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https://github.com/cloudera/cml_amp_canceled_flight_prediction

Perform analytics on a large airline dataset with Spark and build an XGBoost model to predict flight cancellations.
https://github.com/cloudera/cml_amp_canceled_flight_prediction

binary-classification flask pyspark xgboost

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Perform analytics on a large airline dataset with Spark and build an XGBoost model to predict flight cancellations.

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README 

        
# Canceled Flight Prediction

This project is a Cloudera Machine Learning ([CML](https://www.cloudera.com/products/machine-learning.html)) **Applied Machine Learning Prototype** and has all the code and data needed to deploy an end-to-end machine learning project on a running CML instance. 



> NOTE: This AMP has been updated to showcase the Cloudera Data Connections and Exploratory Data Science and Visualization capabilities. To make use of these new features, a CML instance provisioned with a deployed CDW data catalog is required. For those on CDSW or without the Cloudera suite, the AMP will still function without these features by accessing a local version of the preprocessed dataset. 



![app](images/app.png)



The primary goal of this repository is to build a gradient boosted (XGBoost) classification model to predict the likelihood of a flight being canceled based on years of historical records. To achieve that goal, this project demonstrates the end-to-end processing needed to take a large, raw dataset and transform it into a clean, stripped down dataset for model training and inference using Spark on CML. Additionally, this project deploys a hosted model and front-end application to allow users to interact with the trained model. 



The dataset used in this project come from [Kaggle](https://www.kaggle.com/yuanyuwendymu/airline-delay-and-cancellation-data-2009-2018).



## Project Structure



The project is organized with the following folder structure:



```

.

├── code/           # Backend scripts, and notebooks needed to create project artifacts

├── data/           # A post processed sample of the full dataset used for model training

├── app/            # Assets needed to support the front end application

├── images/         # A collection of images referenced in project docs

├── models/         # Directory to hold trained models

├── cdsw-build.sh   # Shell script used to build environment for experiments and models

├── README.md

├── LICENSE.txt

└── requirements.txt

```



By following the notebooks, scripts, and documentation in the `code` directory, you will understand how to perform similar tasks on CML, as well as how to use the platform's major features to your advantage. These features include:



- CDP data access through data connection snippets

- SQL-based and visual exploration of raw data

- Data ingestion, cleaning, and processing with Spark

- Streamlined model development

- Point-and-click model deployment to a RESTful API endpoint

- Application hosting for deploying frontend ML applications



We will focus our attention on working within CML, using all it has to offer, while glossing over the details that are simply standard data science, and in particular, pay special attention to data ingestion and processing at scale with Spark.



## Deploying on CML



There are three ways to launch the this prototype on CML:



1. **From Prototype Catalog** - Navigate to the Prototype Catalog on a CML workspace, select the "Airline Delay Prediction" tile, click "Launch as Project", click "Configure Project"

2. **As ML Prototype** - In a CML workspace, click "New Project", add a Project Name, select "ML Prototype" as the Initial Setup option, copy in the [repo URL](https://github.com/cloudera/CML_AMP_Canceled_Flight_Prediction), click "Create Project", click "Configure Project"



3. **Manual Setup** - In a CML workspace, click "New Project", add a Project Name, select "Git" as the Initial Setup option, copy in the [repo URL](CML_AMP_Canceled_Flight_Prediction), click "Create Project". Then, follow the steps listed [in this document](code/README.md) in order



If you deploy this project as an Applied ML Prototype (AMP) (options 1 or 2 above), you will need to specify whether to run the project with `STORAGE_MODE` set to `local` or `external`. Running in external mode requires having access to a Virtual Warehouse that has the flights dataset available. Running in local mode will bypass the data access and manipulation steps by using the `data/preprocessed_flight_data.tgz` file to train a model and deploy the application. While running the project as an AMP will install, setup, and build all project artifacts for you, it may still be instructive to review the documentation and files in the [code](code/) directory.



----

### **Note**

This project may fail to complete automated setup on a CML workspace for certain hardware configurations.  If this happens, simply follow the **Manual Setup** steps above to enable the project artefacts.