Disaster Response Pipeline Project

Description

This is a project to create a disaster reponse pipeline using the Firgure Eight dataset. The dataset contains pre-labeled messages from an actual disaster. The labels are assigned with an intent to not only categorize the message as a request or not but also, it is used to classify and check if it is a call for help so that appropriate action can be taken.

This project is divided into 3 parts:

1. Data Cleaning and preprocessing, an ETL pripeline is created clean the data and store them in a structured database (SQLite)
2. A Machine learning pipeling to classify the messages amongst the present 36 categories
3. A Web app to display the results

Installation and Usage

Dependencies

1. Interpreter: Python 3.6+
2. Processing libraries: Numpy, Pandas, Scikit-learn, NLTK
3. DB connect library: SQLalchemy
4. For Web app: Flask, Ploty

Usage

1. Clone the repo using:

git clone https://github.com/kashyaparjun/DisasterResponse.git

2. Download the saved model pickle file from the below link, unzip it and put it inside the models folder:

https://drive.google.com/file/d/1Oo2fwvLpkIVrS-6f2twfmLxSvQ5ilvrY/view?usp=sharing

2. Run the following commands in the DisasterResponse directory:
   • ETL pipeline to import data, clean it and store into SQLite DB:

   python data/process_data.py data/disaster_messages.csv data/disaster_categories.csv data/DataResponse.db
   

   • ML pipeline and saving the model (This might take a while to run, be patient!):

   python model/train_classifier.py data/DataResponse.db models/classifier.pkl
   

3. Finally, run the web app by running:

python app/run.py

4. Go the URL:

http://0.0.0.0:3001

License

MIT

Acknowledgements

1. Figure Eight for providing pre-labeled messages dataset