This repository was written in HTML and Python , and requires the following Python packages: pandas, numpy, re, pickle, nltk, flask, json, plotly, sklearn, sqlalchemy, sys, warnings.
This code is designed to iniate a web app which an emergency operators could exploit during a disaster (e.g. an earthquake or Tsunami), to classify a disaster text messages into several categories which then can be transmited to the responsible entity
The app built to have an ML model to categorize every message received
- process_data.py: This python excutuble code takes as its input csv files containing message data and message categories (labels), and then creates a SQL database
- train_classifier.py: This code trains the ML model with the SQL data base
- ETL Pipeline Preparation.ipynb: process_data.py development procces
- ML Pipeline Preparation.ipynb: train_classifier.py. development procces
- data: This folder contains sample messages and categories datasets in csv format.
- app: cointains the run.py to iniate the web app.
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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
- To run ETL pipeline that cleans data and stores in database
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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/ Or Go to http://localhost:3001/
Screenshot 1: App analysis of the data base
Screenshot 2: App word search Page
This app was completed as part of the Udacity Data Scientist Nanodegree.