Disaster-Response-Model

1. Overview

In this project, I'll apply data engineering to analyze disaster data from Figure Eight to build a model for an API that classifies disaster messages.

data directory contains a data set which are real messages that were sent during disaster events. I will be creating a machine learning pipeline to categorize these events so that appropriate disaster relief agency can be reached out for help.

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

2. Required libraries

• nltk 3.5
• numpy 1.18.3
• pandas 1.0.3
• scikit-learn 0.22.2
• sqlalchemy 1.3.16
• flask 1.1.2

3. Project Components

3.1 ETL Pipeline

File data/process_data.py contains data cleaning pipeline that:

• Loads the messages and categories dataset
• Merges the two datasets
• Cleans the data
• Stores it in a SQLite database

3.2. ML Pipeline

File models/train_classifier.py contains machine learning pipeline that:

• Loads data from the SQLite database
• Splits the data into training and testing sets
• Builds a text processing and machine learning pipeline
• Trains and tunes a model using GridSearchCV
• Outputs result on the test set
• Exports the final model as a pickle file

3.3. Flask Web App

Running python run.py from app directory will start the web app where users can enter their query, i.e., a request message sent during a natural disaster.

4. Compilation

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/

5. Credits and Acknowledgements

Thanks Udacity for letting me use their logo as favicon for this web app and for all the supports. Also thanks to Figure Eight for providing the disaster dataset.