README.md

Disaster Response Pipeline Project

Web app where an emergency worker can input a new message and get classification results in several categories

General Information

With the help natural language processing, machine learning and data engineering skills I analyzed disaster data from Figure Eight to build a model for an API that classifies disaster message. The web app will also display visualizations of the data.

Specifications

The project includes the following files:

1. ETL Pipeline Python script, process_data.py, 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 Python script, train_classifier.py, 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. Flask Web App

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 in the app's directory to run your web app. python run.py

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