├── .gitignore
├── BreastCancerEDA.ipynb
├── Breast_Cancer API
├── app.py
├── cancer_model.pkl
├── requirements.txt
├── static
│ └── img
│ │ └── breast.jpg
└── templates
│ ├── breast.jpg
│ ├── cancer.html
│ └── result.html
├── Diabetes_API
├── app.py
├── diabetes_model.pkl
├── requirements.txt
├── static
│ └── img
│ │ └── diabetes.jpg
└── templates
│ ├── diabetes.html
│ └── result.html
├── Diabetes_EDA.ipynb
├── Heart_API
├── app.py
├── heart_model.pkl
├── requirements.txt
├── static
│ └── img
│ │ └── heart.jpg
└── templates
│ ├── heart.html
│ └── result.html
├── Heart_EDA.ipynb
├── Kidney_API
├── app.py
├── kidney_model.pkl
├── requirements.txt
├── static
│ └── img
│ │ └── kidney.png
└── templates
│ ├── kidney.html
│ └── result.html
├── Kidney_EDA.ipynb
├── LICENSE
├── Liver_API
├── app.py
├── liver_model.pkl
├── requirements.txt
├── static
│ └── img
│ │ └── liver.jpg
└── templates
│ ├── liver.html
│ └── result.html
├── Liver_EDA.ipynb
├── README.md
├── data
├── cancer.csv
├── diabetes.csv
├── heart.csv
├── indian_liver_patient.csv
└── kidney.csv
├── index.html
├── model_codes
├── cancer.py
├── diabetes.py
├── heart.py
├── kidney.py
└── liver.py
├── screenupdated.gif
└── static
└── css
├── bootstrap-grid.css
├── bootstrap-grid.css.map
├── bootstrap-grid.min.css
├── bootstrap-grid.min.css.map
├── bootstrap-reboot.css
├── bootstrap-reboot.css.map
├── bootstrap-reboot.min.css
├── bootstrap-reboot.min.css.map
├── bootstrap.css
├── bootstrap.css.map
├── bootstrap.min.css
├── bootstrap.min.css.map
└── carousel.css
/.gitignore:
--------------------------------------------------------------------------------
1 | # Byte-compiled / optimized / DLL files
2 | __pycache__/
3 | *.py[cod]
4 | *$py.class
5 |
6 | # C extensions
7 | *.so
8 |
9 | # Distribution / packaging
10 | .Python
11 | build/
12 | develop-eggs/
13 | dist/
14 | downloads/
15 | eggs/
16 | .eggs/
17 | lib/
18 | lib64/
19 | parts/
20 | sdist/
21 | var/
22 | wheels/
23 | pip-wheel-metadata/
24 | share/python-wheels/
25 | *.egg-info/
26 | .installed.cfg
27 | *.egg
28 | MANIFEST
29 |
30 | # PyInstaller
31 | # Usually these files are written by a python script from a template
32 | # before PyInstaller builds the exe, so as to inject date/other infos into it.
33 | *.manifest
34 | *.spec
35 |
36 | # Installer logs
37 | pip-log.txt
38 | pip-delete-this-directory.txt
39 |
40 | # Unit test / coverage reports
41 | htmlcov/
42 | .tox/
43 | .nox/
44 | .coverage
45 | .coverage.*
46 | .cache
47 | nosetests.xml
48 | coverage.xml
49 | *.cover
50 | *.py,cover
51 | .hypothesis/
52 | .pytest_cache/
53 |
54 | # Translations
55 | *.mo
56 | *.pot
57 |
58 | # Django stuff:
59 | *.log
60 | local_settings.py
61 | db.sqlite3
62 | db.sqlite3-journal
63 |
64 | # Flask stuff:
65 | instance/
66 | .webassets-cache
67 |
68 | # Scrapy stuff:
69 | .scrapy
70 |
71 | # Sphinx documentation
72 | docs/_build/
73 |
74 | # PyBuilder
75 | target/
76 |
77 | # Jupyter Notebook
78 | .ipynb_checkpoints
79 |
80 | # IPython
81 | profile_default/
82 | ipython_config.py
83 |
84 | # pyenv
85 | .python-version
86 |
87 | # pipenv
88 | # According to pypa/pipenv#598, it is recommended to include Pipfile.lock in version control.
89 | # However, in case of collaboration, if having platform-specific dependencies or dependencies
90 | # having no cross-platform support, pipenv may install dependencies that don't work, or not
91 | # install all needed dependencies.
