Disaster Response Pipeline Project

APP:

https://disaster-msg-classification.herokuapp.com/

Classifies message from a web UI into 36 different categories.

Loads messages and message categories data, trains a model on it and uses that model to classify a message into 36 categories on a web UI.

Install requirements:

Run the following commands in the project's root directory to install dependencies

pip install -r requirements.txt

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. Run the following command to run your web app.

   python app/run.py
   

3. Go to http://0.0.0.0:3001/ or http://localhost:3001/

Project Structure:

- app
| - template
| |- master.html  # main page of web app
| |- go.html  # classification result page of web app
|- run.py  # Flask file that runs app

- data
|- disaster_categories.csv  # data to process 
|- disaster_messages.csv  # data to process
|- process_data.py
|- InsertDatabaseName.db   # database to save clean data to

- models
|- train_classifier.py
|- classifier.pkl  # saved model 

- README.md
- requirements.txt

Project Components:

There are three components in this project.

1. ETL Pipeline

   In a Python script, `process_data.py`, a data cleaning pipeline that:

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

2. ML Pipeline

   In a Python script, `train_classifier.py`, a machine learning pipeline that:

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

3. App(WebUI)

Acknowledgement

Udacity for providing the WebUI template code.