Flood Chat
In response to the increasing frequency of flood events in Africa, our team has developed a Natural Language Processing (NLP) chatbot and a Machine Learning (ML) project to enhance awareness and provide guidelines on staying safe during flood occurrences. This project integrates a chatbot capable of answering user queries related to flood events in Africa and an ML model trained on historical flood data for the continent.
The Flood Chat Chatbot is designed to engage users in natural language conversations, providing real-time information and guidelines on staying safe during flood events. Users can ask questions related to flood preparedness, response strategies, and specific details about past flood events in Africa. The chatbot utilizes NLP techniques to understand and respond to user queries, ensuring a user-friendly and informative experience.
The Flood_chat Chatbot, coupled with the ML project, offers a comprehensive solution to flood awareness and safety. Users can interact with the chatbot to receive personalized guidelines, while the ML model provides valuable insights into historical flood patterns in Africa, aiding in proactive preparedness and response strategies.
By combining cutting-edge NLP and ML technologies, this project contributes to the ongoing efforts to mitigate the impact of natural disasters, fostering a safer and more resilient environment in flood-prone regions of Africa. Write a brief summary about the project here (what problem are you solving , whats your solution) including goals, and key features
I. Introduction 1.1 Background 1.2 Objectives 1.3 Scope of the Project
II. FloodSafety Chatbot 2.1 Overview 2.2 Features and Capabilities 2.3 User Interaction and Queries 2.4 NLP Techniques Employed
III. ML Project: African Flood Event Analysis 3.1 Data Collection 3.1.1 Source of Data 3.1.2 Data Range 3.2 Data Cleaning and Preprocessing 3.2.1 Handling Missing Values 3.2.2 Outlier Detection and Treatment 3.3 Feature Engineering 3.3.1 Relevant Features Extracted 3.3.2 New Features Created 3.4 Model Selection 3.4.1 Decision Trees (DT) 3.4.2 Random Forests (RF) 3.4.3 Gradient Boosting (GB) 3.5 Model Training and Evaluation 3.5.1 Training Process 3.5.2 Evaluation Metrics 3.6 Key Findings and Outcomes
IV. Integration of Chatbot and ML Model 4.1 Synergies Between Chatbot and ML Project 4.2 Enhancements in Flood Safety Awareness
V. Conclusion 5.1 Summary of Achievements 5.2 Future Developments and Enhancements
VI. Acknowledgments
VII. References
Explain how to get a copy of the project up and running on a local machine for development and testing purposes
Step-by-step instructions on how to install and set up the project
Special appreciation to Seinna Analytics, https://python.langchain.com/,
Etietop Udofia
Your project should involve the following components:
- Data Sourcing: Web scraping or any other data sourcing method.
- Data Cleaning and Prep: Data Cleaning, preparation and basic statistics reporting
- Modeling: Base Model, Model Comparison, Hyper-parameter Tuning and monitoring with experiment management
- Model Deployment : Deploy on the web or mobile. You can leverage Google Colab/Streamlit/Huggyface where possible.
- Requirements.txt: A file for all dependecies required
- Project Submission Deadline: December 10, 2023
- Presentation Day: December 16, 2023