WhatsApp Chat Analyzer using Python & Streamlit | Data Analytics Portfolio Project

This video demonstrates a comprehensive Data Analytics and NLP project: Analyze WhatsApp Chat. The goal of this project is to build an interactive web-based dashboard that takes an exported, unformatted WhatsApp chat history text file (.txt) as input and parses it to extract deep, meaningful insights about group or individual conversations. ​This project and video showcase are designed to serve as part of my professional data science portfolio, highlighting my proficiency in Regex-based string processing, time-series analysis, and dynamic data visualization. ​💻 GitHub Repository: https://github.com/mahady13/Analyze-Whatsapp-Chat ​🛠️ Key Technical Features ​Regex-Based Text Parsing: Utilized Python's re (Regular Expression) module to extract dates, timestamps, author names, and raw messages from highly unstructured text files across different mobile OS formats. ​Time-Series Analysis: Engineered datetime features to break down chat activity trends over years, months, weeks, and hours—uncovering peak conversation periods. ​Text Mining & NLP: Extracted key metrics such as total word counts, media shared, links shared, and generated word clouds to highlight the most frequently used terms. ​Interactive Data Visualization: Developed rich, dynamic charts (Bar charts, Line graphs, and Heatmaps) using libraries like Matplotlib, Seaborn, or Plotly to track monthly timelines and user activity heatmaps. ​Streamlit UI Implementation: Built a clean, sidebar-driven analytical dashboard using Streamlit, allowing recruiters or users to safely drop their text file and instantly generate comprehensive conversation stats. ​💻 Tech Stack & Tools ​Programming Language: Python ​Libraries: Pandas, NumPy, Regular Expressions (re), Matplotlib, Seaborn, WordCloud ​Development Environment: JupyterLab / Jupyter Notebook, Pycharm ​Deployment & UI: Streamlit