In this project, I analyzed real disaster data provided by Figure Eight to build a model for an API that classifies disaster messages. I created a machine learning pipeline to categorize disaster events so that a users can have their messages sent to an appropriate disaster relief agency.
Run the following command in the app's directory to run your web app.
python run.py
Next, go to http://0.0.0.0:3001/
• Run.py – This Python file contains the following steps:
• Initializes a flask app
• Tokenizes and normalizes text
• Loads data from a database
• Loads a model from “Train_classifer”
• Returns a website that displays model results
• Template Folder – contains the html files
• Go.html – contains html code for master.html
• Master.html – allows users to enter messages that are then automatically classified
• Process_data – This Python file contains the following steps:
• Loads csv data containing category and messages data
• Cleans the data by splitting the category field and dropping duplicates
• Saves that data into a database.
• DisasterResponse.db – this database fie contains the clean data processed in “process_data.py”
• Disaster_categories.csv – this text file contains a column with concatenates category names
• Disaster_messages.csv – this text file contains messages typed by disaster victims
• Train_classifer – This Python file contains the following steps:
• Tokenizes disaster message text, normalizes that text to lower case, and removes stop words
• Builds an Adaboost model that uses grid search to optimize it’s hyperparameters
• Evaluates the model and predicts the categories of messages
• The trained model is saved as “pickle” into the run.py file