README.md

Disaster Response Pipeline Project

This project provides a web app for classifying a message into 36 possible disaster response related categories. We use a multi class classifier algorithm to suggest all the possible categories a message can belong to. The visualizations on the home page show the distributions of messages across genres and categories in the dataset from Figure Eight.

Installations

Your python environment would need the following packages installed. Specifying the versions used while working on the projects.

• Flask==1.1.2
• numpy==1.20.2
• nltk==3.6.1
• pandas==1.2.4
• plotly==4.14.3
• requests==2.25.1
• scikit-learn==0.24.1
• SQLAlchemy==1.4.9

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/

File Structure and details

The files in this repository have the following structure:

- 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  # data to process 
|- disaster_messages.csv  # data to process
|- process_data.py
|- InsertDatabaseName.db   # database to save clean data to

- models
|- train_classifier.py
|- classifier.pkl  # saved model 

- visualization.png # visualization from dataset

- README.md

Visual Insight on Dataset