Disaster Response Pipeline Project

Project Overview:

In this project, I will apply data engineering to analyze disaster data from Figure Eight to build a model for an API that classifies disaster messages. The initial dataset contains pre-labeled tweets and messages from real-life disasters. This project aims to build a Natural Language Processing tool that categorizes messages.

It is divided into the following sections:

1. Data Processing, ETL Pipeline to extract data from the source, clean data, and save them in a proper database structure.
2. Machine Learning Pipeline to train a model that can classify text messages in 36 categories.
3. Web Application using Flask to show model results and predictions in real-time.

File Description

disaster_response_pipeline
  |-- app
        |-- templates
                |-- go.html
                |-- master.html
        |-- run.py
  |-- data
        |-- disaster_message.csv
        |-- disaster_categories.csv
        |-- DisasterResponse.db
        |-- process_data.py
        |-- ETL Pipeline Preparation.ipynb
        |-- Twitter-sentiment-self-drive-DFE.csv
  |-- models
        |-- classifier_1.pkl
        |-- train_classifier.py
        |-- ML Pipeline Preparation.ipynb
  |-- README

Installation:

This project requires Python 3.x and the following Python libraries:

• Machine Learning Libraries: NumPy, SciPy, Pandas, Sciki-Learn
• Natural Language Process Libraries: NLTK
• SQLlite Database Libraries: SQLalchemy
• Web App and Data Visualization: Flask, Plotly

Instructions:

1. Clone this repository

git clone git@github.com:imadarsh1001/Disaster_Response_Pipeline_NLP.git

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/

Screenshots:

Home Page

Classify Messages Page

Input - Please, we need tents and food. We are in Kalupur, Thank you!

Licensing, Authors, Acknowledgements

Many thanks to Figure-8 for making this available to Udacity for training purposes. Special thanks to Udacity for the training. Feel free to utilize the contents of this while citing Udacity or figure-8 accordingly.