/Disaster-Response-Pipeline

A webapp that analyzes message data for disaster response and shows classification result

Primary LanguageJupyter Notebook

Disaster Response Pipeline

Table of Contents

  1. Installation
  2. About the Project
  3. File Descriptions
  4. Results

Installation

  1. Dependencies needed. Use pip install <dependency>

    • Machine Learning Libraries: Numpy, Pandas, Sklearn
    • Natural Language Process Libraries: NLTK
    • SQLlite Database Libraries: SQLalchemy
    • Web App and Data Visualization: Flask, Plotly

  2. 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

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

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

About the Project

This project is about analyzing message data for disaster response. The data gotten from Figure Eight is used to build a model that classifies disaster messages and web app where an respondent can input a new message and get classification results in several categories

File Descriptions 

- 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  # categories data to process 
|- disaster_messages.csv  # messages data to process
|- process_data.py        # cleaning the data
|- ETL Pipeline Preparation.ipynb    # notebook is same as the process_data.py

- models
|- train_classifier.py      #running this gives the model
|- ML Pipeline Preparation.ipynb  # notebook is same as the train_classifier.py   

- README.md

Results

The app shows model results Webapp Screenshot