Intern work

As part of your BTP/MTP you need to code the following ML/DL algo using Claude/Chaptgpt for multi-class classification.
Metrics to be computed
k fold = 2

deadline: Sunday 16 Feb
Some algos you may not get, keep a note of it, and code others.
Make colab.

Dataset: https://drive.google.com/file/d/1Hv1y0cSNWSJ80iCaWjl3TP7Vzvl0Rn2a/view?usp=sharing

Metrics (export to csv). Metrics will be cols. ML algos will be rows

Classification Metrics
1. Accuracy
2. Precision
3. Recall
4. F1 Score
5. True Positive Rate (TPR)
6. True Negative Rate (TNR)
7. False Positive Rate (FPR)
8. False Negative Rate (FNR)
9. Area Under the Curve (AUC)
10. Area Under the Precision-Recall Curve (AUC-PR)
11. Balanced Accuracy (BACC)
12. Matthews Correlation Coefficient (MCC)
13. Kappa Statistic
14. Cohen Kappa score
15. Positive Predictive Value (PPV)
16. Negative Predictive Value (NPV)

Error Metrics
1. Mean Squared Error (MSE)
2. Mean Absolute Error (MAE)
3. Root Mean Squared Error (RMSE)
4. Relative Absolute Error (RAE)
5. Log loss
6. Cross-Entropy Loss
7. Mean absolute error
8. Squared Prediction Error

Time and Efficiency Metrics
1. Training Time
2. Testing Time
3. Detection Time