{"owner":"AmirhosseinHonardoust","github":"https://github.com/AmirhosseinHonardoust","claimed":false,"inventory":[],"indexed":[{"repo":"AmirhosseinHonardoust/Fake-News-Detector","github":"https://github.com/AmirhosseinHonardoust/Fake-News-Detector","description":"A professional TF-IDF + Logistic Regression style-risk classifier for educational fake-news detection, with a Streamlit dashboard, honest evaluation, uncertainty handling, and leakage analysis.","language":"Python","stars":148,"topics":["ai-project","data-science","data-visualization","fake-news-detection","kaggle-dataset","logistic-regression","machine-learning","misinformation","news-analysis","nlp"],"license":"MIT","category":"machine-learning"},{"repo":"AmirhosseinHonardoust/Coffee-Shop-Profit-Predictor","github":"https://github.com/AmirhosseinHonardoust/Coffee-Shop-Profit-Predictor","description":"Predict the profitability of potential coffee shop locations using SQL and Python. Combines data engineering with feature-rich regression modeling, visual analytics, and business insights to support data-driven site selection and retail decision-making.","language":"Python","stars":41,"topics":["business-intelligence","data-science","data-visualization","elasticnet","feature-engineering","machine-learning","portfolio-project","predictive-modeling","profit-prediction","python"],"license":"MIT","category":"machine-learning"},{"repo":"AmirhosseinHonardoust/Sentiment-Analysis-BERT","github":"https://github.com/AmirhosseinHonardoust/Sentiment-Analysis-BERT","description":"End-to-end sentiment analysis of tweets using BERT. Includes preprocessing, training, and evaluation with classification reports, confusion matrices, ROC curves, and word clouds. Demonstrates fine-tuning of transformer models for text classification with modular, reproducible code.","language":"Python","stars":38,"topics":["bert","deep-learning","huggingface","machine-learning","nlp","pytorch","sentiment-analysis","text-classification","transformers","twitter"],"license":"MIT","category":"machine-learning"},{"repo":"AmirhosseinHonardoust/Stock-LSTM-Forecasting","github":"https://github.com/AmirhosseinHonardoust/Stock-LSTM-Forecasting","description":"Predict stock prices using LSTM networks in PyTorch. This project covers data preprocessing, sliding window creation, model training with early stopping, and evaluation with RMSE/MAE/MAPE. Includes visualizations of training loss, predicted vs actual prices, and short-horizon forecasts.","language":"Python","stars":38,"topics":["data-science","deep-learning","finance","forecasting","lstm","machine-learning","neural-networks","portfolio-project","predictive-modeling","python"],"license":"MIT","category":"machine-learning"},{"repo":"AmirhosseinHonardoust/AI-Productivity-Tracker","github":"https://github.com/AmirhosseinHonardoust/AI-Productivity-Tracker","description":"Analyze and predict daily productivity using SQL, machine learning, and psychology. This project combines behavioral data, circadian rhythm analysis, and ElasticNet regression to model focus, stress, and performance, transforming work patterns into actionable insights.","language":"Python","stars":35,"topics":["behavioral-analytics","data-science","data-visualization","elasticnet","feature-engineering","human-performance","machine-learning","mental-health","portfolio-project","predictive-modeling"],"license":"MIT","category":"machine-learning"},{"repo":"AmirhosseinHonardoust/Employee-Performance-Analytics","github":"https://github.com/AmirhosseinHonardoust/Employee-Performance-Analytics","description":"Analyze employee performance and productivity using SQL and Python. Build HR dashboards to evaluate departmental KPIs, efficiency trends, and workload balance. Generates actionable insights through data visualization and KPI aggregation for business decision-making.","language":"Python","stars":33,"topics":["analytics-pipeline","business-intelligence","data-analysis","data-storytelling","data-visualization","employee-performance","hr-analytics","kpi-dashboard","performance-metrics","portfolio-project"],"license":"MIT","category":"analytics"},{"repo":"AmirhosseinHonardoust/AI-Personal-Study-Tracker","github":"https://github.com/AmirhosseinHonardoust/AI-Personal-Study-Tracker","description":"An AI-driven productivity tracking app built with Python, Streamlit, SQLite, and Machine Learning. It logs and analyzes study sessions, predicts productivity using Random Forest models, and visualizes key insights to help learners improve focus, habits, and overall academic efficiency.","language":"Python","stars":31,"topics":["ai","data-analytics","data-visualization","education","learning-analytics","machine-learning","productivity","python","random-forest","self-improvement"],"license":"MIT","category":"machine-learning"},{"repo":"AmirhosseinHonardoust/Fraud-Detection-SQL-Supervised","github":"https://github.com/AmirhosseinHonardoust/Fraud-Detection-SQL-Supervised","description":"Detect and classify fraudulent transactions using SQL and Python. Generate behavioral features with SQLite, train a Logistic Regression model, and evaluate performance with AUC, precision, recall, and ROC analysis. A complete supervised fraud detection workflow.","language":"Python","stars":31,"topics":["data-analysis","data-science","financial-analytics","fraud-detection","logistic-regression","machine-learning","model-evaluation","portfolio-project","python","roc-curve"],"license":"MIT","category":"machine-learning"},{"repo":"AmirhosseinHonardoust/Demand-Forecasting","github":"https://github.com/AmirhosseinHonardoust/Demand-Forecasting","description":"End-to-end demand forecasting with Python using synthetic time-series sales data. Includes data generation, cleaning, ARIMA/SARIMA model selection by AIC, evaluation with RMSE and MAPE, and 90-day forecasts with confidence intervals. Reproducible scripts and visualizations for portfolio showcase.","language":"Python","stars":31,"topics":["arima","data-science","data-visualization","demand-forecasting","forecasting","machine-learning","portfolio-project","predictive-modeling","python","sales-data"],"license":"MIT","category":"machine-learning"},{"repo":"AmirhosseinHonardoust/Beyond-Charts-Interactive-Storytelling","github":"https://github.com/AmirhosseinHonardoust/Beyond-Charts-Interactive-Storytelling","description":"A comprehensive guide and codebase for building interactive storytelling dashboards with Python, Streamlit, and