iskatrina/NLP_DisasterResponse_massages_classifier

classifies massages into relavant categories

Disaster Response Pipeline Project

Overview

This project is part of Udacity Data Science project in collaboration with Figure Eight. During desasters there are a lot of messages simultaneously submitted of various needs, which might be timeconsuming to handle given the urgency of situation. Purpose of this project to build pipeline/model to classify each message by category of need (food , water, military, medical, child help , electricity ...) and send those in respective disaster relief agency for further faster help. Project uses Natural Language Processing model and Classification model, to categorize these events. The project includes a web app where an emergency worker can input a new message and get classification results in several categories.

Content

The project is divided into three components:

• ETL Pipeline: To load datasets, clean the data and store in one SQLite database
• ML Pipeline: To build a text processing and machine learning pipeline, train a model to classify text message in categories
• Flask Web App: To show model results in real time

Structure of project files

This provides an overall view of the project structure :

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
|- InsertDatabaseName.db # database to save clean data to
models
|- train_classifier.py
|- classifier.pkl # saved model
README.md

Instructions:

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/messages.csv data/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 app/run.py

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

Results

1. Input a message to get a result

2. See a result as number of highlighted categories message might belong to

3. Training Dataset overview

   • Messages overview by genre
   • Distribution of messages by Categories