Disaster Response Pipeline Project

Introduction

If a disaster occurs in the world, disaster response organizations receive thousands of messages. In order to be able to react very promptly to such messages, a targeted classification using machine learning algorithms is required I have developed a web application so that experts from such organizations can quickly classify and forward such reports. The application uses a classifier that was trained on the data described below.

Data

Data includes 2 csv files:

1. disaster_messgaes.csv: Messages data.
2. disaster_categories.csv: Disaster categories of messages.

Folder Structure

│   README.md
│   requirements.txt
│
├───app
│   │   run.py
│   │
│   └───templates
│           go.html
│           master.html
│
├───data
│       disaster_categories.csv
│       disaster_messgaes.csv
│       DisasterResponse.db
│       process_data.py
│
├───models
│       classifier.pkl
│       train_classifier.py

Files

• process_data.py: Script that:

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

• train_classifier.py: 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

• run.py: Main file to run Flask app that:

  • Classifies real time input text messages
  • Shows data visualizations

• requirements.txt: List of required Python libraries

Instructions:

1. Make sure to install Python3
2. Navigate to the project's root directory in your terminal
3. Create a virtual enviroment

 python -m venv env

Note that if you are on a Mac you should use python3 instead

Activate your env

4. Install the project requirements:

pip install -r requirements

5. After intalling the requirements, 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

6. Run the following command in the app's directory to run your web app. python run.py

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