best = automl.get_best_model()
print(f"\n✅ Best model: {type(best).__name__}")

# Make predictions
preds = best.predict(X.head(5))
print(f"Predictions (first 5): {preds}")

print("\n✅ Done!")
```

### Create: `examples/03_custom_pipeline.py`

```python
"""
Custom pipeline configuration example.
Shows how to control each phase independently.
"""

from octolearn import AutoML
from seaborn import load_dataset

# Load data
titanic = load_dataset('titanic')
X = titanic.drop('survived', axis=1)
y = titanic['survived']

print("🔗 Octolearn Example 3: Custom Pipeline")
print("=" * 60)

# Use custom config: profile only, no training
print("\n1️⃣ Creating AutoML for analysis only...")
automl_eda = AutoML(
    detect_outliers=True,       # Detect outliers
    analyze_interactions=True,  # Analyze features
    auto_clean=True,           # Clean data
    train_models=False,        # No training
    show_progress=True
)

automl_eda.fit(X, y)

# Get insights
print("\n📊 Analysis Results:")
profile = automl_eda.report()
risk = automl_eda.get_risk_score()
print(f"  Risk Score: {risk['score']:.0f}/100")

# Outliers
outliers = automl_eda.get_outlier_analysis()
print(f"  Outliers detected: {len(outliers) if outliers else 0}")

print("\n✅ Done!")
```

---

## Development Timeline

### Week 1: Core Infrastructure
- [ ] Create `octolearn/presets.py`
- [ ] Create `octolearn/validation.py`
- [ ] Update `AutoML.__init__()` to use presets
- [ ] Add convenience class methods

### Week 2: Enhanced Features
- [ ] Improve logging in `fit()` method
- [ ] Add `summary()`, `to_dict()`, `to_json()` methods
- [ ] Update error messages
- [ ] Add data validation with helpful warnings

### Week 3: Examples & Testing
- [ ] Create 5 example scripts
- [ ] Write unit tests for validation
- [ ] Write tests for presets
- [ ] Update README with presets
- [ ] Create EXAMPLES.md guide

### Testing Checklist
- [ ] All presets load correctly
- [ ] Validation catches bad data
- [ ] Error messages are helpful
- [ ] Examples run without errors
- [ ] Backwards compatibility maintained

---

## Documentation Changes

### Update: README.md

Add preset section:

```markdown
## Quick Start with Presets

### For Beginners (Learning ML):
```python
from octolearn import AutoML

automl = AutoML(preset='beginner')
automl.fit(X, y)
profile = automl.report()  # Just profiling, no model training
```

### For Production (All Features):
```python
automl = AutoML(preset='production')
automl.fit(X, y)
results = automl.train_auto_models()
model = automl.get_best_model()
```

### For Fast Iteration (Speed-Optimized):
```python
automl = AutoML(preset='fast')
automl.fit(X, y)
```

### See More Options:
```python
from octolearn.presets import list_presets()
print(list_presets())
```
```

---

## Backwards Compatibility Note

✅ **All changes are backwards compatible!**

Existing code like:
```python
automl = AutoML(use_full_data=False, sample_size=500)
```

Will continue to work exactly as before. Presets are optional.

---

## Success Metrics

After implementing these changes:

1. **UX Improvement:** 80% of beginners should be able to run `AutoML(preset='beginner')` without confusion
2. **Adoption:** Clearer documentation should increase GitHub stars and PyPI downloads
3. **Support Load:** Better error messages should reduce GitHub issues
4. **Code Quality:** Validation should catch data problems before they waste user time

---

## Next Steps

1. **Discuss with team** on implementation details
2. **Create feature branch** for v0.7.0 development
3. **Implement in order:** Presets → Validation → Logging → Examples
4. **Test thoroughly** with real users
5. **Release v0.7.0** with announcement

---

**This roadmap transforms Octolearn from "powerful but intimidating" to "easy to use with room for experts." That's the sweet spot!** 🐙✨
