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

disaster_response_pipeline
          |-- app
                |-- templates
                        |-- master.html         # Main page of the web app
                        |-- go.html             # Classification result page of the web app
                |-- run.py                      # Script for the app
          |-- data
                |-- disaster_message.csv        # Mesage Data
                |-- disaster_categories.csv     # categories Data
                |-- DisasterResponse.db         # Clean Data
                |-- process_data.py             # script for building an ETL pipeline and data cleaning
          |-- models
                |-- classifier.pkl              # saved model -> Random forest 
                |-- train_classifier.py         # script for building a ML pipeline (there is also AdaBoost model setup)

          |-- README

Results