Disaster Response Pipeline

Build Pipelines to Classify Messages with Figure Eight

This project is my personal motivation which Aims to builds a data pipeline to prepare message data from major natural disasters around the world. I build an ETL and RandomForest pipeline to categorize emergency messages based on the needs communicated by the sender.

Main Steps

1. Clean data by Natural Language Processing (Normalized, tokenized and lemmatized the text messages)
2. Built up pipelines to train Random forest with grid search; Applied TF-IDF to assign the weights to words in the message
3. Use Random Forest in the final model and test results in the website

Data Source: Figure Eight, San Francisco, CA

Install the Requirements

If you are running this in your local environment, run conda install --file requirements.txt

Or pip install -r requirements.txt to install the required python module dependencies

Files in the Repository

app

• template
• master.html # main page of web app
• go.html # classification result page of web app
• run.py # Flask file that runs app

data

• disaster_categories.csv # data to process
• disaster_messages.csv # data to process
• process_data.py
• InsertDatabaseName.db # database to save clean data to

models

• train_classifier.py
• classifier.pkl # saved model

README.md

Instructions

Run the following commands in the project's root directory to set up 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/Disaster.db

• To run ML pipeline that trains classifier and saves - python models/train_classifier.py data/Disaster.db models/classifier.pkl

Running the Web App from the Project Workspace IDE

Step 1: Type in the command line: python run.py

Step 2: Open another Terminal Window, Type: env|grep WORK

Step 3: In a new web browser window, type in the following: https://SPACEID-3001.SPACEDOMAIN where SPACEID & SPACEDOMAIN are shown in step 2.

Environment and Libraries Used

Environment - Jupyer Notebook and Python IDE (Atom)

1. NumPy
2. Pandas
3. Scikit-Learn
4. Plotly
5. SQLAlchemy
6. Flask

Acknowledgement

I would like to thank Future Eight or providing the Data and Assistance.