Welcome to my Project! I'm Carlos. This project began July 2021.
The code requires Python versions of 3.* and general libraries available through the Anaconda package. In addition, the nltk package needs to be installed for the program to run successfully. For more details on the required packages, please refer to the files train_classifier.py and process_data.py.
Download the latest GitHub repository.
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 componants which are: ETL Pipeline: process_data.py file contain the script to create ETL pipline which:
Loads the messages and categories datasets.
Merges the two datasets
Cleans the data
Stores it in a SQLite database
ML Pipeline: train_classifier.py file contain the script to create ML pipline which:
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: 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.
Files Descriptions The files structure is arranged as below:
README.md: read me file
Project Components.txt: Project requirements to complete
workspace
\app
run.py: flask file to run the app
\templates
master.html: main page of the web application
go.html: result web page
\data
disaster_categories.csv: categories dataset
disaster_messages.csv: messages dataset
DisasterResponse.db: disaster response database
process_data.py: ETL process
\models
train_classifier.py: classification code
MLclassifier.pkl: model pickle file
\Pipeline Preparation
ETL Pipeline Preparation.py
ML Pipeline Preparation.py
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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
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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/
