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

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.

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

🔍 Titanic EDA: odkrywanie wzorców przeżywalności przez analizę danych. Profesjonalny projekt z wizualizacjami i insights

An automation script for analyzing and processing CSV data, designed to identify and categorize errors by aggregators, and calculate totals, including a grand total. Developed to streamline data analysis at Infozillion Teletech BD Ltd.

In a complex professional world, understanding the true strength and relevance of your network is more critical than ever. The PSA (Presence Signaling Architecture) Network Analyzer is a sophisticated yet easy-to-use local tool designed to bring clarity, strategy, and ethical visibility to your professional relationships.

Python-based tool for analyzing student enrollment trends, seat availability, and section efficiency across multiple campuses.

A Python CLI tool for analyzing student performance using Pandas. Modular, beginner-friendly, and command-line operated.

Digital Forensics project analyzing browser artifacts using Nirsoft BrowsingHistoryView

A lightweight Flask application for CSV upload, tabular preview, and basic data visualisation using Pandas and Matplotlib. Final project for CS50x.

Streamlit-Powered Business Analytics Dashboard to analyze products, sales, and purchases with visual KPIs and smart recommendations.

A Python-based tool for analyzing and visualizing personal financial transactions from CSV files.

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.