Disaster Response Pipeline Project

Data Scientist Nanodegree Of Udacity

data set containing real messages that were sent during disaster events. We will be creating a machine learning pipeline to categorize these events so that we can send the messages to an appropriate disaster relief agency.

The 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

Files

• ETLPipelinePreparation.ipynb

  • performs the tasks required before the data is fed onto the machinelearning pipelines

• ML Pipeline Preparation.ipynb

  • builds a classifier to classify the messages

• app

  • temples
    • go.html
    • master.html

• data -disaster_categories (contains the different categories in which the data is classified) -disaster_messages (contains the different messages recorded) -Diasterresponse (the database file) -process.py (loads ,cleans and saves the data)

• models -train_classifier.py (builds the model to classify the data and optimizes it)

• README.md

  • Markdown file that summarizes this repository

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/