Disaster-Response-Pipeline

Project Overview:

In the Project Workspace, you'll find a data set containing real messages that were sent during disaster events. You will be creating a machine learning pipeline to categorize these events so that you can send the messages to an appropriate disaster relief agency.

Your project will include a web app where an emergency worker can input a new message and get classification results in several categories. The web app will also display visualizations of the data. Project Components There are three components you'll need to complete for this project.

1. ETL Pipeline

In a Python script, process_data.py, write 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 , write 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. Flask Web App

We are providing much of the flask web app for you, but feel free to add extra features depending on your knowledge of flask, html, css and javascript. For this part, you'll need to:

Modify file paths for database and model as needed Add data visualizations using Plotly in the web app. One example is provided for you Instructions

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/