https://github.com/farheenb/suv-buyers-classification-with-kpca-in-python

Used Kernel PCA to extract the principle components of non-linearly separable dataset of SUV Buyers. Modeled Logistic Regression to classify whether a person will buy a SUV or not. Model Accuracy is 91.25%
https://github.com/farheenb/suv-buyers-classification-with-kpca-in-python

Last synced: 2 months ago
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Used Kernel PCA to extract the principle components of non-linearly separable dataset of SUV Buyers. Modeled Logistic Regression to classify whether a person will buy a SUV or not. Model Accuracy is 91.25%

• Host: GitHub
• URL: https://github.com/farheenb/suv-buyers-classification-with-kpca-in-python
• Owner: FarheenB
• Created: 2020-04-25T15:24:27.000Z (over 5 years ago)
• Default Branch: master
• Last Pushed: 2020-04-25T17:11:16.000Z (over 5 years ago)
• Last Synced: 2025-01-16T23:32:12.819Z (9 months ago)
• Language: Jupyter Notebook
• Homepage:
• Size: 53.7 KB
• Stars: 1
• Watchers: 2
• Forks: 1
• Open Issues: 0
• Metadata Files:
  • Readme: README.md

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README

          
# SUV-Buyers-Classification-with-kPCA-in-Python

Used Kernel PCA to extract the principle components of non-linearly separable dataset of SUV Buyers. Modeled Logistic Regression to classify whether a person will buy a SUV or not. 

#### Model Accuracy- 91.25%