Udacity Disaster Response Project

This project includes a web application where an emergency worker can input a new message and get classification results under several messages categories. The web app will also display visualizations of the data.

Getting Started

Prerequisites

pip install pandas 
pip install Numpy
pip install Sci-kit Learn
pip install Flask 
pip install SQL Alchemy
pip install Plotly
pip install NLTK

Dataset

We use/analyse data from Figure Eightin this project is a labeld dataset contains disatser messages, each messagae is labeled by the category of the message.

Build With

• Plotly - For data visualaziation
• Bootstrap - Web framework, Front-End Library
• Flask - Web framework to build the Back-End. You should sign up for Flask if you don't have an account

Folders and Files

This repository includes:

• Jupyter_Notebook Two notebook files inlcuding ETL(Extract, Transform, Load) Pipeline notebook and ML pipeline notebook.
• data Where you the data is uploaded and processed.
• app running the web application.
• models where the machine learning model is trained/tested/saved.

Running Instrcutions

1. Run the following commands in the project's root directory to set up your database and model.

   • To run ETL pipeline that cleans data and stores in database

     python data/process_data.py data/disaster_messages.csv data/disaster_categories.csv data/DisasterResponse.db
     

   • To run ML pipeline that trains classifier and saves

     python models/train_classifier.py data/DisasterResponse.db models/classifier.pkl
     

2. Run the following command in the app's directory to run your web app.

   python run.py
   

3. Go to http://0.0.0.0:3001/

Authors

• Meriem Ferdjouni

Acknowledgments

• Udacity (Data scientist Nanodegree Program)