This Project is part of Data Science Nanodegree Program by Udacity in collaboration with Figure Eight. The dataset contains pre-labelled tweet and messages from real-life disaster events. The project aim is to build a Natural Language Processing (NLP) model to categorize messages on a real time basis.

This project is divided in the following key sections:

Processing data, building an ETL pipeline to extract data from source, clean the data and save them in a SQLite DB Build a machine learning pipeline to train the which can classify text message in various categories Run a web app which can show model results in real time

Getting Started

Dependencies Python 3.5+ Machine Learning Libraries: NumPy, SciPy, Pandas, Sciki-Learn Natural Language Process Libraries: NLTK SQLlite Database Libraqries: SQLalchemy Model Loading and Saving Library: Pickle Web App and Data Visualization: Flask, Plotly

How to run the program is in the workspace folder readme.

License License: MIT

Acknowledgements Udacity for providing an amazing Data Science Nanodegree Program Figure Eight for providing the relevant dataset to train the model

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

The ETL folder consists of preprocessing the data and storing it in the table before it is passed into the machine learning pipeline