zaveta / Disaster-Response-Pipelines

Build a Machine Learning Pipline to categorize emergency mesages based on the needs communicated by the sender.

Disaster Response Pipeline Project

Description

In this project, I applied 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.

Data Processing, ETL Pipeline to extract data from the source, clean data, and save them in a proper database structure. Machine Learning Pipeline to train and tunning a model able to classify text messages into appropriate categories. Web App to show model results in real-time.

File Description

• Jupiter_Notebooks_Pipline_Preparation

  • ETL Pipeline Preparation.ipynb
  • ML Pipeline Preparation.ipynb

• data

  • disaster_categories.csv: dataset including all the categories
  • disaster_messages.csv: dataset including all the messages
  • process_data.py: ETL pipeline scripts to read, clean, and save data into a database

• models

  • train_classifier.py: machine learning pipeline scripts to train and export a classifier

• app

  • run.py: Flask file to run the web application
  • templates contains html file for the web applicatin

Requirements

• Python 3

to run the web app

• pandas
• nltk
• flask
• plotly
• sqlalchemy

to use the pipelines

• numpy
• sklearn

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/

Licensing

Apache License 2.0

See the LICENSE file for details