s-arora1987 / Disaster-Response-App

An application for categorizing the disaster events based on messages

Disaster-Response-App

The purpose of this project is to create a web application that categorizes the messages/ tweets associated with real-life disasters. The motivation behind this applications is to have a faster anlaysis about what kind of help is needed by person sending message to disaster responders.

Contents and Purpose of different files.

Two csv files have data containing messages/tweets during real-life disasters, and the categories messages can be divided in (aid related, search & rescue, water, medical help, .. etc). After some pre-processing in file workspace/data/process_data.py, that data is saved in a SQL database called DisasterResponse. The details of this code are in ETL_Pipeline_Preparation notebook. The code in file workspace/data/train_classifier.py uses saved data to train a multi output classifier and saves result in pickle format; a detailed code for training classifier and tuning hyperparameters is in ML_Pipeline_Preparation notebook. File workspace/app/run.py creates a Flask web app to visualize some insights about of dataset, and to predict the category of input message using the classifier trained in workspace/data/train_classifier.py

Directory structure of Disaster-Response-App/workspace:

• 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

Instructions for setting up application

Install Python 3.x and following dependencies:

• NumPy
• Pandas
• Matplotlib
• Json
• Plotly
• Nltk
• Flask
• Sklearn
• Sqlalchemy
• Sys
• Re
• Pickle

Instructions for running the application:

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/

Example insight about dataset

Histogram for messages so far in different categories