Plotly. Learn how to transform static analytics into dynamic, user-driven data experiences that engage and inspire, featuring RFM segmentation, cohort analysis, and real-world insights.","language":null,"stars":29,"topics":["business-intelligence","cohort-analysis","data-analytics","data-science","data-storytelling","data-visualization","interactive-dashboards","machine-learning","plotly","python"],"license":"MIT","category":"machine-learning"},{"repo":"AmirhosseinHonardoust/Sales-Insights-SQL","github":"https://github.com/AmirhosseinHonardoust/Sales-Insights-SQL","description":"Analyze retail sales data using SQL and Python. Build a SQLite database from CSV, run SQL queries for key KPIs (revenue, top products, AOV, trends), and visualize results with Matplotlib. A portfolio-ready project demonstrating SQL + data analytics + reporting automation.","language":"Python","stars":28,"topics":["analytics-projects","business-analytics","data-analysis","data-science","data-visualization","matplotlib","pandas","portfolio-project","python","retail-analytics"],"license":"MIT","category":"databases-storage"},{"repo":"AmirhosseinHonardoust/Sales-Data-Analysis","github":"https://github.com/AmirhosseinHonardoust/Sales-Data-Analysis","description":"Synthetic sales data analysis with Python. Generate realistic sales transactions, clean and validate data, compute KPIs, and visualize revenue trends by day, month, and category. Includes reproducible scripts and charts for portfolio demonstration.","language":"Python","stars":28,"topics":["business-intelligence","data-analysis","data-cleaning","data-science","data-visualization","kpi","matplotlib","portfolio-project","python","sales-data"],"license":"MIT","category":"analytics"},{"repo":"AmirhosseinHonardoust/Fake-Review-Detector","github":"https://github.com/AmirhosseinHonardoust/Fake-Review-Detector","description":"An AI-powered Fake Review Detector built with Python, Streamlit, and Scikit-learn. Uses TF-IDF vectorization, Logistic Regression, and behavioral text analytics (sentiment, exclamations, clichés) to identify synthetic or spammy product reviews. Includes training scripts and a full interactive dashboard.","language":"Python","stars":28,"topics":["ai-project","dashboard","data-science","data-visualization","fake-review-detection","logistic-regression","machine-learning","natural-language-processing","nlp","python"],"license":"MIT","category":"machine-learning"},{"repo":"AmirhosseinHonardoust/Data-Storytelling-Dashboard","github":"https://github.com/AmirhosseinHonardoust/Data-Storytelling-Dashboard","description":"A fully interactive data storytelling dashboard for e-commerce analytics. Built with Python, Streamlit, and Plotly, it transforms transactional data into actionable insights through KPIs, cohort retention, RFM segmentation, and global visualizations, perfect for analysts and data scientists.","language":"Python","stars":27,"topics":["business-intelligence","cohort-analysis","dashboard","data-analytics","data-science","data-storytelling","data-visualization","ecommerce-analytics","machine-learning","pandas"],"license":"MIT","category":"dashboards-admin"},{"repo":"AmirhosseinHonardoust/Fraud-Detection-SQL-Unsupervised","github":"https://github.com/AmirhosseinHonardoust/Fraud-Detection-SQL-Unsupervised","description":"Detect suspicious financial transactions using SQL and Python. Build user-level behavioral features in SQLite, apply Isolation Forest for anomaly detection, and visualize high-risk patterns. Demonstrates unsupervised fraud analytics and SQL-driven data science workflow.","language":"Python","stars":27,"topics":["anomaly-detection","banking-data","data-analysis","data-science","financial-analytics","fraud-detection","isolation-forest","machine-learning","portfolio-project","python"],"license":"MIT","category":"machine-learning"},{"repo":"AmirhosseinHonardoust/Anomaly-Detection","github":"https://github.com/AmirhosseinHonardoust/Anomaly-Detection","description":"Anomaly detection in synthetic transaction and sales data with Python. Generates realistic data, injects unusual events, and applies Isolation Forest, Local Outlier Factor, and Z-score methods to detect outliers. Produces anomaly reports and visualizations for portfolio-ready demonstration of data science skills.","language":"Python","stars":26,"topics":["anomaly-detection","data-science","data-visualization","fraud-detection","isolation-forest","local-outlier-factor","machine-learning","outlier-detection","portfolio-project","predictive-modeling"],"license":"MIT","category":"machine-learning"},{"repo":"AmirhosseinHonardoust/RAG-vs-Fine-Tuning","github":"https://github.com/AmirhosseinHonardoust/RAG-vs-Fine-Tuning","description":"A comprehensive, professional guide explaining the differences, strengths, and best practices of Retrieval-Augmented Generation (RAG) and Fine-Tuning for LLMs, including workflows, comparisons, decision frameworks, and real-world hybrid AI use cases.","language":null,"stars":25,"topics":["ai","ai-research","data-science","deep-learning","fine-tuning","generative-ai","hybrid-ai","langchain","large-language-models","llm"],"license":"MIT","category":"ai-agents"},{"repo":"AmirhosseinHonardoust/Community-Price-Tracker","github":"https://github.com/AmirhosseinHonardoust/Community-Price-Tracker","description":"A community-driven app for tracking and comparing the prices of everyday goods across cities. Built with Python, SQLite, Pandas, and Streamlit, it lets users log, visualize, and analyze item price trends, inflation, and cost-of-living differences with clear charts and full local data privacy.","language":"Python","stars":24,"topics":["community-project","cost-of-living","dashboard","data-analysis","data-visualization","economic-analysis","inflation-tracking","local-data","open-data","pandas"],"license":"MIT","category":"dashboards-admin"},{"repo":"AmirhosseinHonardoust/AI-Report-Factory","github":"https://github.com/AmirhosseinHonardoust/AI-Report-Factory","description":"AI Report Factory automates data storytelling, transforming raw business data into analytical, narrative-driven reports. It ingests structured datasets, computes KPIs, generates visualizations, and produces Markdown & HTML reports with actionable insights for startups, analysts, and