/disasterresponse

My solution for the disaster response project at data science nanodegree udacity

Primary LanguagePythonMIT LicenseMIT

distaster response

This project contains code for my solution for the "disaster response" project at he nanodegree of udacity, data science.

Fetching Repository

git clone https://github.com/dariusgm/disasterresponse

Installation Native

You can install a webserver in case you want to play around with the code and add external resources to it. When you want to use python:

Install pyenv on your platform, see: https://github.com/pyenv/pyenv

pyenv install 3.9.2
pyenv local 3.9.2
python3 -m pip install --upgrade pip
pip install pipenv
pyenv rehash
pipenv install --dev --python 3.9.2
pipenv shell

Disaster Response Pipeline Project

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/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/

You can now access http://localhost:8888 and start playing around.

Or you can use ./all.sh for doing all steps in one

Repository Structure

├── app
│   ├── templates
│   │   ├── go.html - result page for predictions
│   │   └─── master.html - header and body part of index page
│   └─── run.py - start flask server and allow predicions (make sure you are in the app directory before you run it)
├── data
│   ├── disaster_categories.csv - raw category data provided by udacity
│   ├── disaster_messages.csv   - raw message data provided by udacity
│   └── process_data.py         - run ETL Pipeline to prepare the data for MLPipeline
├── models
│   └── train_classifer.py      - run MLPipeline, producing a model and a model meta file.
├── .gitignore
├── .python-version             - sets the python version for pyenv
├── .all.sh                     - run ETL Pipeline, than MLPipeline and finally start the webserver
├── LICENCE
├── Pipfile                     - set depedencies using pipenv
├── Pipfile.lock                - lock file for pipenv
├── README.md                   - this file