Disaster-Response-Piplines

Analyzing disaster data from Figure Eight to build a model for an API that classifies disaster messages.

Acknowledgement

• UDACITY (DSND).
• FIGUREIGHT (Disaster Response Data).

Motivation

In this project, I worked worked with a lot of exciting topics, like:

• ETL Pipeline (Pandas-Numpy ...etc).
• NLP (NLTK).
• ML Pipeline (SKLearn).
• Delpoyment (Flask).
• Relational Databases (SQLite).

File Description

• app folder:
  • run.py: we app intialzaton.
• data folder:
  • check_db_results.ipynb: to check the db contents.
  • disaster_categories.csv: data to process.
  • disaster_messages.csv: data to process.
  • DisasterResponse.db: resulted db file (from ETL).
  • process_data.py: ETL pipeline py file.
• models folder:
  • model.pkl: classification model.
  • train_classifier.py: ML pipeline.
  • disaster_messages.csv: data to process.
• ML Piplene Notebook.
• ETL Piplene Notebook.

Installation

Use the package manager pip to install the libraries in requirments file.

pip install -r requirements.txt

Project Components

There are three components on this project.

1. ETL Pipeline

to run the ETL Pupeline you need three command line arguments:

• disaster_messages.csv file.
• disaster_categories.csv file.
• The DataBaseName.

python -m process_data disaster_messages.csv disaster_categories.csv DisasterResponse

• Loading the messages and categories datasets.
• Merging the two datasets
• Data Cleaning.
• Storing the data in a SQLite database.

2. ML Pipeline

python -m train_classifier ../data/DisasterResponse.db model.pkl

• Loading data from the SQLite database.
• Splitting the dataset into training and test sets.
• Building a text processing and machine learning pipeline.
• Training and tuning a model using GridSearchCV.
• Showing results on the test set.
• Exporting the final model as a pickle file.

3. Flask Web App

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

• Go to http://127.0.0.1:5000/