Airlines Classification using PySpark ML

Spark ML

• Dealing with large datasets and diverse data sources can be challenging when applying traditional machine learning techniques.
• Spark, a distributed processing engine utilizing the MapReduce framework, addresses these challenges in big data processing.

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Objective

This project focuses on Classification and Clustering in Spark MLlib using Airlines Data.

• Implementation includes Decision tree classifier, Random forest classifier, and K-Means clustering algorithms.

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Business Overview of Airlines Industry

• OpenFlights
• IBM Developer Exchange - Airline Data
• BTS - Popular Air Carrier Statistics

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S3 Link for Dataset

• s3://airlines123/airline/data.zip

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Tech Stack

• Language: Python
• Package: Pyspark
• Services: Spark

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Code Overview

• File Names:

  • DecisionTree.ipynb
  • RandomForest.ipynb
  • K_means.ipynb

• Datasets:

  • data.zip
  • Social_Network_Ads.csv

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Steps to Run

Command Prompt

1. Execute using Python script:

   <spark_path> spark-submit <file_path>
   

   • <spark_path>: Path to Spark installation
   • <file_path>: Path to the script file

   Example:

   <C:\Users\admin\Desktop\spark\bin>spark-submit C:\Users\admin\Desktop\sparkml\DecisionTree.py>
   

IPython

1. Modular Code
   • Create a virtual environment
   • Install requirements: pip install -r requirements.txt
   • Run code: python DecisionTree.py
   • Check output for all visualizations

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