In this project, WE're performing multi-class classification to detect the type of skin disorder
The dataset consists of information about 6 kinds of skin disorder.The details are as follows
the family history feature has the value 1 if any of these diseases has been observed in the family, and 0 otherwise.
The age feature simply represents the age of the patient.
Every other feature (clinical and histopathological) was given a degree in the range of 0 to 3.
Here, 0 indicates that the feature was not present, 3 indicates the largest amount possible, and 1, 2 indicate the relative intermediate values.
Number of Instances: 366
Number of Attributes: 34
Attribute Information: -- Complete attribute documentation: Clinical Attributes: (take values 0, 1, 2, 3, unless otherwise indicated) 1: erythema 2: scaling 3: definite borders 4: itching 5: koebner phenomenon 6: polygonal papules 7: follicular papules 8: oral mucosal involvement 9: knee and elbow involvement 10: scalp involvement 11: family history, (0 or 1) 34: Age (linear) Histopathological Attributes: (take values 0, 1, 2, 3) 12: melanin incontinence 13: eosinophils in the infiltrate 14: PNL infiltrate 15: fibrosis of the papillary dermis 16: exocytosis 17: acanthosis 18: hyperkeratosis 19: parakeratosis 20: clubbing of the rete ridges 21: elongation of the rete ridges 22: thinning of the suprapapillary epidermis 23: spongiform pustule 24: munro microabcess 25: focal hypergranulosis 26: disappearance of the granular layer 27: vacuolisation and damage of basal layer 28: spongiosis 29: saw-tooth appearance of retes 30: follicular horn plug 31: perifollicular parakeratosis 32: inflammatory monoluclear inflitrate 33: band-like infiltrate 8. Missing Attribute Values: 8 (in Age attribute). Distinguished with '?'.
Class Distribution: Database: Dermatology Class code: Class: Number of instances: 1 psoriasis 112 2 seboreic dermatitis 61 3 lichen planus 72 4 pityriasis rosea 49 5 cronic dermatitis 52 6 pityriasis rubra pilaris 20
Task:1 Determine which features is impacting for a particular skin disorder (for all classes)
Task2:-Create a machine learning model which will predict the disorder available.
Task:3:-Perform the EDA and show the trend of the disease.