I use python 3.5 to create this project and the main libraries I used are:

• sikit-learn ==0.19.1
• nltk == 3.2.5
• Flask==1.0.2
• gunicorn==19.9.0
• numpy==1.15.0
• pandas==0.23.4
• plotly==3.3.0
• sqlalchemy==1.2.12
• jsonschema==2.6.0

Please check detailed version information in requirement.txt.

Project code is deployed a program as a web application in the internet. The web application Programming is a project in Udacity Data Scientist Nanodegree program. In this project, I analyze disaster data from Figure Eight to build a model for an API that classifies disaster messages. I find a data set containing real messages that were sent during disaster events.

I create a machine learning pipeline to categorize these events so that I can send the messages to an appropriate disaster relief agency. I split the data into a training set and a test set. Then,I create a machine learning pipeline that uses NLTK, as well as scikit-learn's Pipeline and GridSearchCV to output a final model. Finally, I export the model to a pickle file.

The project also include a web app with bootstap and Flask where an emergency worker can input a new message and get classification results in several categories. The web app also displays visualizations of the data as follows:

• \
  • README.md
  • ETL Pipeline Preparation.ipynb
  • ML Pipeline Preparation.ipynb
• \app
  • run.py
  • \templates
    • go.html
    • master.html
• \data
  • DisasterResponse.db
  • disaster_categories.csv
  • disaster_messages.csv
  • process_data.py
• \models
  • classifier.pkl : It is too big(about 2GB size) to be included in the github. To run ML pipeline that trains classifier and saves the trained model to classifier.pkl
  • train_classifier.py

 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`

 3. Go to http://0.0.0.0:3001/

• https://github.com/bin-wang-sh/DSND-Disaster-Response-Pipelines

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