This project aims to analyze disaster data from appen to build a model for an API that classifies disaster messages.

There are three components you'll need to complete for this project.

1. ETL Pipeline
2. ML Pipeline
3. Flask Web App
4. Github and Code Quality
5. Instructions

In a Python script, process_data.py, i did a data cleaning pipeline that:

• Loads the messages and categories datasets
• Merges the two datasets
• Cleans the data
• Stores it in a SQLite database

In a Python script, train_classifier.py, i did a machine learning pipeline that:

• Loads data from the SQLite database
• Splits the dataset into training and test sets
• Builds a text processing and machine learning pipeline
• Trains and tunes a model using GridSearchCV
• Outputs results on the test set
• Exports the final model as a pickle file

• Modify file paths for database and model
• Add data visualizations using Plotly in the web app.

The project graded based on the following:

• Use of Git and Github
• Strong documentation
• Clean and modular code

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 https://0.0.0.0:3001/