OBJECTIVE :

1. To enhance the accuracy of a previously implemented priority mail recommendation system.
2. To improve the system's user-friendliness.
3. Implementation of Natural Language Processing (NLP) for context analysis and queries.

DOMAIN:

Deep Learning

SUB-DOMAIN:

Natural Language Processing (NLP)

INTRODUCTION:

In today's modern organizations, employees face challenges in managing work-related documents across various systems and adapting to flexible team structures. This project aims to address these challenges using deep learning and NLP. By suggesting email priorities based on context and past interactions, we aim to reduce information overload and enhance time management for employees. Additionally, an automated response feature will handle common queries efficiently.

BLOCK DIAGRAM:

SOFTWARE REQUIREMENTS:

1. Python 3.x
2. NLP libraries:
   • PyTorch
   • NLTK
   • Pattern
3. Deep Learning Libraries:
   • TensorFlow
   • Keras
4. WebApp library/Framework:
   • Django
5. Other libraries:
   • Pandas
   • NumPy
   • Matplotlib
   • Json
   • os
6. HTML
7. CSS
8. JavaScript

INNOVATION AND USEFULNESS:

This project offers automation of email categorization based on context analysis, providing improved accuracy compared to explicit categorization systems. It can be implemented in organizations with high volumes of daily email conversations.

Overall, the project aims to deploy a deep learning model for recommending priority emails based on conversation history and email context with enhanced accuracy. Additionally, an automated model will handle commonly asked queries, offering efficiency and productivity benefits.

Watch the final working demo here