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The WhatsApp Chat Analyzer is a Python-based project designed for the analysis of WhatsApp chat data. This tool offers valuable insights into user conversations and behavior. Accessible through a user-friendly web interface built with Streamlit.

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📱 WhatsApp Chat Analyzer

📝 About Project

WhatsApp Chat Analyzer is a Python-based project that allows you to analyze your WhatsApp chat data. It provides insights into user conversations and behavior, and it's accessible through a user-friendly web interface created with Streamlit.

WhatsApp Logo

🎯 Problem Statement

  • The objective of this project is to develop a WhatsApp chat analysis tool that allows users to gain insights from their chat data.
  • The challenge is to create a user-friendly web interface using Streamlit for chat data analysis, data cleansing, and visualization.
  • This tool aims to provide meaningful insights into message frequency, user interactions, and chat patterns, simplifying the understanding of WhatsApp chat data.

📋 Table of Contents

  • 📊 Project Overview
  • 🧹 Data Cleaning and Analysis
  • 📊 Data Visualization
  • 🚀 Usage
  • 📂 WhatsApp Chat Export Tutorial
  • 🛠️ Challenges Faced
  • 🔍 Insights Derived
  • 🚀 Future Scope
  • 🤝 Contributing

Project Overview

WhatsApp Chat Analyzer provides a comprehensive analysis of WhatsApp chat data. It includes data cleaning, analysis, and visualization to extract valuable insights from conversations.

🧹 Data Cleaning and Analysis

  1. 📥 Import WhatsApp Chat Data: Begin by importing the WhatsApp chat data from the text file you exported.

  2. 🧹 Text Preprocessing: Perform text preprocessing to ensure data accuracy and consistency. This may include:

    • Removing duplicates and irrelevant messages.
    • Parsing message timestamps, senders, and message content.
    • Handling special characters or formatting inconsistencies.
  3. 🧬 Data Transformation: Transform the cleaned data into a structured format for analysis. Create variables for message timestamps, senders, and message content.

  4. 📊 Metrics Calculation: Calculate relevant metrics, such as message count, media count, average message length, and sender statistics.

  5. 💾 Save Cleaned Data: Save the cleaned and structured data for further analysis.

📊 Data Visualization

  • Matplotlib and Seaborn are used to create visualizations, including:
    • Message distribution over time.
    • Top message senders.
    • Word cloud for most frequent words.
    • Media (images, videos) distribution.
    • Emojis usage analysis.

📷 Screenshots

  • Home Page

Home page / search bar

  • Top Statistics

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  • Message distribution over time

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  • Activity Map

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  • Top message senders

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  • Most Frequently Used Words

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  • Emoji Analysis

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  • Word Cloud

Home page / search bar

🚀 Usage

  1. Export WhatsApp Chat:

    • Open WhatsApp.
    • Go to the upper-right corner and click on the three dots.
    • Select "More" and then "Export chats."
    • Export the chat to a text file on your system.
  2. Analyze Chat:

    • Visit the project's website.
    • Click on the "Browse file" button.
    • Select the exported WhatsApp chat text file.
    • Click on the "Show Analysis" button.
  3. Explore Insights:

    • View statistics, charts, and insights about your WhatsApp chat.

WhatsApp Chat Export Tutorial

  1. Open WhatsApp.


  1. In the upper-right corner, click on the three dots.


  1. Scroll down and find the "More" option, then click on it.


  1. In the "More" menu, you will find the "Export chats" option. Choose a chat to export, and save the chat data to a text file on your device.


🚀 Challenges Faced

  • Data Cleaning: The project required thorough data cleaning to handle variations in chat formats, timestamps, and user messages.

  • User-Friendly Interface: Designing a simple and intuitive web interface for users to upload chat data and view insights presented a challenge.

  • Data Visualization: Creating informative and visually appealing charts and graphs to represent chat patterns and insights was a key challenge.

🌟 Insights Derived

  • User Interaction Patterns: Analyzing message frequency, active users, and word clouds provided insights into user engagement and communication patterns.

  • Data Visualization: Utilizing Matplotlib and Seaborn libraries, the project visualized chat data effectively, enabling users to grasp trends and patterns.

  • Data Cleaning: Expertise in data preprocessing and cleaning ensured that the chat data was accurate and ready for analysis.

🔮 Future Scope

  • Advanced Analysis: Implement advanced natural language processing (NLP) techniques for sentiment analysis and chatbot integration.

  • User Segmentation: Enhance user insights by segmenting users based on chat activity, preferences, and interactions.

  • Enhanced Reporting: Develop customizable report generation to provide users with detailed chat statistics and summaries.

  • Integration: Explore integration with cloud storage services for seamless chat data access.

🤝 Contributing

  • Contributions to this project are welcome! If you have suggestions, improvements, or additional features to propose, please feel free to fork the repository, make your changes, and submit a pull request.

🌐 WhatsApp Chat Analyzer Website

You can access the WhatsApp Chat Analyzer through the following link:

Simply click on the link to visit the website and start analyzing your WhatsApp chat data.

About

The WhatsApp Chat Analyzer is a Python-based project designed for the analysis of WhatsApp chat data. This tool offers valuable insights into user conversations and behavior. Accessible through a user-friendly web interface built with Streamlit.

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