0xyuzi / disaster_response_pipeline

Multioutput classifier model to label emergency type based on the messeage input

Disaster_Response_Pipeline

Project Description

In this project, you'll apply these skills to analyze disaster data from Figure Eight to build a model for an API that classifies disaster messages. A data set containing real messages that were sent during disaster events. A machine learning pipeline to categorize these events so that messages to an appropriate disaster relief agency. The project includes 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

File Description

Disaster_Response_Pipeline
|- 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
  |- ETL Pipeline Preparation.ipynb
  |- DisasterResponse.db   # database to save clean data to

|- models
  |- train_classifier.py
  |- classifier.pkl  # saved model
  |- ML Pipeline Preparation.ipynb  # saved model

Instructions

1. Under the data directory, run the ETL pipeline that cleans and store the cleaned data in to SQLite database in the command prompt python data/process_data.py data/disaster_messages.csv data/disaster_categories.csv data/DisasterResponse.db
2. Under the model directory, run the ML pipeline to train the classifier with GridSearchCV and save to the pickle file in the command prompt python models/train_classifier.py data/DisasterResponse.db models/classifier.pkl
3. Under the the app directory, run the web app in the command prompt python run.py
4. Open the http://localhost:3001 to check the web app.

Installations

NLTK,SQLAlchecmy, sklearn, xgboost

Licensing

It is available below under MIT license.

Acknowledge

This project is under the Udacity Data Science Nanodegree.