Data Science Project
Title of dataset: "Kepler Exoplanet Search Results"
Source: Kaggle - https://www.kaggle.com/nasa/kepler-exoplanet-search-results
Authors: Matthew Bazzo, Soo Hyung Choe, Shiming Yan, Alex Zhang

• Developed a predictive and planet categorization model using supervised and unsupervised learning techniques respectively to predict whether a Kepler Object of Interest is classified as a candidate, confirmed or false positive.
• Derived an accuracy classification rate of 89% by utilizing the following classification techniques: Decision Tree, Randomforest, KNN, SVM, Neural Network.
  
  Refer to "Kepler-Data.pptx" for dataset
  Refer to "Kepler_Exoplanet_Analysis.Rmd" for analysis and code