Browse Projects

Generates anime faces using a DCGAN trained on 63,000 images. Every face is produced from random noise — none of them exist. Trained on Google Colab T4 GPU. Includes latent space interpolation showing smooth morphing between faces.

1970s Boston house price predictor. Linear regression on 13 features with log-transformed target (test r²=0.74). Live slider UI on Azure; full analysis in rendered notebook.

Semantic CV vs job description analysis identifying missing skills and producingactionable gap recommendations. HuggingFace embeddings for match scoring, Pineconefor a searchable job library, ROUGE for ATS keyword alignment. Accepts jobdescriptions as text, PDF, or URL via trafilatura extraction.

Credit risk scoring API on the German Credit dataset. Three classifiers benchmarked; Logistic Regression wins by cost-weighted score. FastAPI serves predictions with plain-language explanations. Deployed on Azure via GitHub Actions CI/CD.

A production-grade, class-based data preprocessing pipeline built in Python on the NYC Airbnb Open Dataset (48,895 listings). Handles missing values, outliers, duplicates, encoding, and scaling — then generates five before/after diagnostic visualisations.

Draw a digit on the canvas — the CNN predicts it in real time. Trained on 60,000 MNIST images. Confidence score and probability bar chart across all 10 digits. Live on Azure App Service.

T5-small fine-tuned on the AESLC corpus (14k Enron email/subject pairs) to generate professional subject lines from an email body. Five suggestions returned at varying temperature levels — from beam-search conservative to top-p creative sampling.

Upload two CSV columns, select a test, get a plain-English verdict. Five hypothesis tests with automatic assumption checking. Datasets above 50,000 rows route through PySpark automatically.

PDF document summarizer using Amazon Bedrock (Claude Haiku) with production LLMOps patterns: versioned YAML prompt templates, automated ROUGE evaluation, Bedrock Guardrails for PII redaction, and MLflow tracking of every inference call. Deployed on Azure App Service.

Detects the language of any text across 22 languages using character n-gram TF-IDF features. Returns the top 3 languages with confidence scores and a plain-English explanation of the character patterns driving the detection.

Four-stage PySpark ETL pipeline on 40M+ NYC 311 service requests. Identical pandas pipeline for direct comparison. Benchmarked at 100k, 1M, and 10M rows — showing the exact crossover where Spark's parallelism overcomes its initialisation overhead.

SMS spam detection comparing three approaches — Naive Bayes, SVM, and Amazon Bedrock (Claude Haiku zero-shot) — on the same test set. Every prediction comes with a plain-English explanation of the words that drove it. Class imbalance addressed explicitly. Deployed on Azure App Service.

Stacked LSTM for stock trend pattern detection, served via Amazon SageMaker Real-Time Endpoint. Flask frontend on Azure App Service — multi-cloud MLOps. Pattern detection only, not financial advice.

Churn prediction with plain-language risk explanations a retention team can act on. Logistic Regression wins (ROC-AUC 0.8362), benchmarked against Amazon Bedrock Claude Haiku 4.5 zero-shot — tied at 70%. Flask API on Azure.