92 | #Pipfile.lock
93 |
94 | # PEP 582; used by e.g. github.com/David-OConnor/pyflow
95 | __pypackages__/
96 |
97 | # Celery stuff
98 | celerybeat-schedule
99 | celerybeat.pid
100 |
101 | # SageMath parsed files
102 | *.sage.py
103 |
104 | # Environments
105 | .env
106 | .venv
107 | env/
108 | venv/
109 | ENV/
110 | env.bak/
111 | venv.bak/
112 |
113 | # Spyder project settings
114 | .spyderproject
115 | .spyproject
116 |
117 | # Rope project settings
118 | .ropeproject
119 |
120 | # mkdocs documentation
121 | /site
122 |
123 | # mypy
124 | .mypy_cache/
125 | .dmypy.json
126 | dmypy.json
127 |
128 | # Pyre type checker
129 | .pyre/
130 |
131 |
132 | # Compiled Object files, Static and Dynamic libs (Shared Objects)
133 | *.o
134 | *.a
135 | *.so
136 |
137 | # Folders
138 | _obj
139 | _test
140 |
141 | # Architecture specific extensions/prefixes
142 | *.[568vq]
143 | [568vq].out
144 |
145 | *.cgo1.go
146 | *.cgo2.c
147 | _cgo_defun.c
148 | _cgo_gotypes.go
149 | _cgo_export.*
150 |
151 | _testmain.go
152 |
153 | *.exe
154 | *.test
155 | *.prof
156 |
157 | # WebStorm
158 | *.iml
159 |
160 | # Directory-based project format:
161 | .idea/
162 | .idea/workspace.xml
163 | **/.idea/workspace.xml
164 |
165 | # mac hidden files
166 | .DS_Store
167 |
168 | ##other
169 | #node_modules/
170 | #bower_components/
171 | .tmp
172 | #.sass-cache
173 | #builds/**/images/*
174 | #*.ogg
175 | #*.mp3
176 | #*.mp4
177 | #*.png
178 | #*.jpeg
179 | *.psd
180 |
181 |
182 |
183 |
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/Breast_Cancer API/app.py:
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1 | from flask import Flask, render_template, url_for, flash, redirect
2 | import joblib
3 | from flask import request
4 | import numpy as np
5 |
6 | app = Flask(__name__, template_folder='templates')
7 |
8 | @app.route("/")
9 |
10 | @app.route("/cancer")
11 | def cancer():
12 | return render_template(r"C:\Users\Mahesh Sharma\Desktop\HealthApp\Indivisual_Deployment\Breast_Cancer API\cancer_model.pkl")
13 |
14 | def ValuePredictor(to_predict_list, size):
15 | to_predict = np.array(to_predict_list).reshape(1,size)
16 | if(size==5):
17 | loaded_model = joblib.load('cancer_model.pkl')
18 | result = loaded_model.predict(to_predict)
19 | return result[0]
20 |
21 | @app.route('/predict', methods = ["POST"])
22 | def predict():
23 | if request.method == "POST":
24 | to_predict_list = request.form.to_dict()
25 | to_predict_list = list(to_predict_list.values())
26 | to_predict_list = list(map(float, to_predict_list))
27 | #cancer
28 | if(len(to_predict_list)==5):
29 | result = ValuePredictor(to_predict_list,5)
30 |
31 | if(int(result)==1):
32 | prediction = "Sorry you chances of getting the disease. Please consult the doctor immediately"
33 | else:
34 | prediction = "No need to fear. You have no dangerous symptoms of the disease"
35 | return(render_template("result.html", prediction_text=prediction))
36 |
37 | if __name__ == "__main__":
38 | app.run(debug=True)
39 |
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/Breast_Cancer API/cancer_model.pkl:
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https://raw.githubusercontent.com/Devansharma/Health-App/dc077a22c11d907ec61c8ec76e9edac2efa4ab77/Breast_Cancer API/cancer_model.pkl
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/Breast_Cancer API/requirements.txt:
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1 | Flask==1.0.2
2 | gunicorn==19.9.0
3 | itsdangerous==1.1.0
4 | Jinja2==2.10.1
5 | MarkupSafe==1.1.1
6 | Werkzeug==0.15.5
7 | tensorflow==2.0.0
8 | Pillow>=5.4.0
9 | numpy==1.17.2
10 | scikit-learn==0.22.2
11 | pandas==0.25.1
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/Breast_Cancer API/static/img/breast.jpg:
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https://raw.githubusercontent.com/Devansharma/Health-App/dc077a22c11d907ec61c8ec76e9edac2efa4ab77/Breast_Cancer API/static/img/breast.jpg
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/Breast_Cancer API/templates/breast.jpg:
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https://raw.githubusercontent.com/Devansharma/Health-App/dc077a22c11d907ec61c8ec76e9edac2efa4ab77/Breast_Cancer API/templates/breast.jpg
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/Breast_Cancer API/templates/cancer.html:
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1 |
2 |
3 |
4 |
5 |
25 |
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/Liver_API/app.py:
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1 | from flask import Flask, render_template, url_for, flash, redirect
2 | import joblib
3 | from flask import request
4 | import numpy as np
5 |
6 | app = Flask(__name__, template_folder='templates')
7 |
8 | @app.route("/")
9 |
10 | @app.route("/liver")
11 | def cancer():
12 | return render_template("liver.html")
13 |
14 | def ValuePredictor(to_predict_list, size):
15 | to_predict = np.array(to_predict_list).reshape(1,size)
16 | if(size==7):
17 | loaded_model = joblib.load(r'C:\Users\Mahesh Sharma\Desktop\HealthApp\Indivisual_Deployment\Liver_API\liver_model.pkl')
18 | result = loaded_model.predict(to_predict)
19 | return result[0]
20 |
21 | @app.route('/predict', methods = ["POST"])
22 | def predict():
23 | if request.method == "POST":
24 | to_predict_list = request.form.to_dict()
25 | to_predict_list = list(to_predict_list.values())
26 | to_predict_list = list(map(float, to_predict_list))
27 | #liver
28 | if(len(to_predict_list)==7):
29 | result = ValuePredictor(to_predict_list,7)
30 |
31 | if(int(result)==1):
32 | prediction = "Sorry you chances of getting the disease. Please consult the doctor immediately"
33 | else:
34 | prediction = "No need to fear. You have no dangerous symptoms of the disease"
35 | return(render_template("result.html", prediction_text=prediction))
36 |
37 | if __name__ == "__main__":
38 | app.run(debug=True)
39 |
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/Liver_API/liver_model.pkl:
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https://raw.githubusercontent.com/Devansharma/Health-App/dc077a22c11d907ec61c8ec76e9edac2efa4ab77/Liver_API/liver_model.pkl
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/Liver_API/requirements.txt:
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1 | Flask==1.0.2
2 | gunicorn==19.9.0
3 | itsdangerous==1.1.0
4 | Jinja2==2.10.1
5 | MarkupSafe==1.1.1
6 | Werkzeug==0.15.5
7 | Pillow>=5.4.0
8 | numpy==1.17.2
9 | scikit-learn==0.22.2
10 | pandas==0.25.1
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/Liver_API/static/img/liver.jpg:
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https://raw.githubusercontent.com/Devansharma/Health-App/dc077a22c11d907ec61c8ec76e9edac2efa4ab77/Liver_API/static/img/liver.jpg
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/Liver_API/templates/liver.html:
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1 |
2 |
3 |
4 |
5 |
6 |
7 |
8 |
9 |
10 |
11 |
29 | Liver Disease Prediction
30 |
31 |
32 |
33 |
34 | Health App
35 |
36 |
37 |
38 |
57 |
58 |
59 |
60 |
Know your chances of getting a liver disease in one Click!
