Disaster Response Pipeline Project

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A Udacity Data Scientist Nanodegree Project

Table of Contents

1.Project_Description

2.Installation

3.Executing_Program

4.Authors

5.LICENSE

6.Acknowledgements

7.Screenshots

Project Description

This project is a part of the Udacity Data Scientist Nanodegree. This project is conating dataeset from pre-labeled tweets and messages from real-life disasters. The aim of this project is to build a model that categorizes messages.

Installation

python3 should be installed including these python liberals: NumPy SciPy Pandas Sciki-Learn Flask Plotly NLTK SQLalchemy

Executing Program

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 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

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

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

Authors

Authors * Abdulelah Asiri

LICENSE

Abduleha Asiri,

License under the MIT License

Acknowledgements

Must give credit to Figure Eight for providing messages dataset to train the model, Also Udacity for providing such a useful data scientist course.

Screenshots

1-The main page shows two graphs that represnet training dataset.

2. Here you can type a sentence to test the ML model performance

3. clicking Classify message to see the categories to show where the sentence related to.