enterprises.","language":"Python","stars":23,"topics":["analytics-pipeline","automation","business-intelligence","dashboard","data-analytics","data-reporting","data-science","data-storytelling","data-visualization","jinja2"],"license":"MIT","category":"workflow-automation"},{"repo":"AmirhosseinHonardoust/Smart-Contract-Risk-Analyzer","github":"https://github.com/AmirhosseinHonardoust/Smart-Contract-Risk-Analyzer","description":"A lightweight static analysis engine for Solidity smart contracts. Extracts code features, detects dangerous patterns (delegatecall, tx.origin, call.value), computes heuristic risk scores, and classifies contracts into Low/Medium/High risk levels. Includes multiple example vulnerabilities and a clean CLI for rapid security assessment.","language":"Solidity","stars":23,"topics":["audit-tools","blockchain-security","cli-tool","code-analysis","dapp-security","developer-tools","ethereum","evm","heuristic-model","python-project"],"license":"MIT","category":"blockchain-web3"},{"repo":"AmirhosseinHonardoust/Forecast-Factory","github":"https://github.com/AmirhosseinHonardoust/Forecast-Factory","description":"Forecast factory is an interactive AI-powered forecasting and simulation tool built with Python, Streamlit, Prophet, and SQL. It enables analysts to forecast business metrics, run what-if scenarios, and visualize results in real time, transforming predictive analytics into actionable strategic simulations.","language":"Python","stars":23,"topics":["ai","business-analytics","data-science","data-visualization","forecasting","interactive-dashboard","machine-learning","mlops","predictive-modeling","prophet"],"license":"MIT","category":"machine-learning"},{"repo":"AmirhosseinHonardoust/Content-KPI-Monitor","github":"https://github.com/AmirhosseinHonardoust/Content-KPI-Monitor","description":"A Streamlit + SQLite + Python dashboard for monitoring content performance KPIs. Tracks impressions, clicks, conversions, CTR, and conversion rates across categories and time. Includes an automated ETL from CSV → SQL view, interactive charts, and filters for data-driven content optimization.","language":"Python","stars":23,"topics":["analytics-dashboard","business-intelligence","content-analytics","dashboard","data-analysis","data-driven-decisions","data-visualization","etl-pipeline","kpi-tracking","marketing-analytics"],"license":"MIT","category":"dashboards-admin"},{"repo":"AmirhosseinHonardoust/Think-Like-a-Data-Scientist","github":"https://github.com/AmirhosseinHonardoust/Think-Like-a-Data-Scientist","description":"A thought-provoking article exploring how to think like a data scientist, without writing a single line of code. Covers the mental framework behind curiosity, structured reasoning, hypothesis testing, and data-driven storytelling, helping readers build analytical intuition beyond tools or syntax.","language":null,"stars":22,"topics":["ai-philosophy","analytics","career-advice","communication","critical-thinking","data-ethics","data-literacy","data-science","data-storytelling","experimentation"],"license":"MIT","category":"analytics"},{"repo":"AmirhosseinHonardoust/How-Streamlit-Makes-AI-Accessible","github":"https://github.com/AmirhosseinHonardoust/How-Streamlit-Makes-AI-Accessible","description":"A detailed educational article exploring how Streamlit revolutionizes AI app development. Learn how this Python framework bridges the gap between data science and usability, empowering anyone to deploy interactive machine learning models without front-end coding or complex infrastructure.","language":null,"stars":22,"topics":["data-science","machine-learning","python","streamlit","ai","ai-accessibility","dashboard","data-engineering","data-visualization","educational"],"license":"MIT","category":"machine-learning"},{"repo":"AmirhosseinHonardoust/The-Analysts-Mirror-Reflective-Dashboards","github":"https://github.com/AmirhosseinHonardoust/The-Analysts-Mirror-Reflective-Dashboards","description":"A professional exploration of reflective analytics, dashboards that evolve through user interaction. This project reimagines BI systems as adaptive, cognitive mirrors that learn analyst behavior, anticipate reasoning patterns, and transform data visualization into a collaborative, intelligent process.","language":null,"stars":22,"topics":["adaptive-systems","analytics-engineering","behavioral-analytics","business-intelligence","cognitive-computing","dashboard-design","data-analytics","data-science","data-visualization","human-in-the-loop"],"license":"MIT","category":"dashboards-admin"},{"repo":"AmirhosseinHonardoust/Onchain-Security-Suite","github":"https://github.com/AmirhosseinHonardoust/Onchain-Security-Suite","description":"A complete Web3 security toolkit combining AI-powered token auditing, ML-based deployer reputation scoring, and live Etherscan V2 data. Includes static analysis for rugpull detection, RandomForest reputation modeling, contract-fetching automation, and Solidity on-chain registries for transparent, reproducible security insights.","language":"Python","stars":22,"topics":["blockchain-security","cybersecurity","defi-security","erc20","etherscan-api","machine-learning","onchain-analytics","python","risk-scoring","rugpull-detection"],"license":"MIT","category":"security-tools"},{"repo":"AmirhosseinHonardoust/Customer-Sentiment-Intelligence-Platform","github":"https://github.com/AmirhosseinHonardoust/Customer-Sentiment-Intelligence-Platform","description":"An enterprise-grade NLP + Streamlit + SQL platform for analyzing customer feedback. Performs automated sentiment detection, stores labeled reviews in SQLite, and delivers real-time dashboards with probability insights to support business, marketing, and product optimization decisions.","language":"Python","stars":22,"topics":["community-project","cost-of-living","dashboard","data-analysis","data-visualization","economic-analysis","inflation-tracking","local-data","open-data","pandas"],"license":"MIT","category":"dashboards-admin"},{"repo":"AmirhosseinHonardoust/Financial-Fraud-Risk-Engine","github":"https://github.com/AmirhosseinHonardoust/Financial-Fraud-Risk-Engine","description":"A complete end-to-end fraud detection system for financial transactions, featuring data pipelines, cost-sensitive ML modeling, explainability with SHAP, threshold optimization, batch scoring, and an interactive Streamlit dashboard. Designed to simulate real-world fintech fraud-risk workflows.","language":"Python","stars":21,"topics":["anomaly-detection","cost-sensitive-learning","credit-card-fraud","data-pipeline","data-science","explainable-ai","financial-data","fintech","fraud-detection","imbalanced-data"],"license":"MIT","category":"data-pipelines"},{"repo":"AmirhosseinHonardoust/Synthetic-Data-Artist","github":"https://github.com/AmirhosseinHonardoust/Synthetic-Data-Artist","description":"A