Disaster Response Pipeline Project
Data Engineering Section -- Project: Disaster Response Pipeline
Table of Contents
Project Motivation
This is a Udacity Nanodegree Project,we will analyzing disaster data to build a model for an API that classifies disaster messages. In this project you will find a data set Disaster Response Messages from Figure Eight containing real messages that were sent during disaster events. These messages are sorted into 36 specific categories such as Water, Hospitals, Aid-Related, that are specifically aimed at helping emergency personnel in their aid efforts.
You will see the result on a web app where an emergency worker can input a new message and get classification results on several categories. The web app will also display visualizations of the data.
Project Components : There are three components we'll need to complete for this project.
ETL Pipeline: process_data.py, a data cleaning pipeline that: Loads the messages and categories datasets Merges the two datasets Cleans the data Stores it in a SQLite database
ML Pipeline: train_classifier.py, a machine learning pipeline that: Loads data from the SQLite database Splits the dataset into training and test sets Builds a text processing and machine learning pipeline Trains and tunes a model using GridSearchCV Outputs results on the test set Exports the final model as a pickle file
Flask Web App:run.py We will be taking the user message and classify them into 36 categories.
Installation
For running this project,from requirements.txt with pip install requirements.txt you will install all necessary python packages for analysis and building models .
File Descriptions
- data/process_data.py: The ETL pipeline used to process and clean data in preparation for model building.
- models/train_classifier.py: The Machine Learning pipeline used to fit, tune, evaluate, and export the model to a Python pickle.
- app/templates/.html: HTML templates required for the web app.
- app/run.py: To start the Python server for the web app and render visualizations.
Instructions
- 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
- To run ETL pipeline that cleans data and stores in database
- Run the following command in the app's directory to run your web app.
python run.py - Go to http://0.0.0.0:3001/
Results
The main observations of the trained classifier can be seen by running this application.