This Project is part of Data Science Nanodegree Program by Udacity in collaboration with Figure Eight. The initial dataset contains pre-labelled tweet and messages from real-life disaster. The aim of the project is to build a Natural Language Processing tool that categorize messages.

The Project is divided in the following Sections:

1. Data Processing, ETL Pipeline to extract data from source, clean data and save them in a proper databse structure
2. Machine Learning Pipeline to train a model able to classify text message in categories
3. Web App to show model results in real time.

• Data:

  • We have the disaster category and message csv files, that contain the data on which the model was trained in.
  • We have process_data.py which has the ETL pipeline.
  • We have the database created 'DisasterResponse.db', this is the output of running the ETL pipeline.

• Models:

  • We have the 'train_classifier.py' from where the ML pipeline is contained.
  • We have the 'starting_verb_extractor.py' which is a class used for expanding the model creating a starting verb extractor as a feature.

• App:

  • We have 'run.py' which is used as the main file for starting the web app.

1. Clone this GIT repository:

https://github.com/dancor7/Udacity_Disaster_Response_Pipeline.git

2. 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, delete the DisasterResponse.db file if it has been created before and run the following code: 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

3. Run the following command in the app's directory to run your web app: python run.py

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