GitHub / ash-0521 / Ensuring-Smiles-using-Spark-ML
The primary objective of this study is to explore the feasibility of using machine learning algorithms to classify health insurance plans based on their coverage for routine dental services. To achieve this, I used six different classification algorithms: LR, DT, RF, GBT, SVM, FM(Tech: PySpark, SQL, Databricks, Zeppelin books, Hadoop, Spark-Submit)
JSON API: http://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/ash-0521%2FEnsuring-Smiles-using-Spark-ML
PURL: pkg:github/ash-0521/Ensuring-Smiles-using-Spark-ML
Stars: 0
Forks: 0
Open issues: 0
License: None
Language: Python
Size: 15.4 MB
Dependencies parsed at: Pending
Created at: about 2 years ago
Updated at: almost 2 years ago
Pushed at: about 2 years ago
Last synced at: almost 2 years ago
Topics: databricks, decison-trees, factorization-machines, gradient-boosting, hadoop-mapreduce, logistic-regression, pyspark, python, random-forest, spark-submit, sql, support-vector-machine, zeppelin-notebook