Data-Science-Nanodegree_Project#2

Table of contents:

1. Introduction
2. Installations
3. Project Components
4. File Descriptions
5. Licensing, Authors, Acknowledgements, etc.

Introduction

In this project, we will analyze disaster data to build a model for an API that classifies disaster messages. The data contains real messages that were sent during disaster events. We will create a machine learning pipeline to categorize these events so that you can send the messages to an appropriate disaster relief agency. This 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. Below are a few screenshots of the web app.

Distribution of Message Genres:

Distribution of Message Categories:

Test example:

Installations

You need to install Python3 and the following packages:

• pandas
• tqdm
• numpy
• sklearn
• nltk
• sqlalchemy
• pickle
• flask
• plotly

Project Components

There are three componentsof this project:

1. ETL Pipeline In a Python script, process_data.py does data cleaning pipeline that:

1. Loads the messages and categories datasets
2. Merges the two datasets
3. Cleans the data
4. Stores it in a SQLite database

2. ML Pipeline In a Python script, train_classifier.py writes a machine learning pipeline that:

1. Loads data from the SQLite database
2. Splits the dataset into training and test sets
3. Builds a text processing and machine learning pipeline
4. Trains and tunes a model using GridSearchCV
5. Outputs results on the test set
6. Exports the final model as a pickle file

3. Flask Web App This components will display the results in a Flask web app.

File Descriptions

- app
| - template
| |- master.html  # main page of web app
| |- go.html  # classification result page of web app
|- run.py  # Flask file that runs app

- data
|- disaster_categories.csv  # data to process 
|- disaster_messages.csv  # data to process
|- process_data.py
|- InsertDatabaseName.db   # database to save clean data to

- models
|- train_classifier.py
|- classifier.pkl  # saved model 

- screenshots
|- screenshot1.png
|- screenshot2.png 
|- screenshot3.png

- README.md

to run the app, go to the app folder then python run.py

Licensing, Authors, Acknowledgements, etc.

Thanks for Udacity for providing this fun project :)!