There is a huge opportunity of Machine Learning use cases to improve quick response in disaster recovery.In this project, I develop a machine learning pipeline to analyze real disaster data from Figure Eight with objective that these message categories will be viewed and sent to an appropriate disaster relief agency.
No extra libraries needs to be installed as all the libraries used here comes up wwith the Anaconda distribution with Python3.6.
Project Motivation Incident managment has huge oppertunity around ML use case as these get generated from huge event log data. Here I thought to analyze Incident event data to see what hidden patterns will help us to come up with impactful ML use case.
File Descriptions Below are the details of files:-
This project requires file:
app
# template # sub folder for html files
master.html # main page of web app
go.html # classification result page of web app
run.py # Flask file that runs app
models
train_classifier.py
classifier.pkl # will be saved model once you run the script
data
disaster_categories.csv # data to process
disaster_messages.csv # data to process
process_data.py
DisasterResponse.db # database to save clean data to
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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
- To run ETL pipeline that cleans data and stores in database
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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/ or use https://view6914b2f4-3001.udacity-student-workspaces.com/ inplace of view6914b2f4 use your space id and get space is using env|grep WORK in different terminal
Data is provided by Figure Eight.