/ml_disaster_response_pipeline

Data Science

Primary LanguagePython

Disaster Response Pipeline Project

Summary:

The primary goal of this project is to develop a model which can be exposed via a flask application which provides a easy way to categorize the disaster related messages into one of the 36 categories.

Files:

  • data\process_data.py - ETL pipeline to process, clean the data and save it into database.
  • models\train_classifier.py - ML pipeline to train the model based on cleaned data and save the model into .pkl file
  • models\classifier.pkl - Saved model parameters
  • app\run.py - Flask application with endpoints (loads test data and saved model)
  • requirements.txt - List of all python libraries used for the whole application.

Instructions:

  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. Go to app directory: cd app

  3. Run your web app: python run.py

  4. Click the PREVIEW button to open the homepage