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applied-ml

Here are 58 public repositories matching this topic...

This article explores the theory behind explainable car pricing using value decomposition, showing how machine learning models can break a predicted price into intuitive components such as brand premium, age depreciation, mileage influence, condition effects, and transmission or fuel-type adjustments.

  • Updated Oct 8, 2026
  • Python

Advanced sentiment analysis lab with TF-IDF word/char features, leakage-safe multi-model training, ROC/PR-AUC evaluation, calibration, cost-aware threshold tuning, error analysis, explainability, and live prediction.

  • Updated Sep 24, 2026
  • Python

Experimental web application demonstrating how an offline-trained financial fraud detection model can be exposed through a web interface. Built with Flask and a pre-trained XGBoost model to showcase ML inference flow, feature engineering, and result communication — not a production fraud prevention system.

  • Updated Apr 18, 2026
  • HTML

A curated collection of applied machine learning projects, ranging from core ML fundamentals to deep learning, reinforcement learning, NLP, and computer vision. Built as a professional learning portfolio demonstrating growth, experimentation, and real‑world problem‑solving.

  • Updated Sep 1, 2026
  • Jupyter Notebook

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