Disaster Response Pipeline Project

This project is aimed at training a classifier that helps in assigning various disaster responses into a set of categories that help in providing prompt and actionable response to these messages.

The project consists of 3 different stages:

1. Preparing the Training and Test Data : The resources for this stage can be found under the data directory and consists of a process_data.py file that loads a CSV file that contains the raw data around various disaster response messages and their classification. The process_data.py script cleans the raw data and structures it to be in a processable format and stores it into a local DB on the file system as provided in the runtime arguments.

2. Training the classifier : The resources for this stage can be found under the models directory and consists of a train_classifier.py file that uses the local DB created to read the processed data and use that to train as well as test a Multi output classifier using a ML pipeline that has the hyper-parameters that have been optimised after doing cross validation on the training data. This stage also uses a custom_transformer.py file to make use of a custom transformer in the ML pipeline which is used to detect the existence of a Verb in the disaster response message. The script after generating the model serializes it to a file on the file system as provided in the runtime arguments.

3. Running the classifier as a Flask Service : The resources for this stage can be found under the app directory and consists of a run.py , which is a flask application that loads the trained classifier and hosts it so that users can interact with it realtime and classify their response messages into a set of categories. It also uses a few resources to generate a web app endpoint that are present under the app/templates directory.

Instructions to run the project:

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/