In this course, you've learned and built on your data engineering skills to expand your opportunities and potential as a data scientist. In this project, you'll apply these skills to analyze disaster data from Figure Eight to build a model for an API that classifies disaster messages.
In the Project Workspace, you'll find a data set containing real messages that were sent during disaster events. You will be creating a machine learning pipeline to categorize these events so that you can send the messages to an appropriate disaster relief agency.
Your project will include a web app where an emergency worker can input a new message and get classification results on several categories. The web app will also display visualizations of the data. This project will show off your software skills, including your ability to create basic data pipelines and write clean, organized code!
There are three components we'll need to complete for this project.
ETL Pipeline In a Python script, process_data.py, write 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 a Python script, train_classifier.py, write 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 We will be taking the user message and classify them into 36 categories. There are some beautiful visualization of the data as well
All of the necessary criteria for developing the pipeline and web app is given in the rubric below https://review.udacity.com/#!/rubrics/1565/view
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/
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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/