professional, research-grade comparison of Gaussian Copula and Variational Autoencoder (VAE) methods for synthetic tabular data generation. Includes full evaluation pipeline with distribution overlap, correlation analysis, PCA projections, pairplots, metrics, and automated visual reports.","language":"Python","stars":21,"topics":["synthetic-data","copula","correlation-analysis","data-augmentation","data-privacy","data-science","data-visualization","deep-learning","generative-model","machine-learning"],"license":"MIT","category":"machine-learning"},{"repo":"AmirhosseinHonardoust/ML-Playground-Autodetect","github":"https://github.com/AmirhosseinHonardoust/ML-Playground-Autodetect","description":"A Streamlit-powered machine learning playground that automatically detects classification or regression tasks, builds pipelines with preprocessing, trains models interactively, and visualizes metrics using Plotly. Backward-compatible, fully responsive, and deployable on Streamlit Cloud or Docker.","language":"Python","stars":21,"topics":["ai","auto-detect","classification","data-science","data-visualization","docker","huggingface","machine-learning","model-training","plotly"],"license":"MIT","category":"deployment-docker-iac"},{"repo":"AmirhosseinHonardoust/Market-IQ","github":"https://github.com/AmirhosseinHonardoust/Market-IQ","description":"MarketIQ is a full-stack Streamlit + SQL + Prophet dashboard for real-time business intelligence. It transforms raw sales data into KPIs, trend analytics, and six-month forecasts. With SQL-driven insights, dynamic filtering, and exportable reports, MarketIQ helps analysts visualize growth and predict performance instantly.","language":"Python","stars":21,"topics":["analytics-tools","business-intelligence","data-analytics","data-science","data-storytelling","data-visualization","forecasting","interactive-dashboard","machine-learning","plotly"],"license":"MIT","category":"machine-learning"},{"repo":"AmirhosseinHonardoust/Measuring-The-Soul-of-Data","github":"https://github.com/AmirhosseinHonardoust/Measuring-The-Soul-of-Data","description":"A narrative and technical exploration of data authenticity through the four pillars of synthetic data realism, Fidelity, Coverage, Privacy, and Utility. This thought-leadership piece combines storytelling, mathematics, and code to explain how these metrics define the ethical and functional “soul” of data in AI systems.","language":null,"stars":20,"topics":["ai-ethics","benchmark","copula","coverage","data-quality","data-science","diffusion","evaluation","explainable-ai","fidelity"],"license":"MIT","category":"analytics"},{"repo":"AmirhosseinHonardoust/How-AI-Detects-Rugpulls","github":"https://github.com/AmirhosseinHonardoust/How-AI-Detects-Rugpulls","description":"A deep technical article exploring how AI, feature engineering, and static smart-contract analysis uncover rugpull risks before humans detect them. Covers Solidity pattern mining, mint abuse detection, blacklist/fee manipulation signals, ML-inspired scoring models, and how to quantify ERC-20 token scam probability.","language":null,"stars":20,"topics":["ai-security","blockchain-security","cybersecurity","data-science","decentralized-finance","defi-security","erc20","feature-engineering","machine-learning","risk-scoring"],"license":"MIT","category":"security-tools"},{"repo":"AmirhosseinHonardoust/Cognitivelens-AI-Human-Comparison","github":"https://github.com/AmirhosseinHonardoust/Cognitivelens-AI-Human-Comparison","description":"CognitiveLens is a Streamlit-powered analytics tool for exploring alignment between human and AI decisions. It visualizes fairness, calibration, and interpretability through metrics like Cohen’s κ, AUC, and Brier score. Designed for ethical AI, bias auditing, and decision transparency in machine learning systems.","language":"Python","stars":20,"topics":["ai","analytics","bias-detection","data-science","ethics","explainable-ai","fairness","human-centered-ai","interpretability","machine-learning"],"license":"MIT","category":"machine-learning"},{"repo":"AmirhosseinHonardoust/Autocurator-Synthetic-Data-Benchmark","github":"https://github.com/AmirhosseinHonardoust/Autocurator-Synthetic-Data-Benchmark","description":"Autocurator is a comprehensive benchmarking toolkit for evaluating synthetic tabular data. It measures fidelity, coverage, privacy, and utility through quantitative metrics, visual reports, and PCA/correlation diagnostics. Ideal for validating VAE, GAN, Copula, or Diffusion-generated datasets.","language":"Python","stars":20,"topics":["benchmark","copula","coverage","data-privacy","data-quality","data-science","data-validation","deep-learning","diffusion","evaluation"],"license":"MIT","category":"machine-learning"},{"repo":"AmirhosseinHonardoust/Shap-Mini","github":"https://github.com/AmirhosseinHonardoust/Shap-Mini","description":"A minimal, reproducible explainable-AI demo using SHAP values on tabular data. Trains RandomForest or LogisticRegression models, computes global and local feature importances, and visualizes results through summary and dependence plots, all in under 100 lines of Python.","language":"Python","stars":20,"topics":["classification-model","data-science","data-visualization","explainable-ai","feature-importance","interpretable-machine-learning","logistic-regression","machine-learning","model-audit","model-explainability"],"license":"MIT","category":"machine-learning"},{"repo":"AmirhosseinHonardoust/ML-Meets-Etherscan-Realtime-Scanner","github":"https://github.com/AmirhosseinHonardoust/ML-Meets-Etherscan-Realtime-Scanner","description":"AI-powered real-time smart contract scanner that connects Machine Learning with Etherscan V2 to analyze newly deployed contracts instantly. Fetches verified Solidity code, performs static risk analysis, computes ML-driven deployer trust scores, and generates full security intelligence pipelines for Web3 threat detection.","language":null,"stars":19,"topics":["blockchain-security","contract-analysis","cybersecurity","defi-security","deployer-reputation","ethereum","etherscan","machine-learning","onchain-data","python"],"license":"MIT","category":"blockchain-web3"},{"repo":"AmirhosseinHonardoust/Economic-DNA-Repair","github":"https://github.com/AmirhosseinHonardoust/Economic-DNA-Repair","description":"A