j450h1/udacity-data-scientist-nanodegree-project-2-disaster-response-pipelines

In this project, I've analyzed disaster data from Figure Eight and built a model for an API that classifies disaster messages.

udacity-data-scientist-nanodegree-project-2-disaster-response-pipelines

In this project, I've analyzed disaster data from Figure Eight and built a model for an API that classifies disaster messages.

This project consists of three parts:

1. ETL Pipeline

process_data.py writes a data cleaning pipeline that:

• Loads the messages and categories datasets
• Merges the two datasets
• Cleans the data
• Stores it in a SQLite database

2. ML Pipeline

train_classifier.py writes 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

3. Flask Web App

• Displays visualizations about the training data using Plotly
• Runs the model on new messages that you enter yourself

Project File Tree

- app
| - template
| |- master.html  # main page of web app
| |- go.html  # classification result page of web app
|- run.py  # Flask file that runs app

- data
|- disaster_categories.csv  # data to process
|- disaster_messages.csv  # data to process
|- process_data.py
|- DisasterResponse.db   # database to save clean data to

- models
|- train_classifier.py
|- classifier.pkl  # saved model

- img
|- screenshot.png # screenshot of the web app

- README.md # this file