hamadalaqeel / Disaster_Response_Pipelines_Project

Udacity Data Scientist Nanodegree Project

Disaster_Response_Pipelines_Project

Installation

Beyond the Anaconda distribution of Python, the following packages need to be installed for nltk:

• punkt
• wordnet
• stopwords

Project Motivation

This project is part of my Data Science Nanodegree program. I appled data engineering, natural language processing, and machine learning skills to analyze message data that people sent during disasters to build a model for an API that classifies disaster messages. These messages could potentially be sent to appropriate disaster relief agencies.

File Descriptions

• app:
  • run.py: Flask file to run the web application.
  • templates contains html file for the web applicatin.
• data:
  • disaster_categories.csv: dataset including all the categories.
  • disaster_messages.csv: dataset including all the messages.
  • ETL_Pipeline_Preparation.ipynb: Jupyter Notebook containing ETL pipeline scripts to read, clean, and save data into a database.
  • process_data.py: ETL pipeline scripts to read, clean, and save data into a database.
• images:
  • header.png: screenshot of the web page
  • plots.png: screenshots of the plots
• models:
  • ML_Pipeline_Preparation.ipynb: Jupyter Notebook, tokenize messages from clean data and create new columns through feature engineering. The data with new features are trained with a ML pipeline and pickled.
  • train_classifier.py: Script to tokenize messages from clean data and create new columns through feature engineering. The data with new features are trained with a ML pipeline and pickled.

Results

1. An ETL pipleline was built to read data from two csv files, clean data, and save data into a SQLite database.
2. A machine learning pipepline was developed to train a classifier to performs multi-output classification on the 36 categories in the dataset.
3. A Flask app was created to show data visualization and classify the message that user enters on the web page.

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/disaster_message_categories.db
• To run ML pipeline that trains classifier and saves python models/train_classifier.py data/disaster_message_categories.db models/model.p

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/

Licensing, Author, Acknowledgements

Credits must be given to Udacity for the starter codes and FigureEight for provding the data used by this project.