deep research study introducing the concept of Economic DNA Repair for smart contracts, designing self-correcting tokenomics that detect anomalies, repair unstable parameters, rebalance incentives, and restore economic equilibrium. Explores adaptive rewards, automated governance, liquidity healing, and resilience in decentralized systems.","language":null,"stars":19,"topics":["autonomous-systems","cryptoeconomics","decentralized-governance","defi-research","dynamic-parameters","economic-modeling","economic-resilience","economic-stability","evolutionary-economics","feedback-loops"],"license":"MIT","category":"blockchain-web3"},{"repo":"AmirhosseinHonardoust/ML-Powered-Token-Launch-Auditor","github":"https://github.com/AmirhosseinHonardoust/ML-Powered-Token-Launch-Auditor","description":"A hybrid Solidity + Python security toolkit that analyzes ERC-20 token contracts using static pattern extraction and ML-inspired scoring. Detects mint backdoors, blacklist controls, fee manipulation, trading locks, and rugpull mechanics. Outputs interpretable risk scores, labels, and structured features for deeper analysis.","language":"Solidity","stars":19,"topics":["audit-automation","blockchain-security","cryptocurrency","cybersecurity","decentralized-finance","defi-security","erc20","feature-engineering","ml-security","python"],"license":"MIT","category":"blockchain-web3"},{"repo":"AmirhosseinHonardoust/Missing-Data-Doctor","github":"https://github.com/AmirhosseinHonardoust/Missing-Data-Doctor","description":"Missing Data Doctor is a diagnostic and treatment toolkit for missing values in machine learning datasets. It profiles missingness patterns, visualizes gaps, applies multiple imputation strategies, and evaluates their impact on model performance. Includes automated plots, metrics, and a full HTML report.","language":"Python","stars":19,"topics":["automation","data-cleaning","data-pipelines","data-preprocessing","data-profiling","data-quality","data-reporting","data-science","data-visualization","eda"],"license":"MIT","category":"data-pipelines"},{"repo":"AmirhosseinHonardoust/Mobile-AI-Satisfaction-Behavior-Aanalysis","github":"https://github.com/AmirhosseinHonardoust/Mobile-AI-Satisfaction-Behavior-Aanalysis","description":"Deep behavioral and machine learning analysis explaining why mobile users systematically report lower satisfaction with AI systems. Includes SHAP explainability, cognitive load modeling, device-context effects, interaction metadata analysis, and end-to-end reproducible research code and visuals.","language":null,"stars":19,"topics":["behavioral-analytics","behavioral-science","cognitive-load","data-science","data-visualization","explainable-ai","feature-engineering","hci","human-ai-interaction","interaction-analysis"],"license":"MIT","category":"machine-learning"},{"repo":"AmirhosseinHonardoust/Python-Solidity-Feature-Engineering","github":"https://github.com/AmirhosseinHonardoust/Python-Solidity-Feature-Engineering","description":"A practical, research-friendly toolkit demonstrating how Python can read, parse, and analyze Solidity smart contracts using feature-engineering techniques. Extracts structural and security-relevant signals from Solidity code, detects risky patterns, builds interpretable features, and forms the basis for heuristic or ML-driven security analysis.","language":null,"stars":19,"topics":["blockchain","code-analysis","data-science","developer-tools","ethereum","evm","feature-engineering","machine-learning","python","risk-analysis"],"license":"MIT","category":"blockchain-web3"},{"repo":"AmirhosseinHonardoust/Safe-Token-Design-Guide","github":"https://github.com/AmirhosseinHonardoust/Safe-Token-Design-Guide","description":"A comprehensive guide to designing economically safe ERC-20 tokens using formal invariants and strict permission boundaries. Covers supply caps, fee ceilings, liquidity guarantees, transfer safety, oracle integrity, upgrade control, and economic threat modeling. Focused on real-world DeFi security and robust token design.","language":null,"stars":19,"topics":["audit","best-practices","blockchain","decentralized-finance","defi","economic-security","erc20","ethereum","governance","permission-boundaries"],"license":"MIT","category":"blockchain-web3"},{"repo":"AmirhosseinHonardoust/The-Psychology-of-Fraud","github":"https://github.com/AmirhosseinHonardoust/The-Psychology-of-Fraud","description":"A deep exploration of how human psychology shapes fraud behavior and how those patterns become measurable signals in transaction data. This article reveals the behavioral, cognitive, and economic forces behind fraud, explaining how ML models detect deviations, anomalies, and intent hidden within financial transactions.","language":null,"stars":19,"topics":["anomaly-detection","behavioral-analytics","behavioral-science","credit-card-fraud","crime-analysis","cybersecurity","data-science","digital-identity","explainable-ai","financial-crime"],"license":"MIT","category":"security-tools"},{"repo":"AmirhosseinHonardoust/Academic-Instability-Early-Warning-System","github":"https://github.com/AmirhosseinHonardoust/Academic-Instability-Early-Warning-System","description":"An interpretable early-warning engine that detects academic instability before grades collapse. Instead of predicting performance, it models pressure accumulation, buffer strength, and transition risk using attendance, engagement, and study load to explain fragility and identify high-leverage interventions.","language":"Python","stars":18,"topics":["academic-performance","ai-in-education","counterfactual-analysis","data-visualization","decision-support","early-warning-systems","education-analytics","equilibrium-modeling","ethical-ai","explainable-ai"],"license":"MIT","category":"analytics"},{"repo":"AmirhosseinHonardoust/Crypto-Price-Equilibrium-Simulator","github":"https://github.com/AmirhosseinHonardoust/Crypto-Price-Equilibrium-Simulator","description":"An explainable modeling system that analyzes cryptocurrency prices as equilibrium outcomes shaped by market forces. This simulator computes force decompositions, equilibrium bands, tension scores, and scenario-based what-if simulations to reveal how demand, supply, volatility, liquidity, and speculation negotiate