syedaws123uk / disaster-response-pipelines

A machine learning pipeline to categorize emergency messages based on the needs communicated by the sender.

Disaster-Response-Pipelines

Required libraries

• nltk 3.3.0
• numpy 1.15.2
• pandas 0.23.4
• scikit-learn 0.20.0
• sqlalchemy 1.2.12

Motivation

In this project, It will provide disaster responses to analyze data from Figure Eight to build a model for an API that classifies disaster messages.

This project will include a web app where an emergency worker can input a new message and get classification results in several categories. The web app will also display visualizations of the data.

Below are a few screenshots of the web app.

Files

• ETL Pipeline Preparation.ipynb: Description for workspace/data/process_data.py
• ML Pipeline Preparation.ipynb: Description for workspace/model/train_classifier.py
• workspace/data/process_data.py: A data cleaning pipeline that:
  • Loads the messages and categories datasets
  • Merges the two datasets
  • Cleans the data
  • Stores it in a SQLite database
• workspace/model/train_classifier.py: A machine learning pipeline that:
  • Loads data from the SQLite database
  • Splits the dataset into training and test sets
  • Builds a text processing and machine learning pipeline
  • Trains and tunes a model using GridSearchCV
  • Outputs results on the test set
  • Exports the final model as a pickle file

Acknowledgements

I wish to thank Figure Eight for dataset, and thank Udacity for advice and review.