This project build ETL & Machine Learning pipelines to build a model for an API that classifies disaster messages

Disaster text messages from Figure Eight

• disaster _categories.csv
• disaster_messages.csv

1. Built ETL pipeline with Python that extract, clean and load the emergency messages to a SQLite database
2. create a machine learning pipeline that uses NLTK, as well as scikit-learn's Pipeline and GridSearchCV to output a final model that uses the message column to predict classifications for 36 categories (multi-output classification)
3. Create a Flask App that contain data visulizations and allow users to use the trained model to classify new messages.

1. 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

2. Run the following command in the app's directory to run your web app. python run.py

3. Go to http://0.0.0.0:3001/