61 |
62 |
63 |
64 |
65 |
66 |
67 |
101 |
102 |
103 |
104 |
105 |
106 |
107 |
108 |
109 |
110 |
111 |
112 |
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/Liver_API/templates/result.html:
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1 |
2 |
3 |
4 | {{ prediction_text }}
5 |
6 |
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/README.md:
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1 | # [Health App](http://ec2-18-217-53-87.us-east-2.compute.amazonaws.com/)
2 | This project is an end to end data science project that is able to predict the chances of getting a disease in organs like liver, kidey, heart, etc.
3 | 
4 |
5 | # Dataset Used
6 | 1. [Breast Cancer](https://www.kaggle.com/uciml/breast-cancer-wisconsin-data): Winconsin Breast Cancer Dataset
7 | 2. [Diabetes](https://www.kaggle.com/uciml/pima-indians-diabetes-database): Pima Indian Diabetes Dataset
8 | 3. [Liver](https://www.kaggle.com/uciml/indian-liver-patient-records): Indian Patient Liver Records
9 | 4. [Heart](https://www.kaggle.com/ronitf/heart-disease-uci): Heart Disease Dataset
10 | 5. [Kidney](https://www.kaggle.com/mansoordaku/ckdisease): Chronic Kidney Disease Dataset
11 |
12 | # Libraries Used:
13 | 1. Sci-kit Learn
14 | 2. Flask
15 | 3. Joblib
16 | 4. Pandas
17 | 5. Numpy
18 | 6. Bootstrap
19 | 7. HTML & CSS
20 |
21 | This whole Project is Deployed over AWS instance over the Apache Web Server using Flask
22 |
23 | # Steps used for Deployment:
24 | a. Created the ML model and saved it using joblib.
25 | b. Created app.py and the webpage page it will render to.
26 | c. Created an EC2 instance over RHEL8.
27 | d. Similarly Deployed all models and connected them to the home page inside apache web server.
28 |
29 | # Project Created by:
30 | [Devansh Sharma](https://www.linkedin.com/in/aboutdevansh/)
31 | [Bedanta Bhandar Kayastha](https://www.linkedin.com/in/bedanta-bhandar-kayastha-ba39251b2/)
32 |
33 |
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/data/diabetes.csv:
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1 | Pregnancies,Glucose,BloodPressure,SkinThickness,Insulin,BMI,DiabetesPedigreeFunction,Age,Outcome
2 | 6,148,72,35,0,33.6,0.627,50,1
3 | 1,85,66,29,0,26.6,0.351,31,0
4 | 8,183,64,0,0,23.3,0.672,32,1
5 | 1,89,66,23,94,28.1,0.167,21,0
6 | 0,137,40,35,168,43.1,2.288,33,1
7 | 5,116,74,0,0,25.6,0.201,30,0
8 | 3,78,50,32,88,31,0.248,26,1
9 | 10,115,0,0,0,35.3,0.134,29,0
10 | 2,197,70,45,543,30.5,0.158,53,1
11 | 8,125,96,0,0,0,0.232,54,1
12 | 4,110,92,0,0,37.6,0.191,30,0
13 | 10,168,74,0,0,38,0.537,34,1
14 | 10,139,80,0,0,27.1,1.441,57,0
15 | 1,189,60,23,846,30.1,0.398,59,1
16 | 5,166,72,19,175,25.8,0.587,51,1
17 | 7,100,0,0,0,30,0.484,32,1
18 | 0,118,84,47,230,45.8,0.551,31,1
19 | 7,107,74,0,0,29.6,0.254,31,1
20 | 1,103,30,38,83,43.3,0.183,33,0
21 | 1,115,70,30,96,34.6,0.529,32,1
22 | 3,126,88,41,235,39.3,0.704,27,0
23 | 8,99,84,0,0,35.4,0.388,50,0
24 | 7,196,90,0,0,39.8,0.451,41,1
25 | 9,119,80,35,0,29,0.263,29,1
26 | 11,143,94,33,146,36.6,0.254,51,1
27 | 10,125,70,26,115,31.1,0.205,41,1
28 | 7,147,76,0,0,39.4,0.257,43,1
29 | 1,97,66,15,140,23.2,0.487,22,0
30 | 13,145,82,19,110,22.2,0.245,57,0
31 | 5,117,92,0,0,34.1,0.337,38,0
32 | 5,109,75,26,0,36,0.546,60,0
33 | 3,158,76,36,245,31.6,0.851,28,1
34 | 3,88,58,11,54,24.8,0.267,22,0
35 | 6,92,92,0,0,19.9,0.188,28,0
36 | 10,122,78,31,0,27.6,0.512,45,0
37 | 4,103,60,33,192,24,0.966,33,0
