Initial commit: organized project structure for student handoff

Reorganized flat 41-file directory into structured layout with:
- scripts/ for Python analysis code with shared config.py
- notebooks/ for Jupyter analysis notebooks
- data/ split into raw/, metadata/, processed/
- docs/ with analysis summary, experimental design, and bimodal hypothesis tutorial
- tasks/ with todo checklist and lessons learned
- Comprehensive README, PLANNING.md, and .gitignore

Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>

scripts/ml_classification.py

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+import pandas as pd
+import numpy as np
+from sklearn.model_selection import train_test_split, cross_val_score
+from sklearn.ensemble import RandomForestClassifier
+from sklearn.linear_model import LogisticRegression
+from sklearn.metrics import accuracy_score, classification_report, confusion_matrix
+from sklearn.preprocessing import StandardScaler
+from sklearn.impute import SimpleImputer
+import matplotlib.pyplot as plt
+import seaborn as sns
+
+from config import DATA_PROCESSED, FIGURES
+
+# Load data
+trained_distances = pd.read_csv(DATA_PROCESSED / 'trained_distances.csv')
+untrained_distances = pd.read_csv(DATA_PROCESSED / 'untrained_distances.csv')
+
+# Add group labels
+trained_distances['group'] = 'trained'
+untrained_distances['group'] = 'untrained'
+
+# Combine data
+combined_data = pd.concat([trained_distances, untrained_distances], ignore_index=True)
+combined_data = combined_data.dropna(subset=['group'])
+
+# Prepare features and target
+features = ['distance', 'n_flies', 'area_fly1', 'area_fly2']
+X = combined_data[features]
+y = combined_data['group']
+
+# Handle missing values in features
+imputer = SimpleImputer(strategy='mean')
+X_imputed = pd.DataFrame(imputer.fit_transform(X), columns=features)
+
+# Split data
+X_train, X_test, y_train, y_test = train_test_split(X_imputed, y, test_size=0.2, random_state=42)
+
+# Standardize features
+scaler = StandardScaler()
+X_train_scaled = scaler.fit_transform(X_train)
+X_test_scaled = scaler.transform(X_test)
+
+print("=== MACHINE LEARNING CLASSIFICATION ===")
+print(f"Training set size: {len(X_train)}")
+print(f"Testing set size: {len(X_test)}")
+
+# 1. Logistic Regression
+print("\n1. Logistic Regression:")
+lr_model = LogisticRegression(random_state=42)
+lr_model.fit(X_train_scaled, y_train)
+lr_predictions = lr_model.predict(X_test_scaled)
+lr_accuracy = accuracy_score(y_test, lr_predictions)
+print(f"Accuracy: {lr_accuracy:.4f}")
+print("\nClassification Report:")
+print(classification_report(y_test, lr_predictions))
+
+# 2. Random Forest
+print("\n2. Random Forest:")
+rf_model = RandomForestClassifier(n_estimators=100, random_state=42)
+rf_model.fit(X_train, y_train)
+rf_predictions = rf_model.predict(X_test)
+rf_accuracy = accuracy_score(y_test, rf_predictions)
+print(f"Accuracy: {rf_accuracy:.4f}")
+print("\nClassification Report:")
+print(classification_report(y_test, rf_predictions))
+
+# Feature importance
+print("\nFeature Importance (Random Forest):")
+feature_importance = pd.DataFrame({
+    'feature': features,
+    'importance': rf_model.feature_importances_
+}).sort_values('importance', ascending=False)
+print(feature_importance)
+
+# Confusion matrix for the best model
+best_model_name = "Random Forest" if rf_accuracy > lr_accuracy else "Logistic Regression"
+best_predictions = rf_predictions if rf_accuracy > lr_accuracy else lr_predictions
+
+plt.figure(figsize=(8, 6))
+cm = confusion_matrix(y_test, best_predictions)
+sns.heatmap(cm, annot=True, fmt='d', cmap='Blues',
+            xticklabels=['Trained', 'Untrained'],
+            yticklabels=['Trained', 'Untrained'])
+plt.title(f'Confusion Matrix - {best_model_name}')
+plt.xlabel('Predicted')
+plt.ylabel('Actual')
+plt.tight_layout()
+plt.savefig(FIGURES / 'confusion_matrix.png', dpi=300, bbox_inches='tight')
+plt.show()
+
+# Cross-validation scores
+print("\n=== CROSS-VALIDATION SCORES ===")
+lr_cv_scores = cross_val_score(LogisticRegression(random_state=42), X_train_scaled, y_train, cv=5)
+rf_cv_scores = cross_val_score(RandomForestClassifier(n_estimators=100, random_state=42), X_train, y_train, cv=5)
+
+print(f"Logistic Regression CV Score: {lr_cv_scores.mean():.4f} (+/- {lr_cv_scores.std() * 2:.4f})")
+print(f"Random Forest CV Score: {rf_cv_scores.mean():.4f} (+/- {rf_cv_scores.std() * 2:.4f})")