Implemented and compared the performance of below Classifiers using Cross-validation & error metrics for classification Handwritten Digits.

1. Linear classifier
2. K-nearest neighbor classifier
3. RBF neural network
4. 1 & 2-hidden layer Neural Network

Notebook Descriptions:

1. Classification of Handwritten Digits using Logistic Regression Classifier.ipynb: Logistic Regression Modeled against scaled and unscaled data.

2. Change in Parameter Analysis for Logistic Regression.ipynb: Logistic Regression Performance analysis againt training set tuning & epoch tuning

3. Classification of Handwritten Digits using SVC.ipynb: SVC Modeled against scaled and unscaled data.

4. Change in Parameter Analysis for SVC.ipynb: SVC Performance analysis againt training set tuning & epoch tuning

5. Classification of Handwritten Digits using One Hidden Layer Neural Network.ipynb: 1 Hidden layer NN modelled and performance analysed for diff epochs, diff activation funcs & diff training set size

6. Classification of Handwritten Digits using Two Hidden Layer Neural Network.ipynb: 2 Hidden layer NN modelled and performance analysed for diff epochs, diff activation funcs & diff training set size

7. Classification of Handwritten Digits using Decision Tree .ipynb: Decision Tree Model of unscaled data.

8. Classification of Handwritten Digits using Decision Tree[max depth = 20] .ipynb: Decision Tree Model with max_depth = 20

9. Classification of Handwritten Digits using Decision Tree[max depth = 100] .ipynb: Decision Tree Model with max_depth = 100

10. Classification of Handwritten Digits using Decision Tree[train set = 30000].ipynb: Decision Tree Model with train_set = 30000

11. Classification of Handwritten Digits using Decision Tree[train set = 50000].ipynb: Decision Tree Model with train_set = 50000

12. Classification of Handwritten Digits using KNN Classifier [neighbors = 4 ].ipynb: KNN Model with 4 neighbors

13. Classification of Handwritten Digits using KNN Classifier [neighbors = 2 ].ipynb: KNN Model with 2 neighbors

14. Classification of Handwritten Digits using KNN Classifier [neighbors = 3 ].ipynb: KNN Model with 3 neighbors

15. Classification of Handwritten Digits using KNN Classifier [train data = 50000 ].ipynb: KNN Model with 4 neighbors and train_data is 50000

16. Classification of Handwritten Digits using KNN Classifier [train data = 30000 ].ipynb: KNN Model with 4 neighbors and train_data is 30000

17. Classification of Handwritten Digits using Random Forest Classifier [max_depth = 5].ipynb: Random Forest Model with 4 max_depth 5

18. Classification of Handwritten Digits using Random Forest Classifier [max_depth = 10].ipynb: Random Forest Model with 4 max_depth 10

19. Classification of Handwritten Digits using Random Forest Classifier[max_depth = 20].ipynb : Random Forest Model with 4 max_depth 20

20. Classification of Handwritten Digits using Random Forest Classifier[max_depth = 20][train = 30000].ipynb: Random Forest Model with 4 max_depth 20 and 30000 train data

21. Classification of Handwritten Digits using Random Forest Classifier[max_depth = 20][train = 50000].ipynb: Random Forest Model with 4 max_depth 20 and 50000 train data

22. Classification of Handwritten Digits using RBF Classifier{RBF}-[iter = 10].ipynb: RBF Model with epoch 10

23. Classification of Handwritten Digits using RBF Classifier{RBF}-[iter = 500].ipynb: RBF Model with epoch 500

24. Classification of Handwritten Digits using RBF Classifier{RBF}-[iter = 700].ipynb: RBF Model with epoch 700

25. Classification of Handwritten Digits using RBF Classifier{RBF}-[iter = 700][train = 30000]: RBF Model with epoch 10 and train data is 30000

26. Classification of Handwritten Digits using RBF Classifier{RBF}-[iter = 700][train = 50000]: RBF Model with epoch 10 and train data is 50000