38 | 11,138,76,0,0,33.2,0.42,35,0
39 | 9,102,76,37,0,32.9,0.665,46,1
40 | 2,90,68,42,0,38.2,0.503,27,1
41 | 4,111,72,47,207,37.1,1.39,56,1
42 | 3,180,64,25,70,34,0.271,26,0
43 | 7,133,84,0,0,40.2,0.696,37,0
44 | 7,106,92,18,0,22.7,0.235,48,0
45 | 9,171,110,24,240,45.4,0.721,54,1
46 | 7,159,64,0,0,27.4,0.294,40,0
47 | 0,180,66,39,0,42,1.893,25,1
48 | 1,146,56,0,0,29.7,0.564,29,0
49 | 2,71,70,27,0,28,0.586,22,0
50 | 7,103,66,32,0,39.1,0.344,31,1
51 | 7,105,0,0,0,0,0.305,24,0
52 | 1,103,80,11,82,19.4,0.491,22,0
53 | 1,101,50,15,36,24.2,0.526,26,0
54 | 5,88,66,21,23,24.4,0.342,30,0
55 | 8,176,90,34,300,33.7,0.467,58,1
56 | 7,150,66,42,342,34.7,0.718,42,0
57 | 1,73,50,10,0,23,0.248,21,0
58 | 7,187,68,39,304,37.7,0.254,41,1
59 | 0,100,88,60,110,46.8,0.962,31,0
60 | 0,146,82,0,0,40.5,1.781,44,0
61 | 0,105,64,41,142,41.5,0.173,22,0
62 | 2,84,0,0,0,0,0.304,21,0
63 | 8,133,72,0,0,32.9,0.27,39,1
64 | 5,44,62,0,0,25,0.587,36,0
65 | 2,141,58,34,128,25.4,0.699,24,0
66 | 7,114,66,0,0,32.8,0.258,42,1
67 | 5,99,74,27,0,29,0.203,32,0
68 | 0,109,88,30,0,32.5,0.855,38,1
69 | 2,109,92,0,0,42.7,0.845,54,0
70 | 1,95,66,13,38,19.6,0.334,25,0
71 | 4,146,85,27,100,28.9,0.189,27,0
72 | 2,100,66,20,90,32.9,0.867,28,1
73 | 5,139,64,35,140,28.6,0.411,26,0
74 | 13,126,90,0,0,43.4,0.583,42,1
75 | 4,129,86,20,270,35.1,0.231,23,0
76 | 1,79,75,30,0,32,0.396,22,0
77 | 1,0,48,20,0,24.7,0.14,22,0
78 | 7,62,78,0,0,32.6,0.391,41,0
79 | 5,95,72,33,0,37.7,0.37,27,0
80 | 0,131,0,0,0,43.2,0.27,26,1
81 | 2,112,66,22,0,25,0.307,24,0
82 | 3,113,44,13,0,22.4,0.14,22,0
83 | 2,74,0,0,0,0,0.102,22,0
84 | 7,83,78,26,71,29.3,0.767,36,0
85 | 0,101,65,28,0,24.6,0.237,22,0
86 | 5,137,108,0,0,48.8,0.227,37,1
87 | 2,110,74,29,125,32.4,0.698,27,0
88 | 13,106,72,54,0,36.6,0.178,45,0
89 | 2,100,68,25,71,38.5,0.324,26,0
90 | 15,136,70,32,110,37.1,0.153,43,1
91 | 1,107,68,19,0,26.5,0.165,24,0
92 | 1,80,55,0,0,19.1,0.258,21,0
93 | 4,123,80,15,176,32,0.443,34,0
94 | 7,81,78,40,48,46.7,0.261,42,0
95 | 4,134,72,0,0,23.8,0.277,60,1
96 | 2,142,82,18,64,24.7,0.761,21,0
97 | 6,144,72,27,228,33.9,0.255,40,0
98 | 2,92,62,28,0,31.6,0.13,24,0
99 | 1,71,48,18,76,20.4,0.323,22,0
100 | 6,93,50,30,64,28.7,0.356,23,0
101 | 1,122,90,51,220,49.7,0.325,31,1
102 | 1,163,72,0,0,39,1.222,33,1
103 | 1,151,60,0,0,26.1,0.179,22,0
104 | 0,125,96,0,0,22.5,0.262,21,0
105 | 1,81,72,18,40,26.6,0.283,24,0
106 | 2,85,65,0,0,39.6,0.93,27,0
107 | 1,126,56,29,152,28.7,0.801,21,0
108 | 1,96,122,0,0,22.4,0.207,27,0
109 | 4,144,58,28,140,29.5,0.287,37,0
110 | 3,83,58,31,18,34.3,0.336,25,0
111 | 0,95,85,25,36,37.4,0.247,24,1
112 | 3,171,72,33,135,33.3,0.199,24,1
113 | 8,155,62,26,495,34,0.543,46,1
114 | 1,89,76,34,37,31.2,0.192,23,0
115 | 4,76,62,0,0,34,0.391,25,0
116 | 7,160,54,32,175,30.5,0.588,39,1
117 | 4,146,92,0,0,31.2,0.539,61,1
118 | 5,124,74,0,0,34,0.22,38,1
119 | 5,78,48,0,0,33.7,0.654,25,0
120 | 4,97,60,23,0,28.2,0.443,22,0
121 | 4,99,76,15,51,23.2,0.223,21,0
122 | 0,162,76,56,100,53.2,0.759,25,1
123 | 6,111,64,39,0,34.2,0.26,24,0
124 | 2,107,74,30,100,33.6,0.404,23,0
125 | 5,132,80,0,0,26.8,0.186,69,0
126 | 0,113,76,0,0,33.3,0.278,23,1
127 | 1,88,30,42,99,55,0.496,26,1
128 | 3,120,70,30,135,42.9,0.452,30,0
129 | 1,118,58,36,94,33.3,0.261,23,0
130 | 1,117,88,24,145,34.5,0.403,40,1
131 | 0,105,84,0,0,27.9,0.741,62,1
132 | 4,173,70,14,168,29.7,0.361,33,1
133 | 9,122,56,0,0,33.3,1.114,33,1
