1. Description
2. Getting Started
   1. Prerequisites
   2. Data Preprocessing
   3. Model training
   4. PEP8 style / Pylint
   5. Model Evaluation
   6. App usage instructions
   7. Project folders structure
3. Author
4. License

The Project is part of Data Science Nanodegree Program by Udacity in collaboration with Figure Eight. It aims to build a machine learning pipeline to categorize emergency messages based on the needs communicated by the sender.

['request', 'offer', 'aid_related', 'medical_help', 'medical_products',
    'search_and_rescue', 'security', 'military', 'child_alone', 'water',
    'food', 'shelter', 'clothing', 'money', 'missing_people', 'refugees',
    'death', 'other_aid', 'infrastructure_related', 'transport',
    'buildings', 'electricity', 'tools', 'hospitals', 'shops',
    'aid_centers', 'other_infrastructure', 'weather_related', 'floods',
    'storm', 'fire', 'earthquake', 'cold', 'other_weather',
    'direct_report']

- app
| - template
| |- master.html  # main page of the web application
| |- go.html  # classification result page of the web application
|- run.py  # script for running the web application using Flask

- data
|- disaster_categories.csv  # messages categories dataset usesd for training the model
|- disaster_messages.csv  # messages dataset used for training the model
|- process_data.py # Script data processing
|- DisasterResponse.db   # database to save clean data to

- models
|- train_classifier.py # Model training script
|- classifier.pkl  # Model file

- notebooks
|- ETL Pipeline Preparation.ipynb
|- ML Pipeline Preparation.ipynb

• Create a Python 3.6 conda virtual environment conda create --name py36 python=3.6
• Activate the new environment conda activate py36
• Install required packages by running the following command in the app's directory pip install -r requirements.txt
• In order to be able to download the model file you need to have git-lfs installed and used in the local repository copy. For that, install Git LFS following the instructions in https://git-lfs.github.com/. Now install git-lfs in the local repository with git lfs install

Run the processing script in the data folder e.g. process_data.py disaster_messages.csv disaster_categories.csv DisasterResponse.db

Run the training script in the models folder e.g. train_classifier.py ../data/DisasterResponse.db classifier.pkl

You can check the scripts PEP8 style by running the following command in the project's top folder pylint --rcfile .pylintrc file_path. e.g.

pylint --rcfile .pylintrc models/train_classifier.py

F1 Score of the disaster messages categories

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/ You will get the home page with visualizations of the used training data set.

• The message catgeories are highlighted in green

Sample Output

• slitayem