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This project aims to analyze WhatsApp group chat data to extract meaningful insights and visualize trends. The analysis includes metrics like message frequency, active hours, user participation, and content types. By examining these patterns, we can gain a deeper understanding of communication dynamics within the group.

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Rakesh-konne/Whatsappgrpchatanalysis

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WhatsApp Group Chat Analysis

Project Overview

This project aims to analyze WhatsApp group chat data to extract meaningful insights and visualize trends. The analysis includes metrics like message frequency, active hours, user participation, and content types. By examining these patterns, we can gain a deeper understanding of communication dynamics within the group.

Features

  • Data Extraction: Extracts messages from WhatsApp chat exports, including text, media, and metadata.
  • Message Analysis: Analyzes message frequency, user participation, and most active hours.
  • Sentiment Analysis: Assesses the sentiment of the messages to determine the overall mood of the chat.
  • Media Analysis: Identifies and categorizes different types of media shared in the chat.
  • Visualization: Provides visual representations of the analysis through graphs and charts.

Technologies Used

  • Python: The core programming language used for data processing and analysis.
  • Pandas: For data manipulation and analysis.
  • Matplotlib: For data visualization.
  • IPywidgets: For interactive widgets in Jupyter notebooks.

Installation

  1. Clone the repository:

    git clone https://github.com/Rakesh-konne/Whatsappgrpchatanalysis.git
  2. Make Sure to install python

  3. Make sure to install matplotlib else use below command:

    pip install matplotlib
    

usage

  • Export your WhatsApp group chat and place the text file in the data/ directory.
  • Run the Jupyter notebook and upload the text file from the data/ directory.
  • follow the instructions to analyze your chat data

About

This project aims to analyze WhatsApp group chat data to extract meaningful insights and visualize trends. The analysis includes metrics like message frequency, active hours, user participation, and content types. By examining these patterns, we can gain a deeper understanding of communication dynamics within the group.

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