134 | 3,170,64,37,225,34.5,0.356,30,1
135 | 8,84,74,31,0,38.3,0.457,39,0
136 | 2,96,68,13,49,21.1,0.647,26,0
137 | 2,125,60,20,140,33.8,0.088,31,0
138 | 0,100,70,26,50,30.8,0.597,21,0
139 | 0,93,60,25,92,28.7,0.532,22,0
140 | 0,129,80,0,0,31.2,0.703,29,0
141 | 5,105,72,29,325,36.9,0.159,28,0
142 | 3,128,78,0,0,21.1,0.268,55,0
143 | 5,106,82,30,0,39.5,0.286,38,0
144 | 2,108,52,26,63,32.5,0.318,22,0
145 | 10,108,66,0,0,32.4,0.272,42,1
146 | 4,154,62,31,284,32.8,0.237,23,0
147 | 0,102,75,23,0,0,0.572,21,0
148 | 9,57,80,37,0,32.8,0.096,41,0
149 | 2,106,64,35,119,30.5,1.4,34,0
150 | 5,147,78,0,0,33.7,0.218,65,0
151 | 2,90,70,17,0,27.3,0.085,22,0
152 | 1,136,74,50,204,37.4,0.399,24,0
153 | 4,114,65,0,0,21.9,0.432,37,0
154 | 9,156,86,28,155,34.3,1.189,42,1
155 | 1,153,82,42,485,40.6,0.687,23,0
156 | 8,188,78,0,0,47.9,0.137,43,1
157 | 7,152,88,44,0,50,0.337,36,1
158 | 2,99,52,15,94,24.6,0.637,21,0
159 | 1,109,56,21,135,25.2,0.833,23,0
160 | 2,88,74,19,53,29,0.229,22,0
161 | 17,163,72,41,114,40.9,0.817,47,1
162 | 4,151,90,38,0,29.7,0.294,36,0
163 | 7,102,74,40,105,37.2,0.204,45,0
164 | 0,114,80,34,285,44.2,0.167,27,0
165 | 2,100,64,23,0,29.7,0.368,21,0
166 | 0,131,88,0,0,31.6,0.743,32,1
167 | 6,104,74,18,156,29.9,0.722,41,1
168 | 3,148,66,25,0,32.5,0.256,22,0
169 | 4,120,68,0,0,29.6,0.709,34,0
170 | 4,110,66,0,0,31.9,0.471,29,0
171 | 3,111,90,12,78,28.4,0.495,29,0
172 | 6,102,82,0,0,30.8,0.18,36,1
173 | 6,134,70,23,130,35.4,0.542,29,1
174 | 2,87,0,23,0,28.9,0.773,25,0
175 | 1,79,60,42,48,43.5,0.678,23,0
176 | 2,75,64,24,55,29.7,0.37,33,0
177 | 8,179,72,42,130,32.7,0.719,36,1
178 | 6,85,78,0,0,31.2,0.382,42,0
179 | 0,129,110,46,130,67.1,0.319,26,1
180 | 5,143,78,0,0,45,0.19,47,0
181 | 5,130,82,0,0,39.1,0.956,37,1
182 | 6,87,80,0,0,23.2,0.084,32,0
183 | 0,119,64,18,92,34.9,0.725,23,0
184 | 1,0,74,20,23,27.7,0.299,21,0
185 | 5,73,60,0,0,26.8,0.268,27,0
186 | 4,141,74,0,0,27.6,0.244,40,0
187 | 7,194,68,28,0,35.9,0.745,41,1
188 | 8,181,68,36,495,30.1,0.615,60,1
189 | 1,128,98,41,58,32,1.321,33,1
190 | 8,109,76,39,114,27.9,0.64,31,1
191 | 5,139,80,35,160,31.6,0.361,25,1
192 | 3,111,62,0,0,22.6,0.142,21,0
193 | 9,123,70,44,94,33.1,0.374,40,0
194 | 7,159,66,0,0,30.4,0.383,36,1
195 | 11,135,0,0,0,52.3,0.578,40,1
196 | 8,85,55,20,0,24.4,0.136,42,0
197 | 5,158,84,41,210,39.4,0.395,29,1
198 | 1,105,58,0,0,24.3,0.187,21,0
199 | 3,107,62,13,48,22.9,0.678,23,1
200 | 4,109,64,44,99,34.8,0.905,26,1
201 | 4,148,60,27,318,30.9,0.15,29,1
202 | 0,113,80,16,0,31,0.874,21,0
203 | 1,138,82,0,0,40.1,0.236,28,0
204 | 0,108,68,20,0,27.3,0.787,32,0
205 | 2,99,70,16,44,20.4,0.235,27,0
206 | 6,103,72,32,190,37.7,0.324,55,0
207 | 5,111,72,28,0,23.9,0.407,27,0
208 | 8,196,76,29,280,37.5,0.605,57,1
209 | 5,162,104,0,0,37.7,0.151,52,1
210 | 1,96,64,27,87,33.2,0.289,21,0
211 | 7,184,84,33,0,35.5,0.355,41,1
212 | 2,81,60,22,0,27.7,0.29,25,0
213 | 0,147,85,54,0,42.8,0.375,24,0
214 | 7,179,95,31,0,34.2,0.164,60,0
215 | 0,140,65,26,130,42.6,0.431,24,1
216 | 9,112,82,32,175,34.2,0.26,36,1
217 | 12,151,70,40,271,41.8,0.742,38,1
218 | 5,109,62,41,129,35.8,0.514,25,1
219 | 6,125,68,30,120,30,0.464,32,0
220 | 5,85,74,22,0,29,1.224,32,1
221 | 5,112,66,0,0,37.8,0.261,41,1
222 | 0,177,60,29,478,34.6,1.072,21,1
223 | 2,158,90,0,0,31.6,0.805,66,1
224 | 7,119,0,0,0,25.2,0.209,37,0
225 | 7,142,60,33,190,28.8,0.687,61,0
226 | 1,100,66,15,56,23.6,0.666,26,0
227 | 1,87,78,27,32,34.6,0.101,22,0
