Disaster Response Udacity Pipeline

Figure Eight is a company that provides datasets for data analysis and delivered us a dataset with messages classified into different categories to analyze emergency response messages.

Using Machine Learning we will be able to predict the category of the message

The process for this exercise is as follows:

1. Data Cleaning and Processing

Clean the data so that it can be used in a Machine Learning model (https://github.com/restevesd/Clasificacion/blob/master/data/process_data.py).

2. Trainign Model

We use a pipeline to automate tasks and a prediction model is made. See script in (https://github.com/restevesd/Clasificacion/blob/master/models/train_classifier.py).

3. Model in production

Execute intructions below.

Instructions:

Run the following instructions in the directory root directory of the project to set up all assets:

To run ETL:

python data/process_data.py data/disaster_messages.csv data/disaster_categories.csv data/DisasterResponse.db

To run ML pipeline:

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

Go to http://0.0.0.0:3001/

File structure of project:

1. ../app - folder for web app

   ../app/run.py - flask web app

   ../templates - .html templates

2. ../data - folder for files for the datasets

   ../data/disaster_categories.csv - raw file containing the categories

   ../data/disaster_messages.csv - raw file containing the messages

   ../data/process_data.py

   ../data/DisasterResponse.db - database for the clean data

3. ../models - folder for the classifier model and pickle file

   ../models/train_classifier.py - model training script

   ../models/classifier.pkl - saved model when running python train_classifier.py

4. README.md