• A Graphical user interface which takes Hepatitis Disease dataset as input.
• Splitting the data in multiple chunks, analyse and visualize each data chunk as a pattern.
• Identification of the changes in data patterns and invoke some pop-up msg at the time when there is a change.
• Building a Correlation matrix from the attributes of the given data.
• Computation of Metrics while processing each data attribute
• Prediction of Person surviving or dying acoording to the user input values of different attributes
• Visualisation of each attribute according to their density curve
• Exploratory DataAnalysis of each attribute on the Dataset

• Frontend Workflow
• Backend Workflow

The Dataset have the following attribute information

Attribute information:
1. Class: DIE, LIVE
2. AGE: 10, 20, 30, 40, 50, 60, 70, 80
3. SEX: male, female
4. STEROID: no, yes
5. ANTIVIRALS: no, yes
6. FATIGUE: no, yes
7. MALAISE: no, yes
8. ANOREXIA: no, yes
9. LIVER BIG: no, yes
10. LIVER FIRM: no, yes
11. SPLEEN PALPABLE: no, yes
12. SPIDERS: no, yes
13. ASCITES: no, yes
14. VARICES: no, yes
15. BILIRUBIN: 0.39, 0.80, 1.20, 2.00, 3.00, 4.00
16. ALK PHOSPHATE: 33, 80, 120, 160, 200, 250
17. SGOT: 13, 100, 200, 300, 400, 500,
18. ALBUMIN: 2.1, 3.0, 3.8, 4.5, 5.0, 6.0
19. PROTIME: 10, 20, 30, 40, 50, 60, 70, 80, 90
20. HISTOLOGY: no, yes

DFD DIAGRAM

ACTIVITY DIAGRAM

• Python 3.8 Version
• Embedded HTML
• Embedded CSS

Install the modules in Requirements.txt

streamlit==0.79.0
pandas==1.2.3
matplotlib==3.4.1
numpy==1.20.2
pandas_profiling==2.11.0
plotly==4.14.3
seaborn==0.11.1
streamlit-pandas-profiling==0.1.1
sweetviz==2.1.0
altair==4.1.0
joblib==1.0.1
lime==0.2.0.1

• fork the repo
• git clone [REPO-URL]
• Setup the project in IDE with installed requirements
• Run streamlit run app.py

Medha - IIT2019021

Vidushi - IIT2019027

Aarushi - IIT2019032

Jyotika - IIT2019036