Disaster Response Pipeline Project
Project Overview
In this project, I analyzed disaster data from Figure Eight to build a model for an API that classifies disaster messages.
The data set contains real messages that were sent during disaster events. I created a machine learning pipeline to categorize these events so that you can send the messages to an appropriate disaster relief agency.
I also included a web app where an emergency worker can input a new message and get classification results in several categories. The web app displays visualizations of the data.
Below are a few screenshots of the web app.
Project Components
There are three components in this project.
- ETL Pipeline In process_data.py, it has a data cleaning pipeline that:
Loads the messages and categories datasets Merges the two datasets Cleans the data Stores it in a SQLite database
- ML Pipeline In train_classifier.py, it has a machine learning pipeline that:
Loads data from the SQLite database Splits the dataset into training and test sets Builds a text processing and machine learning pipeline Trains and tunes a model using GridSearchCV Outputs results on the test set Exports the final model as a pickle file
- Flask Web App In the last step, I'll display my results in a Flask web app. It has three plotly visualization of EDA on the data, and a query UI that can classsify the message.
Instructions:
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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/