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ML Portfolio

12 projects with interactive demos. Click, interact, and see model outputs — predictions, visualizations, and scores — not code.

Organized by learning paradigm for teams evaluating data science depth.

Supervised Learning

Outcome prediction, risk scoring, forecasting — models trained on labeled data to drive measurable business decisions.

supervisedLive Demo

Customer Churn Prediction Engine

Toggle customer features and watch churn probability update in real time with confusion matrix, ROC curve, and feature importance.

89%

Accuracy

87%

Precision

91%

Recall

0.89

F1 Score

XGBoostscikit-learnSMOTEFeature EngineeringPlotly
supervisedLive Demo

Real-Time Fraud Scoring API

Submit a sample transaction and get a fraud risk score with SHAP waterfall explanation and precision-recall tradeoff.

0.91

PR-AUC

-34%

False Positives

-41%

Review Time

<120ms

Inference Latency

XGBoostLightGBMSHAPFastAPIDocker+1
supervisedLive Demo

Loan Default Risk Scorer

Mini form: input applicant details → instant approval probability, risk tier, and fairness metrics across demographics.

0.94

AUC-ROC

72%

Approval Rate

<3%

Fairness Gap

<50ms

Latency

scikit-learnSHAPFairlearnLogistic RegressionRandom Forest

Unsupervised Learning

Anomaly detection, segmentation, pattern discovery — extracting structure when labels are limited or unavailable.

unsupervisedLive Demo

Customer Segmentation Intelligence

Interactive 3D cluster visualization — hover over points to see customer profiles with elbow plots and silhouette scores.

7

Segments

0.62

Silhouette

+19%

Campaign CTR

+14%

CAC Efficiency

K-MeansDBSCANPCAt-SNEPlotly 3D+1
unsupervisedLive Demo

Network Anomaly Detection System

Live-style feed showing normal vs. anomalous data points flagged in real time with threshold tuning slider.

94%

Detection Accuracy

<100ms

Latency

10K+

Sensors

5M+

Events/Day

Isolation ForestApache SparkStreamingONNXAWS
unsupervisedLive Demo

Whale Tracker — Crypto Behavior Analysis

Visual map of whale wallet movements with behavioral clustering, temporal heatmaps, and outlier identification.

78%

Forecast Accuracy

1000+

Data Points/Min

11+

API Integrations

<200ms

Response Time

DBSCANTensorFlow.jsWebSocketsNetworkXNode.js+1

Deep Learning & NLP

Neural networks, transformers, RAG pipelines, agent orchestration — advanced architectures for language, sequence, and multi-modal tasks.

Deep Learning & NLP

DataFlow Hub — Enterprise ML Platform

Full-stack ML platform with FRED integration (800K+ datasets), GPT-4 AI assistant, and Docker orchestration.

15+

Docker Services

800K+

Datasets

25+

API Endpoints

6

Data Sources

FastAPIPostgreSQLDockerRAGFRED API+3
Deep Learning & NLPLive Demo

Transformer Sentiment & Intent Classifier

Type text → real-time sentiment + intent classification with attention heatmap and confidence distribution.

92%

Accuracy

1000/min

Processing

5

Languages

3

Classes

BERTPyTorchHugging FaceTransformersONNX
Deep Learning & NLPLive Demo

Multi-Agent Research Assistant

Enter a research question → watch agents search, reason, and synthesize an answer with cited sources.

94%

Answer Quality

5-12

Sources Cited

3-7

Agent Steps

<8s

Response Time

LangGraphLangChainChromaDBOpenAIFastAPI+1
Deep Learning & NLPLive Demo

Conversational RAG with Knowledge Graph

Chat over documents — see retrieved chunks, knowledge graph traversal path, and answers with inline citations.

0.89

Retrieval MRR

91%

Answer Accuracy

50K+

KG Entities

<3s

Latency

LangChainNeo4jFAISSHugging FaceKnowledge Graph
Deep Learning & NLPLive Demo

Stock Price Forecasting Dashboard

Select a ticker to see predicted vs. actual prices with confidence intervals and interactive candlestick charts.

$2.01

RMSE

$1.54

MAE

3.2%

MAPE

0.964

LSTMTensorFlowProphetyfinancePlotly
Deep Learning & NLPLive Demo

AdaptML — Live Model Training & Inference Playground

Upload any dataset — CSV, PDF, images, JSON. The system auto-detects the task, trains a model in real time with a live loss curve, and lets you query it instantly with built-in SHAP/attention explainability.

5

Data Modalities

96%

Auto-Detect Accuracy

<30s

Train → Inference

100%

Explainability

PythonPyTorchscikit-learnXGBoostHugging Face+11

Want a deeper walkthrough?

I can walk through model design choices, tradeoffs, deployment architecture, and the measured impact from each project.