Disaster Response Pipeline with Figure Eight
Apply Data Engineering to Build ETL & NLP Pipelines and Create an App for Disaster Relief using Flask
In this project, I am applying Data Engineering & Data Science skills to analyze disaster data from Figure Eight to build a model for an API that classifies disaster messages.
The project contains data set containing real messages that were sent during disaster events. I am creating a machine learning pipeline to categorize these events so that it can be sent to an appropriate disaster relief agency.
Project includes 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.
There are three components in this project.
- ETL Pipeline A Python script, process_data.py, 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 A Python script, train_classifier.py, 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