This project aims to develop a machine learning model that analyzes scenarios and predicts:

1. How a person might feel (emotion) based on the given situation.
2. An appropriate response tailored to the context of the scenario.

The ultimate goal is to create an intelligent system capable of understanding human emotions and generating empathetic, context-aware responses. This repository serves as a collaborative platform for developers and researchers to refine, enhance, and extend the project's capabilities.

The current implementation includes:

• A basic machine learning pipeline that classifies scenarios into predefined emotion labels using a Naive Bayes classifier.
• Text preprocessing, including stopword removal, punctuation handling, and vectorization with TF-IDF.
• Evaluation of the classification model using metrics like accuracy, precision, recall, and F1-score.

However, the current dataset only contains scenarios and their corresponding labels. Expanding the dataset to include emotional causes and responses is essential for achieving the project's goals.

1. Expand the dataset to include the following columns:
   • Scenario: Description of the situation or event.
   • Emotion: The emotional reaction to the scenario (e.g., happy, sad, angry, etc.).
   • Response: An appropriate response or action based on the scenario.
2. Modify the model to handle multi-output predictions, allowing it to predict both emotion and response.
3. Explore advanced NLP models (e.g., transformers like BERT or GPT) for improved performance.

For the project to progress, the dataset should include the following fields:

If you want to contribute a dataset, ensure it follows this format and contains diverse, realistic scenarios.