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README.md
hepatitis.data

Data Science Dojo
Copyright (c) 2016 - 2019


Level: Intermediate
Recommended Use: Classification Models
Domain: Healthcare

Hepatitis Data Set

Predict chance of survival


This intermediate level data set has 155 rows and 20 columns and provides various attributes of a patient. This data set is recommended for learning and practicing your skills in exploratory data analysis, data visualization, and classification modelling techniques. Feel free to explore the data set with multiple supervised and unsupervised learning techniques. The Following data dictionary gives more details on this data set:


Data Dictionary

Column Position Atrribute Name Definition Data Type Example % Null Ratios
1 Class Class (1: DIE, 2: LIVE) Quantitative 1, 2 0
2 Age Age (In Years) Quantitative 34, 20, 55 0
3 Sex Sex (1: Male, 2: Female) Quantitative 1, 2 0
4 Steroid Steroid (No: 1, Yes: 2) Quantitative 1, 2 1
5 Antivirals Antivirals (No: 1, Yes: 2) Quantitative 1, 2 0
6 Fatigue Fatigue (No: 1, Yes: 2) Quantitative 1, 2 1
7 Malaise Malaise (No: 1, Yes: 2) Quantitative 1, 2 1
8 Anorexia Anorexia (No: 1, Yes: 2) Quantitative 1, 2 1
9 Liver Big Liver Big (No: 1, Yes: 2) Quantitative 1, 2 10
10 Liver Firm Liver Firm (No: 1, Yes: 2) Quantitative 1, 2 11
11 Spleen Palpable Spleen Palpable (No: 1, Yes: 2) Quantitative 1, 2 5
12 Spiders Spiders (No: 1, Yes: 2) Quantitative 1, 2 5
13 Ascites Ascites (No: 1, Yes: 2) Quantitative 1, 2 5
14 Varices Varices (No: 1, Yes: 2) Quantitative 1, 2 5
15 Bilirubin Bilirubin Quantitative 0.39, 0.80, 1.20 6
16 Alk Phosphate Alk Phosphate Quantitative 33, 80, 120 29
17 Sgot SGOT Quantitative 13, 100, 200 4
18 Albumin Albumin Quantitative 2.1, 3.0, 3.8 16
19 Protime Protime Quantitative 60, 70, 80 67
20 Histology Histology (No: 1, Yes: 2) Quantitative 1, 2 0

Acknowledgement

This data set has been sourced from the Machine Learning Repository of University of California, Irvine Hepatitis Data Set (UC Irvine). The UCI page mentions the following authors for providing the data set:

Dua, D. and Karra Taniskidou, E. (2017)