Disaster-Response-Pipeline

ETL Pipeline

In a Python script, process_data.py:

Loads the messages and categories datasets
Merges the two datasets
Cleans the data
Stores it in a SQLite database

ML Pipeline

In a Python script, train_classifier.py:

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

Project Motivation

In this project, I apply skills I learned in Data Engineering Section to analyze disaster data from Figure Eight to build a model for an API that classifies disaster messages.

Instructions:

Run run.py directly if DisasterResponse.db and claasifier.pkl already exist.
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

Run the following command in the app's directory to run your web app.

python run.py

Tanay0510/Disaster-Response-Pipeline