Skip to content

Latest commit

 

History

History
57 lines (42 loc) · 2.02 KB

README.md

File metadata and controls

57 lines (42 loc) · 2.02 KB

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