1. Installation
2. Project Motivation
3. Project Components
4. Instructions
5. Licensing, Authors, and Acknowledgements

The main libraries needed to run the code are the following:

• Python 3.6.7 |Anaconda, Inc.|
• Scikit-Learn for Machine Learning algorithms
• NumPy for numerical vectorize calculations
• Pandas for data manipulation
• NLTK for Natural Language Processing
• sqlalchemy for interaction within databases

For this project, I designed an ETL pipeline, machine learning pipeline that uses Natural Language Processing (NLP) to analyze disaster data from FigureEight to build a model for an API that classifies disaster messages.

The project includes 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.

There are three main components for this project:

1. ETL Pipeline

The Python script, process_data.py, contains 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 the Python script, train_classifier.py, there is 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

Web App that shows data visualizations and allows a person to use the trained model to classify disaster messages.

1. Run the following commands in the project's root directory to set up the 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 the web app.

   python run.py

3. Go to http://0.0.0.0:3001/ (Go to your web app link)

Must give credit to FigureEight for the data and project idea. Author: Gustavo Cedeno following recommendations and requirements from Udacity's Data Science ND Program.