malhomaid / disaster-response-pipeline

Natural Language Processing tool that categorize messages

Disaster Response Pipeline

The aim of the project is to build a Natural Language Processing tool that categorize messages. The initial dataset contains pre-labelled tweet and messages from real-life disaster provided by Figure Eight.

The Project is divided in the following Sections:

• Data Processing, ETL Pipeline to extract data from source, clean data and save it to a database
• Machine Learning Pipeline to train a model to be able to classify the text messages to categories
• Web Application to predict message's categories interactively

File Descriptions

• data
  • disaster_categories.csv: dataset including all the categories
  • disaster_messages.csv: dataset including all the messages
  • process_data.py: ETL pipeline scripts that save the data into a SQLite database
  • DisasterResponse.db: output of the ETL pipeline, SQLite database containing messages and categories data
• models
  • train_classifier.py: machine learning pipeline scripts to train and export a classifier
  • classifier.pkl: output of the machine learning pipeline, i.e. trained model
• run.py: python file to run the web application
• templates: contain HTML templates for the web application

Getting Started

Dependencies

• Python 3.5+
• NumPy, Pandas for data manipulation
• Sciki-Learn machine learning library
• NLTK for natural language processing
• SQLalchemy python ORM
• Flask web application framework
• Plotly for data visualization

Instructions

1. Run the following commands 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 to run your web app. python run.py

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