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Emotion Detection using Machine Learning

Overview:

This mini project focuses on implementing an Emotion Detection system using machine learning techniques. The goal is to create a model that can accurately classify the emotions present in a given input, such as text, audio, or images.

Features:

  • Text Emotion Detection: The model can analyze and classify emotions from textual input.

Technologies and Tools:

Machine Learning Frameworks: Python libraries such as scikit-learn, will be used to develop and train the machine learning models.

Goals:

  • Train and fine-tune machine learning models to achieve high accuracy in emotion detection across different input types.
  • Optimize the models for speed and efficiency to allow real-time or near real-time emotion detection.
  • Encourage collaboration and contributions from the open-source community to enhance the accuracy and capabilities of the emotion detection system.

How to Contribute:

  • Fork the repository and clone it to your local machine.
  • Choose an area of interest (text emotion detection or frontend web page) and propose improvements or optimizations.
  • Implement new features, fix bugs, or enhance the existing functionality. Ensure to follow the coding standards and guidelines.
  • Submit a pull request with a clear description of your changes and improvements.

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  • HTML 60.6%
  • Python 39.4%