price.","language":"Python","stars":18,"topics":["behavioral-finance","crypto-analytics","cryptocurrency","data-science","explainable-ai","financial-simulation","interpretable-ml","market-dynamics","pricing-models","python"],"license":"MIT","category":"blockchain-web3"},{"repo":"AmirhosseinHonardoust/AI-Assistant-Satisfaction-Prediction-Engine","github":"https://github.com/AmirhosseinHonardoust/AI-Assistant-Satisfaction-Prediction-Engine","description":"A complete machine-learning system that predicts AI assistant user satisfaction using behavioral signals such as device, usage category, time features, session metrics, and model metadata. Includes full ML pipeline, SHAP explainability, evaluation suite, and an interactive Streamlit analytics dashboard.","language":"Python","stars":18,"topics":["ai-analytics","behavioral-analysis","behavioral-modeling","classification-model","data-science","data-visualization","explainable-ai","feature-engineering","human-ai-interaction","machine-learning"],"license":"MIT","category":"machine-learning"},{"repo":"AmirhosseinHonardoust/Defi-Risk-Scenario-Lab","github":"https://github.com/AmirhosseinHonardoust/Defi-Risk-Scenario-Lab","description":"A research-grade lab for stress-testing DeFi protocols using Solidity mini-systems, a Python simulation engine, and a Streamlit dashboard. Simulates price crashes, liquidity shifts, AMM behavior, lending liquidations, and systemic risk dynamics. Designed for DeFi engineers, auditors, and researchers.","language":"Python","stars":16,"topics":["amm","decentralized-finance","defi","economics","ethereum","financial-modeling","onchain-data","protocol-design","python","quantitative-finance"],"license":"MIT","category":"blockchain-web3"},{"repo":"AmirhosseinHonardoust/Workforce-Disruption-Equilibrium-Engine","github":"https://github.com/AmirhosseinHonardoust/Workforce-Disruption-Equilibrium-Engine","description":"An interpretable system that models the future of work as an equilibrium under AI-driven forces. Instead of predicting job loss, it decomposes workforce disruption into automation pressure, adaptability, skill transferability, demand, and AI augmentation to explain stability, tension, and transition paths by 2030.","language":"Python","stars":16,"topics":["ai-augmentation","artificial-intelligence","automation","data-visualization","economic-modeling","equilibrium-modeling","explainable-ai","future-of-work","interpretable-models","kaggle-dataset"],"license":"MIT","category":"workflow-automation"},{"repo":"AmirhosseinHonardoust/Loyalty-Behavioral-Geometry","github":"https://github.com/AmirhosseinHonardoust/Loyalty-Behavioral-Geometry","description":"A deep exploration of loyalty as a multi-dimensional behavioral system shaped by intent, habit, and sensitivity. This article introduces a geometric framework for modeling customer behavior, predicting churn trajectories, and designing ML systems that understand loyalty as a dynamic state, not a metric.","language":null,"stars":16,"topics":["behavioral-economics","behavioral-science","business-intelligence","churn-prediction","customer-analytics","customer-behavior","customer-intelligence","decision-intelligence","dynamic-modeling","latent-variables"],"license":"MIT","category":"auth-billing-email"},{"repo":"AmirhosseinHonardoust/Tumor-Doppelganger-Studio","github":"https://github.com/AmirhosseinHonardoust/Tumor-Doppelganger-Studio","description":"Similarity-first interpretability studio for breast tumor samples: pick a case, find its closest “twins” (benign/malignant look-alikes), visualize neighborhood structure, compare feature fingerprints, and run minimal-change counterfactual edits toward a target class. Educational demo only, not for diagnosis.","language":"Python","stars":16,"topics":["breast-cancer","case-based-reasoning","classification","counterfactuals","data-visualization","educational-demo","explainable-ai","exploratory-data-analysis","feature-attribution","healthcare-analytics"],"license":"MIT","category":"analytics"},{"repo":"AmirhosseinHonardoust/Rate-Limited-Token-Faucet","github":"https://github.com/AmirhosseinHonardoust/Rate-Limited-Token-Faucet","description":"A secure, minimalistic ERC20 token faucet with per-address cooldown enforcement. Designed for testnets, demos, QA automation, and educational Solidity environments. Provides configurable drip amounts, adjustable cooldown timings, owner-controlled management, transparent event logs, and a fully dependency-free implementation.","language":"Solidity","stars":15,"topics":["blockchain-development","developer-tools","educational-project","erc20","rate-limit","smart-contract","smart-contract-examples","smart-contract-security","solidity","token-distribution"],"license":"MIT","category":"blockchain-web3"},{"repo":"AmirhosseinHonardoust/Skill-Adaptation-Debt-Engine","github":"https://github.com/AmirhosseinHonardoust/Skill-Adaptation-Debt-Engine","description":"A Streamlit dashboard that measures skill adaptation debt instead of predicting outcomes. It decomposes pressure into churn, novelty, and breadth to explain which roles/industries are becoming harder to staff. Includes role/industry reports, skill pressure maps, what-if scenario simulation, and a dataset explorer.","language":"Python","stars":15,"topics":["analytics","churn","dashboard","data-science","explainable-ai","future-of-work","interpretable-ml","jobs","kaggle","labor-market"],"license":"MIT","category":"dashboards-admin"},{"repo":"AmirhosseinHonardoust/Subscription-Loyalty-Risk-Radar","github":"https://github.com/AmirhosseinHonardoust/Subscription-Loyalty-Risk-Radar","description":"A customer intelligence engine that predicts subscription probability, models purchase frequency, and computes a unified loyalty risk score. Includes explainability, segment insights, and a scenario simulator, all integrated into an interactive Streamlit dashboard.","language":"Python","stars":15,"topics":["business-intelligence","churn-prediction","customer-loyalty","customer-segmentation","data-science","explainable-ai","feature-engineering","machine-learning","marketing-analytics","predictive-modeling"],"license":"MIT","category":"machine-learning"},{"repo":"AmirhosseinHonardoust/The-Physics-of-Defi","github":"https://github.com/AmirhosseinHonardoust/The-Physics-of-Defi","description":"A deep exploration of the economic physics governing DeFi crashes, AMM decay, liquidity spirals, and liquidation cascades. This article models decentralized finance as a nonlinear system driven by invariants, thresholds, and feedback loops, revealing why crashes follow predictable laws of