1. Description
2. Getting Started
   1. Dependencies
   2. Installing
   3. Executing Program
   4. Additional Material
3. Authors
4. License
5. Acknowledgement
6. Screenshots

This Project is part of Data Science Nanodegree Program by Udacity using data provided by Figure Eight. The dataset contains pre-labelled tweet and messages from real-world disasters. The purpose of the project is to build a pipeline using Natural Language Processing tools that categorize the messages. The project has three components:

1. Create an ETL Pipeline to extract data from source, clean data, complete other necessary data processing steps, and save the cleaned dataframe in sqlite databse.
2. Create a Machine Learning Pipeline to train a model that will classify messages into given categories.
3. Deploy the model to a Web App that will classify messages and display charts illustrating the dataset.

• Python 3.5+
• NumPy
• SciPy
• Pandas
• Sciki-Learn
• NLTK
• SQLalchemy
• Flask
• Plotly

Clone this GIT repository:

git clone https://github.com/mknox0826/DisasterResponsePipeline.git

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/DisasterCleaned.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/

This assignment required students to first plan out the ETL and ML pipelines in a jupyter notebook. These two notebooks can be found in the "data" and "models" folders, respectively, to get a better understanding of each step in the process. These two notebooks informed the finalized code that is run in the python scripts inExecuting Program.

• Mica Knox

• Udacity for providing such a complete Data Science Nanodegree Program
• Figure Eight for providing messages dataset to train my model

1. The main page displays graphs illustrating aspects of the training dataset provided by Figure 8.
2. This is an example of a message that can be classified by the model in the app.

3. This is an example of the classification results of the message above.