https://github.com/heschmat/project-disaster-response
Analyze disaster data from Figure Eight to build a model for an API that classifies disaster messages.
https://github.com/heschmat/project-disaster-response
class-imbalance deployment etl-pipeline machine-learning machine-learning-pipeline
Last synced: 2 months ago
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Analyze disaster data from Figure Eight to build a model for an API that classifies disaster messages.
- Host: GitHub
- URL: https://github.com/heschmat/project-disaster-response
- Owner: heschmat
- Created: 2021-03-12T10:14:26.000Z (about 4 years ago)
- Default Branch: master
- Last Pushed: 2021-03-14T14:02:27.000Z (about 4 years ago)
- Last Synced: 2025-01-08T19:26:07.129Z (4 months ago)
- Topics: class-imbalance, deployment, etl-pipeline, machine-learning, machine-learning-pipeline
- Language: Jupyter Notebook
- Homepage:
- Size: 2.05 MB
- Stars: 0
- Watchers: 2
- Forks: 0
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
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README
# Disaster Response Project
Analyze disaster data from [Figure Eight][https://appen.com/] to build a model for an API that classifies disaster messages.
# Project Components
There are three components in this project.
1. ETL Pipeline
`process_data.py` is responsible for data cleaning pipeline:
- Loads the messages and categories datasets
- Merges the two datasets
- Cleans the data
- Stores it in a SQLite database
2. ML Pipeline
`train_classifier.py` builds 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
3. Flask Web App
The results will be displayed in a flask web app. To run the app, go to the `app` directory and run `python run.py`. Then you have to go to the following link: `http://localhost:3001/`.
```
(disaster_env) C> python run.py
Data Done!
* Serving Flask app "run" (lazy loading)
* Environment: production
WARNING: This is a development server. Do not use it in a production deployment.
Use a production WSGI server instead.
* Debug mode: on
* Restarting with stat
Data Done!
* Debugger is active!
* Debugger PIN: 101-974-328
* Running on http://0.0.0.0:3001/ (Press CTRL+C to quit)
```
# Project Interface
1. To run the app, simply go to the `app` directory and run `python run.py` in the command line.
2. To process a new dataset you need to go to the `datasets` directory. An example to do ETL would be `python process_data.py messages.csv categories.csv DisasterResponse.db`.
Here, first and 2nd arguments - after the script - are the message and categories dataset. The last argument is the path to save the cleaned data into a database.
```
(disaster_env) C> python process_data.py messages.csv categories.csv DisasterResponseDB.db
Loading data...
MESSAGES: messages.csv
CATEGORIES: categories.csv
Cleaning data...
Saving data...
DATABASE: DisasterResponseDB.db
Cleaned data saved to database!
```
3. To train the model, go to the `models` directory. To train the classifier run `python train_classifier.py ../datasets/DisasterResponse.db classifier.pkl`.
Here, first argument is where the cleaned data stored in the database, and the last argument is the name with which you want to save the newly created classifier.
# Libraries
- sqlalchemy
- pandas
- sklearn
- flask
- nltk
# Challenges
There are 36 categories in this dataset; 33 of the categories are flaged less than 20%. And 29 of them are flaged less than 10%. For this reason, `accuracy` is not a viable metric to judge the model performance. In this case, depending on the nature of the task, we may want to optimize the performance for `precision` or `recall`; or simply use `f1score` as a singular metric that takes both into account.