This portfolio project's purpose is to apply data engineering to classify disaster response messages by machine learning.
I use python 3.5 to create this project and the main libraries I used are:
sikit-learn, nltk, Flask, gunicorn, numpy, pandas, plotly, sqlalchemy, jsonschema, punkt, wordnet, stopwords
run.py: flask file to run the web application
templates: folder containing the html templates
DisasterResponse.db: created database from transformed and cleaned data.
disaster_categories.csv: dataset including all the categories.
disaster_messages.csv: dataset including all the messages.
process_data.py: reads in the data, cleans and stores it in a SQL database.
train_classifier.py: created database from transformed and cleaned data.
frontpage of result
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
Run the following command in the app's directory to run your web app. python run.py
Go to http://0.0.0.0:3001/