motion.","language":null,"stars":15,"topics":["amm","decentralized-finance","defi","economic-modeling","ethereum","financial-simulation","liquidations","liquidity","onchain-analytics","protocol-design"],"license":"MIT","category":"blockchain-web3"},{"repo":"AmirhosseinHonardoust/Token-Tax-Abuse-Science","github":"https://github.com/AmirhosseinHonardoust/Token-Tax-Abuse-Science","description":"A deep technical exploration of how malicious smart-contract developers weaponize fee logic in ERC-20 tokens. Covers dynamic tax flipping, hidden sell traps, fee obfuscation, whitelist-based bypasses, liquidity-drain funnels, attack timelines, forensic analysis, mathematical modeling, and ML-powered detection strategies for tax abuse.","language":null,"stars":15,"topics":["blockchain-security","contract-analysis","cybersecurity","defi-security","ethereum","honeypot-detection","machine-learning","risk-scoring","rugpull-detection","security-research"],"license":"MIT","category":"blockchain-web3"},{"repo":"AmirhosseinHonardoust/Adaptive-Tax-Token","github":"https://github.com/AmirhosseinHonardoust/Adaptive-Tax-Token","description":"An experimental ERC20 token implementing adaptive tokenomics through an auto-adjusting transfer tax. The contract monitors recent transfer volume in a configurable time window and dynamically increases or decreases fees based on market activity. A clean, dependency-free demonstration of self-regulating, volume-responsive economic design.","language":"Solidity","stars":15,"topics":["adaptive-tokenomics","blockchain-development","cryptoeconomics","economic-modeling","erc20","evm-contracts","market-activity","open-source-solidity","research-project","self-regulating-systems"],"license":"MIT","category":"analytics"},{"repo":"AmirhosseinHonardoust/Physiological-Debt-Accumulation-Engine","github":"https://github.com/AmirhosseinHonardoust/Physiological-Debt-Accumulation-Engine","description":"A systems-level analysis engine that models sleep as a recovery debt process rather than a nightly outcome. Using physiological traits and ecological pressure signals, it estimates predicted sleep need, quantifies sleep debt, and visualizes how stress accumulates silently before visible fatigue or failure occurs.","language":"Python","stars":15,"topics":["counterfactual-analysis","data-visualization","early-warning-systems","health-analytics","interpretable-ml","machine-learning","python","recovery-modeling","research-prototype","sleep-analysis"],"license":"MIT","category":"machine-learning"},{"repo":"AmirhosseinHonardoust/Solidity-Economic-Risk-Scanner","github":"https://github.com/AmirhosseinHonardoust/Solidity-Economic-Risk-Scanner","description":"A research-grade tool that analyzes Solidity smart contracts for economic vulnerabilities such as unbounded minting, toxic fee mechanisms, liquidity traps, oracle manipulation, centralized control, and broken financial invariants. Focused on economic correctness, incentive risks, and DeFi system stability.","language":"Python","stars":14,"topics":["amms","contract-analysis","defi","economic-analysis","economic-security","ethereum","lending","liquidity-risk","oracle-security","protocol-security"],"license":"MIT","category":"blockchain-web3"},{"repo":"AmirhosseinHonardoust/Bytecode-Truth-Not-Source","github":"https://github.com/AmirhosseinHonardoust/Bytecode-Truth-Not-Source","description":"Bytecode Truth, Not Source is a deep technical exploration of why smart-contract source code cannot be trusted as the ground truth for security. This repository shows how compiler optimizations, hidden assembly, proxies, and unreachable logic make verified Solidity misleading, and why only EVM bytecode reveals actual on-chain behavior.","language":null,"stars":14,"topics":["blockchain-security","bytecode-analysis","compiler-optimizations","decompiler","dynamic-analysis","ethereum","evm","evm-assembly","honeypot-detection","malware-analysis"],"license":"MIT","category":"blockchain-web3"},{"repo":"AmirhosseinHonardoust/Multichain-Scam-Deployer-Graph","github":"https://github.com/AmirhosseinHonardoust/Multichain-Scam-Deployer-Graph","description":"A cross-chain intelligence toolkit that maps suspicious smart-contract deployers across Ethereum, BSC, Arbitrum, and Base. Fetches deployer histories using Scan V2 APIs, builds a structured NetworkX graph, extracts ML-ready behavioral features, and assigns heuristic risk scores to identify scam clusters and malicious deployment patterns.","language":"Python","stars":14,"topics":["arbitrum","base-chain","blockchain-security","bsc","cybersecurity","data-science","deployer-reputation","ethereum","forensics","graph-analysis"],"license":"MIT","category":"blockchain-web3"},{"repo":"AmirhosseinHonardoust/Underwriting-Decision-Safety-Lab","github":"https://github.com/AmirhosseinHonardoust/Underwriting-Decision-Safety-Lab","description":"A decision-safety lab for loan approval: trains a baseline classifier, calibrates probabilities (ECE/Brier), sweeps confidence thresholds to build a coverage, quality frontier and outputs a defensible abstention policy (auto-decide vs review). Includes a Streamlit dashboard for report cards, triage UI, and data quality checks.","language":"Python","stars":12,"topics":["abstention","calibration","classification","credit-risk","data-quality","data-science","decision-policy","loan-approval","machine-learning","mlops"],"license":"MIT","category":"machine-learning"},{"repo":"AmirhosseinHonardoust/Retail-Calendar-Pattern-Finder","github":"https://github.com/AmirhosseinHonardoust/Retail-Calendar-Pattern-Finder","description":"A retail analytics capstone that converts transactions into a calendar intelligence system. It quantifies day-of-week and monthly seasonality, builds a baseline expected revenue model, detects event-like spike days using robust residual z-scores, and explains spikes via transactions, units, AOV, and category mix, with a Streamlit dashboard+exports.","language":"Python","stars":11,"topics":["anomaly-detection","baseline-model","dashboard","data-analysis","data-science","exploratory-data-analysis","feature-engineering","kaggle-dataset","matplotlib","numpy"],"license":"MIT","category":"dashboards-admin"},{"repo":"AmirhosseinHonardoust/Analysis-to-Policy-Playbook","github":"https://github.com/AmirhosseinHonardoust/Analysis-to-Policy-Playbook","description":"A practical framework for turning data analysis into decision policies you can defend. Covers risk modeling, thresholding, exception handling, policy