228 | 0,101,76,0,0,35.7,0.198,26,0
229 | 3,162,52,38,0,37.2,0.652,24,1
230 | 4,197,70,39,744,36.7,2.329,31,0
231 | 0,117,80,31,53,45.2,0.089,24,0
232 | 4,142,86,0,0,44,0.645,22,1
233 | 6,134,80,37,370,46.2,0.238,46,1
234 | 1,79,80,25,37,25.4,0.583,22,0
235 | 4,122,68,0,0,35,0.394,29,0
236 | 3,74,68,28,45,29.7,0.293,23,0
237 | 4,171,72,0,0,43.6,0.479,26,1
238 | 7,181,84,21,192,35.9,0.586,51,1
239 | 0,179,90,27,0,44.1,0.686,23,1
240 | 9,164,84,21,0,30.8,0.831,32,1
241 | 0,104,76,0,0,18.4,0.582,27,0
242 | 1,91,64,24,0,29.2,0.192,21,0
243 | 4,91,70,32,88,33.1,0.446,22,0
244 | 3,139,54,0,0,25.6,0.402,22,1
245 | 6,119,50,22,176,27.1,1.318,33,1
246 | 2,146,76,35,194,38.2,0.329,29,0
247 | 9,184,85,15,0,30,1.213,49,1
248 | 10,122,68,0,0,31.2,0.258,41,0
249 | 0,165,90,33,680,52.3,0.427,23,0
250 | 9,124,70,33,402,35.4,0.282,34,0
251 | 1,111,86,19,0,30.1,0.143,23,0
252 | 9,106,52,0,0,31.2,0.38,42,0
253 | 2,129,84,0,0,28,0.284,27,0
254 | 2,90,80,14,55,24.4,0.249,24,0
255 | 0,86,68,32,0,35.8,0.238,25,0
256 | 12,92,62,7,258,27.6,0.926,44,1
257 | 1,113,64,35,0,33.6,0.543,21,1
258 | 3,111,56,39,0,30.1,0.557,30,0
259 | 2,114,68,22,0,28.7,0.092,25,0
260 | 1,193,50,16,375,25.9,0.655,24,0
261 | 11,155,76,28,150,33.3,1.353,51,1
262 | 3,191,68,15,130,30.9,0.299,34,0
263 | 3,141,0,0,0,30,0.761,27,1
264 | 4,95,70,32,0,32.1,0.612,24,0
265 | 3,142,80,15,0,32.4,0.2,63,0
266 | 4,123,62,0,0,32,0.226,35,1
267 | 5,96,74,18,67,33.6,0.997,43,0
268 | 0,138,0,0,0,36.3,0.933,25,1
269 | 2,128,64,42,0,40,1.101,24,0
270 | 0,102,52,0,0,25.1,0.078,21,0
271 | 2,146,0,0,0,27.5,0.24,28,1
272 | 10,101,86,37,0,45.6,1.136,38,1
273 | 2,108,62,32,56,25.2,0.128,21,0
274 | 3,122,78,0,0,23,0.254,40,0
275 | 1,71,78,50,45,33.2,0.422,21,0
276 | 13,106,70,0,0,34.2,0.251,52,0
277 | 2,100,70,52,57,40.5,0.677,25,0
278 | 7,106,60,24,0,26.5,0.296,29,1
279 | 0,104,64,23,116,27.8,0.454,23,0
280 | 5,114,74,0,0,24.9,0.744,57,0
281 | 2,108,62,10,278,25.3,0.881,22,0
282 | 0,146,70,0,0,37.9,0.334,28,1
283 | 10,129,76,28,122,35.9,0.28,39,0
284 | 7,133,88,15,155,32.4,0.262,37,0
285 | 7,161,86,0,0,30.4,0.165,47,1
286 | 2,108,80,0,0,27,0.259,52,1
287 | 7,136,74,26,135,26,0.647,51,0
288 | 5,155,84,44,545,38.7,0.619,34,0
289 | 1,119,86,39,220,45.6,0.808,29,1
290 | 4,96,56,17,49,20.8,0.34,26,0
291 | 5,108,72,43,75,36.1,0.263,33,0
292 | 0,78,88,29,40,36.9,0.434,21,0
293 | 0,107,62,30,74,36.6,0.757,25,1
294 | 2,128,78,37,182,43.3,1.224,31,1
295 | 1,128,48,45,194,40.5,0.613,24,1
296 | 0,161,50,0,0,21.9,0.254,65,0
297 | 6,151,62,31,120,35.5,0.692,28,0
298 | 2,146,70,38,360,28,0.337,29,1
299 | 0,126,84,29,215,30.7,0.52,24,0
300 | 14,100,78,25,184,36.6,0.412,46,1
301 | 8,112,72,0,0,23.6,0.84,58,0
302 | 0,167,0,0,0,32.3,0.839,30,1
303 | 2,144,58,33,135,31.6,0.422,25,1
304 | 5,77,82,41,42,35.8,0.156,35,0
305 | 5,115,98,0,0,52.9,0.209,28,1
306 | 3,150,76,0,0,21,0.207,37,0
307 | 2,120,76,37,105,39.7,0.215,29,0
308 | 10,161,68,23,132,25.5,0.326,47,1
309 | 0,137,68,14,148,24.8,0.143,21,0
310 | 0,128,68,19,180,30.5,1.391,25,1
311 | 2,124,68,28,205,32.9,0.875,30,1
312 | 6,80,66,30,0,26.2,0.313,41,0
313 | 0,106,70,37,148,39.4,0.605,22,0
314 | 2,155,74,17,96,26.6,0.433,27,1
315 | 3,113,50,10,85,29.5,0.626,25,0
316 | 7,109,80,31,0,35.9,1.127,43,1
317 | 2,112,68,22,94,34.1,0.315,26,0
318 | 3,99,80,11,64,19.3,0.284,30,0
319 | 3,182,74,0,0,30.5,0.345,29,1
320 | 3,115,66,39,140,38.1,0.15,28,0
321 | 6,194,78,0,0,23.5,0.129,59,1
