A data analysis project exploring consumer behavior and sales trends through EDA using Python. Includes visualizations and insights derived from retail shopping data.

[BETA — v2 rebuild] MCP server for data analytics — Shopify, Stripe, CSV, forecasting, ML. Works in Claude, Cursor, and any MCP client. Expect rough edges while the rewrite lands.

Upload a CSV → get a full EDA report with LLM-generated executive summary, charts, and downloadable PDF/Word exports. Built with Streamlit, Pandas, and OpenAI GPT-4. Handles 50K+ row datasets, cuts manual EDA time by 60%. Includes a sample output report.

Enterprise-grade CSV data quality analyzer powered by Machine Learning. Automatic anomaly detection, statistical profiling, PII scanning, and actionable insights. Secure user authentication, custom data pipelines, and interactive dashboards. Production-ready SaaS application.

A menu-driven Student Result Analysis system built with Streamlit and Pandas. Perform automated grading, topper identification, and subject-wise performance analysis from CSV data.

Adaptive analytics cockpit for CSV-driven business decision workflows.

InsightData is an intelligent data analysis tool that turns natural language queries into Python code. Built using Streamlit, LangChain, and Google Gemini (Flash 2.5), it allows users to upload datasets (CSV/Excel) or connect Google Sheets to perform EDA, clean data, and generate matplotlib visualizations instantly. Supports English & Indonesian.

This project aims to analyze e-commerce data to derive meaningful insights about customer behavior, sales trends, and product performance. We utilize Python, MySQL, and various data visualization libraries to perform the analysis.

AI-powered Streamlit app that profiles CSV datasets, cleans data, trains baseline ML models, generates charts, and writes analysis reports.

An AI-powered trade prediction system using machine learning, technical analysis, and time series models. Built with FastAPI, React, and Tailwind CSS.

Interactive CSV analytics dashboard built with Python, Pandas, Plotly and Streamlit.

🧠 A 100% local, privacy-focused RAG system that lets you chat with PDFs, CSVs, and NoSQL data offline using Ollama & ChromaDB.

This project uncovers audience behavior patterns by analyzing YouTube video engagement metrics using Python. From 360° EDA to interactive dashboards, it breaks down how views, likes, dislikes, and comments reveal user sentiment and content performance, built with NumPy, Pandas, Seaborn, Dash, and hypothesis testing to produce real time analytics.

An interactive web app to analyze tweet sentiments using TextBlob or VADER. Supports real-time predictions and CSV uploads for bulk analysis. Features automatic tweet cleaning, model selection, and sentiment visualizations with Seaborn/Matplotlib.

AI-powered financial analysis platform built with Django and AWS Bedrock, designed for accountants to gain insights from CSV data with multi-agent support (MCP).

A High-Performance RAG Engine using Streamlit, LangChain, & Gemini 2.5 Flash. Built on ConversationalRetrievalChain for instant, precise document analysis (PDF, CSV, MD, TXT) without agentic overhead.

Automated EDA tool for ML datasets. Upload CSV, select task type, get comprehensive insights with exportable reports.

A full-stack AI-powered business intelligence dashboard. Upload a CSV file containing sales, customer, and feedback data — get instant forecasts, anomaly detection, customer segmentation, sentiment analysis, and AI-generated insights.

AI-powered data analysis dashboard built with Streamlit for CSV exploration, visualization, and insights.

Local infrastructure inventory and documentation dashboard built with Python & Streamlit.