cards, monitoring, and update triggers, using real patterns like abstention rules, reorder points, and fairness-aware benchmarking. Built for “ship it” data science.","language":null,"stars":11,"topics":["abstention","calibration","dashboards","data-analysis","data-science","decision-making","drift-detection","experiment-design","fairness","human-in-the-loop"],"license":"MIT","category":"dashboards-admin"},{"repo":"AmirhosseinHonardoust/KPI-Denominator-Truth","github":"https://github.com/AmirhosseinHonardoust/KPI-Denominator-Truth","description":"A long-form analytics article on why KPIs fail when the denominator silently changes. Covers denominator contracts, eligibility vs observed vs attempted populations, drift/coverage failure modes, mix shifts, time alignment, and a practical audit checklist to design decision-safe ratio metrics that don’t lie.","language":null,"stars":11,"topics":["analytics","business-intelligence","cohort-analysis","conversion-rate","dashboarding","data-analysis","data-literacy","data-quality","data-science","decision-making"],"license":"MIT","category":"analytics"},{"repo":"AmirhosseinHonardoust/Decision-Safety","github":"https://github.com/AmirhosseinHonardoust/Decision-Safety","description":"Long-form article introducing Decision Safety: a trust gate between dashboards and actions. Defines four pillars (coverage, freshness, stability, measurement risk), proposes a Decision Safety Score (0–100), shows common failure modes, and includes a copy-paste “Decision Safety Contract”+checklist to block unsafe decisions without hiding dashboards.","language":null,"stars":11,"topics":["ab-testing","analytics","analytics-engineering","best-practices","dashboards","data-analytics","data-engineering","data-observability","data-quality","data-validation"],"license":"MIT","category":"dashboards-admin"},{"repo":"AmirhosseinHonardoust/Digital-Balance-Index-Dashboard","github":"https://github.com/AmirhosseinHonardoust/Digital-Balance-Index-Dashboard","description":"Composition-first analytics for daily screen time. Computes the Digital Balance Index (DBI) using normalized entropy, plus dominance, tiers, and a “high-load & skewed” flag. Exports scored rows, segment summaries, daily trends, and figures, and ships a Streamlit dashboard to explore patterns by age group, primary device, and internet type.","language":"Python","stars":10,"topics":["behavioral-analytics","composition-analysis","dashboard","data-analytics","data-science","digital-behavior","entropy","exploratory-data-analysis","feature-engineering","matplotlib"],"license":"MIT","category":"dashboards-admin"},{"repo":"AmirhosseinHonardoust/Workout-Efficiency-Benchmark","github":"https://github.com/AmirhosseinHonardoust/Workout-Efficiency-Benchmark","description":"Streamlit + Python pipeline that benchmarks gym workout efficiency (kcal/min) using present sessions only. Generates sortable workout-type benchmarks, distribution plots, fairness-aware gap analysis with uncertainty/low-sample flags, and a data-quality report to prevent misleading comparisons.","language":"Python","stars":10,"topics":["analytics","benchmarking","bias-audit","dashboard","data-analysis","data-quality","data-science","eda","fairness","fitness"],"license":"MIT","category":"dashboards-admin"},{"repo":"AmirhosseinHonardoust/Trend-vs-Collection-Forensics","github":"https://github.com/AmirhosseinHonardoust/Trend-vs-Collection-Forensics","description":"A deep-dive analytics article on why most KPI “trends” are actually measurement artifacts. Covers the fake-trend taxonomy (schema drift, coverage loss, time shifts, backfills, dedupe, sampling, mix change), a Trend Courtroom evidence protocol, segment invariance tests, and copy-ready checklists + a Trend Report Card for decision safety.","language":null,"stars":10,"topics":["analytics","cohort-analysis","coverage","dashboards","data-analysis","data-engineering","data-quality","data-science","decision-making","event-tracking"],"license":"MIT","category":"dashboards-admin"},{"repo":"AmirhosseinHonardoust/Abstention-is-Product-Feature","github":"https://github.com/AmirhosseinHonardoust/Abstention-is-Product-Feature","description":"Longform article reframing abstention (reject option / selective prediction) as product design, not model weakness. Covers coverage as a KPI, calibration as a prerequisite, threshold selection under review capacity and risk, queue/UX design for human-in-the-loop workflows, and anti-patterns that break safety in production.","language":null,"stars":10,"topics":["abstention","ai-safety","calibration","coverage","decision-systems","human-in-the-loop","machine-learning","metrics","mlops","model-evaluation"],"license":"MIT","category":"machine-learning"},{"repo":"AmirhosseinHonardoust/Cafe-Demand-Forecasting-Reorder-Simulator","github":"https://github.com/AmirhosseinHonardoust/Cafe-Demand-Forecasting-Reorder-Simulator","description":"End-to-end café inventory project: clean transaction data, build daily item-level demand series, backtest strong baseline forecasters, generate next-30-day demand forecasts, convert forecasts into safety stock + reorder points, and validate policies with Monte Carlo stockout-risk simulations, wrapped in a Streamlit dashboard.","language":"Python","stars":10,"topics":["backtesting","baseline-models","dashboard","data-analysis","data-science","decision-science","demand-forecasting","forecasting","inventory-management","kaggle-dataset"],"license":"MIT","category":"trading"},{"repo":"AmirhosseinHonardoust/Coverage-is-The-Silent-Killer","github":"https://github.com/AmirhosseinHonardoust/Coverage-is-The-Silent-Killer","description":"A long-form, practical article on data coverage: why clean dashboards still lie when datasets don’t represent the full calendar or population. Includes definitions, real failure modes (joins, filters, late data), coverage metrics, visualization patterns, anomaly/forecasting pitfalls, and reusable checklists.","language":null,"stars":10,"topics":["anomaly-detection","best-practices","dashboards","data-analytics","data-engineering","data-observability","data-quality","data-science","data-validation","documentation"],"license":"MIT","category":"dashboards-admin"}],"how_to_buy":"GET /r/AmirhosseinHonardoust/<repo> (Accept: application/json) for any listed repo here: tree, README, price and the checkout to pay (x402; rehearse first at its test twin, simulated money). Repos under 'indexed' are free: clone them from GitHub."}