322 | 4,129,60,12,231,27.5,0.527,31,0
323 | 3,112,74,30,0,31.6,0.197,25,1
324 | 0,124,70,20,0,27.4,0.254,36,1
325 | 13,152,90,33,29,26.8,0.731,43,1
326 | 2,112,75,32,0,35.7,0.148,21,0
327 | 1,157,72,21,168,25.6,0.123,24,0
328 | 1,122,64,32,156,35.1,0.692,30,1
329 | 10,179,70,0,0,35.1,0.2,37,0
330 | 2,102,86,36,120,45.5,0.127,23,1
331 | 6,105,70,32,68,30.8,0.122,37,0
332 | 8,118,72,19,0,23.1,1.476,46,0
333 | 2,87,58,16,52,32.7,0.166,25,0
334 | 1,180,0,0,0,43.3,0.282,41,1
335 | 12,106,80,0,0,23.6,0.137,44,0
336 | 1,95,60,18,58,23.9,0.26,22,0
337 | 0,165,76,43,255,47.9,0.259,26,0
338 | 0,117,0,0,0,33.8,0.932,44,0
339 | 5,115,76,0,0,31.2,0.343,44,1
340 | 9,152,78,34,171,34.2,0.893,33,1
341 | 7,178,84,0,0,39.9,0.331,41,1
342 | 1,130,70,13,105,25.9,0.472,22,0
343 | 1,95,74,21,73,25.9,0.673,36,0
344 | 1,0,68,35,0,32,0.389,22,0
345 | 5,122,86,0,0,34.7,0.29,33,0
346 | 8,95,72,0,0,36.8,0.485,57,0
347 | 8,126,88,36,108,38.5,0.349,49,0
348 | 1,139,46,19,83,28.7,0.654,22,0
349 | 3,116,0,0,0,23.5,0.187,23,0
350 | 3,99,62,19,74,21.8,0.279,26,0
351 | 5,0,80,32,0,41,0.346,37,1
352 | 4,92,80,0,0,42.2,0.237,29,0
353 | 4,137,84,0,0,31.2,0.252,30,0
354 | 3,61,82,28,0,34.4,0.243,46,0
355 | 1,90,62,12,43,27.2,0.58,24,0
356 | 3,90,78,0,0,42.7,0.559,21,0
357 | 9,165,88,0,0,30.4,0.302,49,1
358 | 1,125,50,40,167,33.3,0.962,28,1
359 | 13,129,0,30,0,39.9,0.569,44,1
360 | 12,88,74,40,54,35.3,0.378,48,0
361 | 1,196,76,36,249,36.5,0.875,29,1
362 | 5,189,64,33,325,31.2,0.583,29,1
363 | 5,158,70,0,0,29.8,0.207,63,0
364 | 5,103,108,37,0,39.2,0.305,65,0
365 | 4,146,78,0,0,38.5,0.52,67,1
366 | 4,147,74,25,293,34.9,0.385,30,0
367 | 5,99,54,28,83,34,0.499,30,0
368 | 6,124,72,0,0,27.6,0.368,29,1
369 | 0,101,64,17,0,21,0.252,21,0
370 | 3,81,86,16,66,27.5,0.306,22,0
371 | 1,133,102,28,140,32.8,0.234,45,1
372 | 3,173,82,48,465,38.4,2.137,25,1
373 | 0,118,64,23,89,0,1.731,21,0
374 | 0,84,64,22,66,35.8,0.545,21,0
375 | 2,105,58,40,94,34.9,0.225,25,0
376 | 2,122,52,43,158,36.2,0.816,28,0
377 | 12,140,82,43,325,39.2,0.528,58,1
378 | 0,98,82,15,84,25.2,0.299,22,0
379 | 1,87,60,37,75,37.2,0.509,22,0
380 | 4,156,75,0,0,48.3,0.238,32,1
381 | 0,93,100,39,72,43.4,1.021,35,0
382 | 1,107,72,30,82,30.8,0.821,24,0
383 | 0,105,68,22,0,20,0.236,22,0
384 | 1,109,60,8,182,25.4,0.947,21,0
385 | 1,90,62,18,59,25.1,1.268,25,0
386 | 1,125,70,24,110,24.3,0.221,25,0
387 | 1,119,54,13,50,22.3,0.205,24,0
388 | 5,116,74,29,0,32.3,0.66,35,1
389 | 8,105,100,36,0,43.3,0.239,45,1
390 | 5,144,82,26,285,32,0.452,58,1
391 | 3,100,68,23,81,31.6,0.949,28,0
392 | 1,100,66,29,196,32,0.444,42,0
393 | 5,166,76,0,0,45.7,0.34,27,1
394 | 1,131,64,14,415,23.7,0.389,21,0
395 | 4,116,72,12,87,22.1,0.463,37,0
396 | 4,158,78,0,0,32.9,0.803,31,1
397 | 2,127,58,24,275,27.7,1.6,25,0
398 | 3,96,56,34,115,24.7,0.944,39,0
399 | 0,131,66,40,0,34.3,0.196,22,1
400 | 3,82,70,0,0,21.1,0.389,25,0
401 | 3,193,70,31,0,34.9,0.241,25,1
402 | 4,95,64,0,0,32,0.161,31,1
403 | 6,137,61,0,0,24.2,0.151,55,0
404 | 5,136,84,41,88,35,0.286,35,1
405 | 9,72,78,25,0,31.6,0.28,38,0
406 | 5,168,64,0,0,32.9,0.135,41,1
407 | 2,123,48,32,165,42.1,0.52,26,0
408 | 4,115,72,0,0,28.9,0.376,46,1
409 | 0,101,62,0,0,21.9,0.336,25,0
410 | 8,197,74,0,0,25.9,1.191,39,1
411 | 1,172,68,49,579,42.4,0.702,28,1
412 | 6,102,90,39,0,35.7,0.674,28,0
413 | 1,112,72,30,176,34.4,0.528,25,0
414 | 1,143,84,23,310,42.4,1.076,22,0
415 | 1,143,74,22,61,26.2,0.256,21,0
