This project is part of the Udacity Data Scientist Nanodegree program and involves the implementation of a natural language processing tool to analyze and classify disaster messages compiled by Figure Eight.
The project comprises the following sections:

1. Building an ETL Pipeline that
   • Loads the messages and categories datasets
   • Merges the two datasets
   • Cleans the data
   • Stores it in a SQLite database
2. Building an ML 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. Building a Flask Web App that
   • Visualizes features of the training data
   • Classifies messages entered by the user according to the ML model built in step 2

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/

│       ETL Pipeline Preparation.ipynb - Jupyter Notebook with ETL Pipeline
│       ML Pipeline Preparation.ipynb - Jupyter Notebook with Machine Learning Pipeline
│       README.md
│       
+------app
│       │       run.py - Flask app
│
│       +------templates
│       │               go.html - Template file
│       │               master.html - Main template file
|
+-----data
│       │       DisasterResponse.db - SQLite database with result of ETL Pipeline
│       │       disaster_categories.csv - CSV data file
│       │       disaster_messages.csv - CSV data file
│       │       process_data.py - ETL script file
|
+------models
        │       train_classifier.py Machine Learning pipeline script