automated-ml

AI platform for automated research paper analysis, knowledge extraction, and ML experimentation with SHAP explainability and PDF report generation.

Automated‑ML automates training and evaluating machine learning models on tabular data with minimal setup.

AutoClean AI — An automated data preprocessing system that transforms messy datasets into clean, model-ready data with intelligent profiling, preprocessing, recommendations, and a transparent audit trail.

Autonomous multi-agent Data Science pipeline — upload a CSV, get a trained model, EDA charts, and an executive report. Zero manual intervention. Built with CrewAI · FastAPI · React · XGBoost · WebSockets.

An automated-ML library that automates model training completely using simple APIs. Moreover, it provides curated data analysis modules for preprocessing, anomaly/outlier removal, sanity check(bias-variance tradeoffs), data splitter and explainability of model predictions with visualizations.

Dataset Auto-Diagnosis Python Library — detect and fix data quality issues (leakage, skewness, outliers, imbalance) before model training.

My first startup failed after corporate life... still best decision I ever made (I will not promote)

Automated customer churn prediction pipeline with feature selection, model comparison, and deployment-ready output. Python + scikit-learn.

Automated end-to-end MLOps pipeline for predicting customer purchase likelihood of a wellness tourism package, enabling data-driven marketing through CI/CD-enabled model training and deployment.

An algorithmic bias audit and fairness evaluation of an emergency department clinical triage model using Azure Machine Learning and macro-balanced mitigation strategies.

Neural Architecture Search using Differential Evolution

Recommend products based on user behavior

Classify customer churn (yes/no) from usage and account features (classification).

Trust & Safety risk analysis system detecting account takeover, credential stuffing and data exfiltration in 50K+ user activity logs. Statistical analysis + ML scoring (ROC-AUC: 0.9998, Precision@10: 1.0). CI/CD via Azure DevOps.

Predict whether a news article is real or fake.

Reproducible ML research & evaluation harness for financial time series: time-aware validation, PyTorch models, cost-aware metrics, experiment contracts and audit gates.

Classify wine quality from physicochemical properties

[D] Why Causality Matters for Production ML: Moving Beyond Correlation

Classify SMS or email messages as spam or not spam using text content, word frequency, and message m...

Classify emails as spam or not spam using NLP techniques