Machine Learning
Machine Learning Algorithms
Logistic Regression & Iris Classification
This dual-machine-learning project showcases two complete classification pipelines:
A systematic approach to modeling and evaluating classification problems using real-world datasets.
A comparative analysis between Random Forest and Decision Tree models on the classic Iris dataset.
This project emphasizes end-to-end ML workflows, model interpretability, and deployment readiness.
Data Ingestion → Preprocessing → Feature Engineering → Model Training → Evaluation → Visualization → Model Persistence
Accuracy, Precision, Recall, F1-score, ROC Curve, Confusion Matrix, Feature Importance
Core programming language
Data manipulation
Numerical computing
Machine learning library
Achieved 91% accuracy in classification tasks
Random Forest showed superior classification and generalization compared to Decision Tree
Add real-time model visualization capabilities
Explore AdaBoost and Gradient Boosting techniques
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