1.Installation
2.Project Motivation
3.Project Descriptions
4.Files Descriptions
5.Instructions
6.Github Link
7.Acknowledgement

All libraries are available in Anaconda distribution of Python. The used libraries are:

pandas
re
sys
json
sklearn
nltk
sqlalchemy
pickle
Flask
plotly
sqlite3
The code should run using Python versions 3.*.

The goal of the project is to classify the disaster messages into categories. In this project, I analyzed disaster data from Figure Eight to build a model for an API that classifies disaster messages. Through a web app, the user can input a new message and get classification results in several categories. The web app also display visualizations of the data.

The project has three components which are:

1. ETL Pipeline: process_data.py file contain the script to create ETL pipeline which:
   a. Loads the messages and categories datasets
   b. Merges the two datasets
   c. Cleans the data
   d. Stores it in a SQLite database
   

2. ML Pipeline: train_classifier.py file contain the script to create ML pipeline which:
   a. Loads data from the SQLite database
   b. Splits the dataset into training and test sets
   c. Builds a text processing and machine learning pipeline
   d. Trains and tunes a model using GridSearchCV
   e. Outputs results on the test set
   f. Exports the final model as a pickle file
   

3. Flask Web App: The web app enables the user to enter a disaster message, and then view the categories of the message.
   

The web app also contains some visualizations that describe the data.

The files structure is arranged as below:

• app
  | - template
  | |- master.html # main page of web app
  | |- go.html # classification result page of web app
  |- run.py # Flask file that runs app
  

• data
  |- disaster_categories.csv # data to process
  |- disaster_messages.csv # data to process
  |- process_data.py
  |- DisasterDatabase.db # database to save clean data to
  

• models
  |- train_classifier.py
  |- classifier.pkl # saved model
  

• README.md
  

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/DisasterDatabase.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/
   

The Github link to see the code is https://github.com/Shivaae/Disaster-Response-Pipelines

I would like to thanks Figure Eight and Udacity for providing the Dataset.