hungnguyendinh1999/disaster_response_pipeline_project

Disaster Response Pipeline Project

Content

• Project Description
• Run Instructions
• Environment Setup

Project Description

• Data Processing Assessing and cleaning the data, so that it can be utilized by machine learning algorithms.

• Model training Data was passed through a pipeline and a prediction model is made.

• Prediction and Visualization: a webapp where user input emergency messages and see visualization of distribution of genres and categories.

Run Instructions:

0. Make sure your environment is setup correctly (I use Python 3.6 with conda full setup).

1. 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, go to data folder and run: 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, go to models folder and run: python models/train_classifier.py data/DisasterResponse.db models/classifier.pkl

2. Run the following command in the app's directory to run your web app. cd app python run.py

3. Go to http://0.0.0.0:3001/

Environment Setup

NOTE: I use Python 3.6. All versions of packages should be in the requirements.txt file. FYI macOS

I exported the requirements.txt file. You can just do pip install -r requirements.txt to install required packages for your environment.

For Anaconda users:

1. Create Environment:

   • Run this line in Terminal to install all ~290 packages from Anaconda conda create -n env_full anaconda python=3.6

   • Otherwise, run this to create an enviroment and install only the necessary on for this project. conda create -n env --file requirements.txt

2. Activate Environment
   • conda activate env_full
3. When not used, deactivate Environment
   • conda deactivate
4. To check all packages installed
   • conda list -n env_full