Problem

ROADMAP.md and README.md are outdated and don't reflect the current state of v0.4.1 which includes the complete TOP 10 ML algorithms implementation.

Current Status (v0.4.1)

The project has successfully completed:

• ✅ All TOP 10 ML algorithms ([TOP 10] Implement K-Nearest Neighbors (kNN) Classifier #23-26, Implement Isolation Forest for Anomaly Detection #17-22, Implement graph algorithms and descriptive statistics #9, Implement PCA (Principal Component Analysis) for Dimensionality Reduction #13)
• ✅ Graph algorithms (betweenness centrality, PageRank, community detection)
• ✅ Statistics module (descriptive statistics, histograms, Apriori)
• ✅ Advanced clustering (DBSCAN, Hierarchical, GMM, Spectral)
• ✅ Anomaly detection (Isolation Forest, LOF)
• ✅ Dimensionality reduction (PCA, t-SNE)
• ✅ 683 passing tests
• ✅ Zero clippy warnings
• ✅ Comprehensive documentation book

Updates Needed

1. ROADMAP.md

Current state: Shows v0.4.0 as "Planned" with unchecked classification items
Needed changes:

• Mark v0.4.0 as ✅ Released
• Check all completed items in v0.4.0 section:
  • Logistic regression
  • Support Vector Machines (SVM)
  • Naive Bayes (GaussianNB)
  • K-Nearest Neighbors (kNN)
  • Gradient Boosting Machine (GBM)
  • Metrics: accuracy, precision, recall, F1, confusion_matrix
• Add v0.4.1 section documenting:
  • Graph algorithms (Issue Implement graph algorithms and descriptive statistics #9)
  • Advanced clustering (DBSCAN, Hierarchical, GMM, Spectral)
  • Anomaly detection (Isolation Forest, LOF)
  • Dimensionality reduction (PCA, t-SNE)
  • Community detection (Louvain)
  • Association rules (Apriori)
• Update Quality Metrics Achieved section with current stats:
  • Total Tests: 683 passing
  • Zero SATD violations (except 1 low-priority Bayesian Blocks TODO)
  • Zero clippy warnings

2. README.md

Current state: Lists only basic algorithms, shows v0.4.0
Needed changes:

• Update version in Installation section: aprender = "0.4.1"
• Expand "Machine Learning Models" section to include:
  • Classification: LogisticRegression, DecisionTree, RandomForest, GradientBoosting, NaiveBayes, kNN, SVM
  • Clustering: KMeans, DBSCAN, HierarchicalClustering, GMM, SpectralClustering
  • Anomaly Detection: IsolationForest, LocalOutlierFactor
  • Dimensionality Reduction: PCA, TSNE
  • Association Rules: Apriori
• Add "Graph Algorithms" section:
  • Betweenness Centrality
  • PageRank
  • Community Detection (Louvain)
• Add "Statistics" section:
  • Descriptive statistics (mean, median, std, quartiles)
  • Histograms with multiple binning strategies
• Update Metrics section:
  • Add classification metrics: accuracy, precision, recall, f1_score, confusion_matrix
• Update badges if needed (test count, TDG score)

3. Cargo.toml keywords

Current: ["machine-learning", "ml", "regression", "clustering", "tree-models"]
Suggested: Add "classification", "statistics", "graph-algorithms"

Acceptance Criteria

• ROADMAP.md accurately reflects all completed work through v0.4.1
• README.md showcases all TOP 10 algorithms + graph + stats
• Version numbers updated to 0.4.1 throughout
• Quality metrics updated with current test counts
• Examples section mentions the comprehensive book
• All checkboxes in ROADMAP reflect actual completion status

Priority

High - Documentation accuracy is critical for new users and contributors

Related

• Completed TOP 10 issues: [TOP 10] Implement K-Nearest Neighbors (kNN) Classifier #23, [TOP 10] Implement Support Vector Machine (SVM) #24, [TOP 10] Implement Naive Bayes Classifier #25, [TOP 10] Implement Gradient Boosting Machine (GBM) #26
• Completed advanced features: Implement Isolation Forest for Anomaly Detection #17-22, Implement graph algorithms and descriptive statistics #9, Implement PCA (Principal Component Analysis) for Dimensionality Reduction #13