Disaster Response Pipeline Web App with Figure Eight

Project Overview

The aim of this project is to build a machine learning pipeline to categorize emergency messages based on the needs communicated by the sender.

Files Description

• data/process_data.py: this code takes as an input .csv files that contain message data; data undergoes ETL Pipeline and get prepared to run Machine Learning Model
• models/train_classifier.py: this code takes as an input SQLite database produced by process_data.py and uses the data contained within it to train and tune a ML model for categorizing messages; the output is a pickle file containing fitted model; test evaluation metrics are also printed as part of the training process
• ETL_Pipeline_Preparation.ipynb: this code is for ML Pipeline and prediction of messages
• ML_Pipeline_Preparation.ipynb: this code was used in train_classifier.py
• data: folder contains sample messages and categories datasets in .csv format
• app: folder contains all of the files necessary to run and render the web app
• model: folder contains ML model

Instructions

run process_data.py

1. save the data folder in the current working directory and process_data.py in the data folder
2. from the current working directory, run the following command: python data/process_data.py data/disaster_messages.csv data/disaster_categories.csv data/DisasterResponse.db

run train_classifier.py

1. in current working directory, create a folder called 'models' and save train_classifier.py there
2. from current working directory, run the following command: python models/train_classifier.py data/DisasterResponse.db models/classifier.pkl

run web app

1. save app folder in current working directory
2. run following command in the app directory: python run.py
3. go to http://0.0.0.0:3001/

Acknowledgements

This project was completed as a part of Udacity Data Scientist Nanodegree.