Use Python (Machine Learning)

Consider the attached Glass dataset from the UCI machine learning repository: (Below)

The study of the classification of types of glass was motivated by criminological investigation. At the scene of the crime, the glass left can be used as evidence if it is correctly identified!

All attributes are continuous.

NOTE: the last column is class identifier (1-7).

Attribute Information:

1. Id number: 1 to 214 (do not use for classification)

2. RI: refractive index

3. Na: Sodium (unit measurement: weight percent in corresponding oxide, as are attributes 4-10)

4. Mg: Magnesium

5. Al: Aluminum

6. Si: Silicon

7. K: Potassium

8. Ca: Calcium

9. Ba: Barium

10. Fe: Iron

Class descriptions:

1 building_windows_float_processed

2 building_windows_non_float_processed

3 vehicle_windows_float_processed

4 vehicle_windows_non_float_processed (none in this database)

5 containers

6 tableware

7 headlamps

Use 5-fold cross-validation to evaluate the classification performance of a decision tree and a random forest classifier.

Describe which classifier gives you the best performance.

Provide:

1. Confusion matrix

2, Sensitivity

3. Specificity

4. Total accuracy

5. F1-score

6. Roc curve

7. Area under the curve in python

1	1.52101	13.64	4.49	1.1	71.78	0.06	8.75	0	0	1
2	1.51761	13.89	3.6	1.36	72.73	0.48	7.83	0	0	1
3	1.51618	13.53	3.55	1.54	72.99	0.39	7.78	0	0	1
4	1.51766	13.21	3.69	1.29	72.61	0.57	8.22	0	0	1
5	1.51742	13.27	3.62	1.24	73.08	0.55	8.07	0	0	1
6	1.51596	12.79	3.61	1.62	72.97	0.64	8.07	0	0.26	1
7	1.51743	13.3	3.6	1.14	73.09	0.58	8.17	0	0	1
8	1.51756	13.15	3.61	1.05	73.24	0.57	8.24	0	0	1
9	1.51918	14.04	3.58	1.37	72.08	0.56	8.3	0	0	1
10	1.51755	13	3.6	1.36	72.99	0.57	8.4	0	0.11	1