https://github.com/farheenb/suv-buyers-classification-in-python
Performed Classification on non-linearly separable datasets of SUV Buyers. Modeled all the classification techniques available to find the best algorithm that classify whether a person will buy a SUV or not. Used k-Fold Validation for all the techniques. Model Accuracy on test set are: Logistic Regression-89.00% KNN- 93.00% SVM-90.00% Kernel SVM-93.00%, Naive Bayer's-90.00%, Decision Tree-91.00%, Random Forest-91.00%
https://github.com/farheenb/suv-buyers-classification-in-python
Last synced: 7 months ago
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Performed Classification on non-linearly separable datasets of SUV Buyers. Modeled all the classification techniques available to find the best algorithm that classify whether a person will buy a SUV or not. Used k-Fold Validation for all the techniques. Model Accuracy on test set are: Logistic Regression-89.00% KNN- 93.00% SVM-90.00% Kernel SVM-93.00%, Naive Bayer's-90.00%, Decision Tree-91.00%, Random Forest-91.00%
- Host: GitHub
- URL: https://github.com/farheenb/suv-buyers-classification-in-python
- Owner: FarheenB
- Created: 2020-04-13T18:15:19.000Z (over 5 years ago)
- Default Branch: master
- Last Pushed: 2020-04-25T19:47:42.000Z (over 5 years ago)
- Last Synced: 2025-01-16T23:32:12.993Z (9 months ago)
- Language: Jupyter Notebook
- Homepage:
- Size: 947 KB
- Stars: 1
- Watchers: 2
- Forks: 0
- Open Issues: 0
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Metadata Files:
- Readme: README.md
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README
# SUV-Buyers-Classification-in-Python
Performed Classification on non-linearly separable datasets of SUV Buyers.
Modeled all the classification techniques available to find the best algorithm that classifies whether a person will buy a SUV or not.
Used k-Fold Validation for all the techniques.
### Model Accuracy on test set:
#### Logistic Regression-
89.00%
#### KNN Classifier- 93.00% SVM-
90.00%
#### Kernel SVM Classifier-
93.00%
#### Naive Bayer's Classifier-
90.00%
#### Decision Tree Classifier-
91.00%
#### Random Forest Classifier-
91.00%