Disaster Response Pipeline Project

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
Go to app directory: cd app
Run your web app: python run.py
Click the PREVIEW button to open the homepage
The homepage shows master.html file, which is made of two parts

Fig 1. master.html: classify message

Fig 2. go.html: message category-labels

As shown in the above figures, part one is composed of classification of messages, master.html has a text input where a message can be entered, and below it there is a button Classify Message. Clicking on the button submits a request, and returns a response with the message category-labels.

Fig 3. master.html: simple plots

As shown in figures 3 and 4, part two is composed of plots, there are simple and multicategory plots. There are two such simple plots - each of which contain a sorted representation of a pandas series. The other plots are similar sorted representations of grouped data. Because these plots sort after grouping, they are multicategory in nature due to the grouped data being used.

Fig 4. master.html: multicategory plots

pattmehta/response_pipeline_project