1. Installation
2. Project Motivation
3. Project Descriptions
4. Files Descriptions
5. Instructions

All libraries are available in Anaconda distribution of Python. The used libraries are:

• pandas
• re
• sys
• json
• sklearn
• nltk
• sqlalchemy
• pickle
• Flask
• plotly
• sqlite3

The code should run using Python versions 3.*.

The goal of the project is to classify the disaster messages into categories. In this project, I analyzed disaster data from Figure Eight to build a model for an API that classifies disaster messages. Through a web app, the user can input a new message and get classification results in several categories. The web app also display visualizations of the data.

The project has three componants which are:

1. ETL Pipeline: process_data.py file contain the script to create ETL pipline which:

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

2. ML Pipeline: train_classifier.py file contain the script to create ML pipline which:

• 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: the web app enables the user to enter a disaster message, and then view the categories of the message.

The web app also contains some visualizations that describe the data.

The files structure is arranged as below:

- README.md: read me file
- ETL Pipeline Preparation.ipynb: contains ETL pipeline preparation code
- ML Pipeline Preparation.ipynb: contains ML pipeline preparation code
- workspace
	- \app
		- run.py: flask file to run the app
	- \templates
		- master.html: main page of the web application 
		- go.html: result web page
	- \data
		- disaster_categories.csv: categories dataset
		- disaster_messages.csv: messages dataset
		- DisasterResponse.db: disaster response database
		- process_data.py: ETL process
	- \models
		- train_classifier.py: classification code

To execute the app follow the 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/