416 | 0,138,60,35,167,34.6,0.534,21,1
417 | 3,173,84,33,474,35.7,0.258,22,1
418 | 1,97,68,21,0,27.2,1.095,22,0
419 | 4,144,82,32,0,38.5,0.554,37,1
420 | 1,83,68,0,0,18.2,0.624,27,0
421 | 3,129,64,29,115,26.4,0.219,28,1
422 | 1,119,88,41,170,45.3,0.507,26,0
423 | 2,94,68,18,76,26,0.561,21,0
424 | 0,102,64,46,78,40.6,0.496,21,0
425 | 2,115,64,22,0,30.8,0.421,21,0
426 | 8,151,78,32,210,42.9,0.516,36,1
427 | 4,184,78,39,277,37,0.264,31,1
428 | 0,94,0,0,0,0,0.256,25,0
429 | 1,181,64,30,180,34.1,0.328,38,1
430 | 0,135,94,46,145,40.6,0.284,26,0
431 | 1,95,82,25,180,35,0.233,43,1
432 | 2,99,0,0,0,22.2,0.108,23,0
433 | 3,89,74,16,85,30.4,0.551,38,0
434 | 1,80,74,11,60,30,0.527,22,0
435 | 2,139,75,0,0,25.6,0.167,29,0
436 | 1,90,68,8,0,24.5,1.138,36,0
437 | 0,141,0,0,0,42.4,0.205,29,1
438 | 12,140,85,33,0,37.4,0.244,41,0
439 | 5,147,75,0,0,29.9,0.434,28,0
440 | 1,97,70,15,0,18.2,0.147,21,0
441 | 6,107,88,0,0,36.8,0.727,31,0
442 | 0,189,104,25,0,34.3,0.435,41,1
443 | 2,83,66,23,50,32.2,0.497,22,0
444 | 4,117,64,27,120,33.2,0.23,24,0
445 | 8,108,70,0,0,30.5,0.955,33,1
446 | 4,117,62,12,0,29.7,0.38,30,1
447 | 0,180,78,63,14,59.4,2.42,25,1
448 | 1,100,72,12,70,25.3,0.658,28,0
449 | 0,95,80,45,92,36.5,0.33,26,0
450 | 0,104,64,37,64,33.6,0.51,22,1
451 | 0,120,74,18,63,30.5,0.285,26,0
452 | 1,82,64,13,95,21.2,0.415,23,0
453 | 2,134,70,0,0,28.9,0.542,23,1
454 | 0,91,68,32,210,39.9,0.381,25,0
455 | 2,119,0,0,0,19.6,0.832,72,0
456 | 2,100,54,28,105,37.8,0.498,24,0
457 | 14,175,62,30,0,33.6,0.212,38,1
458 | 1,135,54,0,0,26.7,0.687,62,0
459 | 5,86,68,28,71,30.2,0.364,24,0
460 | 10,148,84,48,237,37.6,1.001,51,1
461 | 9,134,74,33,60,25.9,0.46,81,0
462 | 9,120,72,22,56,20.8,0.733,48,0
463 | 1,71,62,0,0,21.8,0.416,26,0
464 | 8,74,70,40,49,35.3,0.705,39,0
465 | 5,88,78,30,0,27.6,0.258,37,0
466 | 10,115,98,0,0,24,1.022,34,0
467 | 0,124,56,13,105,21.8,0.452,21,0
468 | 0,74,52,10,36,27.8,0.269,22,0
469 | 0,97,64,36,100,36.8,0.6,25,0
470 | 8,120,0,0,0,30,0.183,38,1
471 | 6,154,78,41,140,46.1,0.571,27,0
472 | 1,144,82,40,0,41.3,0.607,28,0
473 | 0,137,70,38,0,33.2,0.17,22,0
474 | 0,119,66,27,0,38.8,0.259,22,0
475 | 7,136,90,0,0,29.9,0.21,50,0
476 | 4,114,64,0,0,28.9,0.126,24,0
477 | 0,137,84,27,0,27.3,0.231,59,0
478 | 2,105,80,45,191,33.7,0.711,29,1
479 | 7,114,76,17,110,23.8,0.466,31,0
480 | 8,126,74,38,75,25.9,0.162,39,0
481 | 4,132,86,31,0,28,0.419,63,0
482 | 3,158,70,30,328,35.5,0.344,35,1
483 | 0,123,88,37,0,35.2,0.197,29,0
484 | 4,85,58,22,49,27.8,0.306,28,0
485 | 0,84,82,31,125,38.2,0.233,23,0
486 | 0,145,0,0,0,44.2,0.63,31,1
487 | 0,135,68,42,250,42.3,0.365,24,1
488 | 1,139,62,41,480,40.7,0.536,21,0
489 | 0,173,78,32,265,46.5,1.159,58,0
490 | 4,99,72,17,0,25.6,0.294,28,0
491 | 8,194,80,0,0,26.1,0.551,67,0
492 | 2,83,65,28,66,36.8,0.629,24,0
493 | 2,89,90,30,0,33.5,0.292,42,0
494 | 4,99,68,38,0,32.8,0.145,33,0
495 | 4,125,70,18,122,28.9,1.144,45,1
496 | 3,80,0,0,0,0,0.174,22,0
497 | 6,166,74,0,0,26.6,0.304,66,0
498 | 5,110,68,0,0,26,0.292,30,0
499 | 2,81,72,15,76,30.1,0.547,25,0
500 | 7,195,70,33,145,25.1,0.163,55,1
501 | 6,154,74,32,193,29.3,0.839,39,0
502 | 2,117,90,19,71,25.2,0.313,21,0
503 | 3,84,72,32,0,37.2,0.267,28,0
504 | 6,0,68,41,0,39,0.727,41,1
505 | 7,94,64,25,79,33.3,0.738,41,0
506 | 3,96,78,39,0,37.3,0.238,40,0
507 | 10,75,82,0,0,33.3,0.263,38,0
508 | 0,180,90,26,90,36.5,0.314,35,1
509 | 1,130,60,23,170,28.6,0.692,21,0
510 | 2,84,50,23,76,30.4,0.968,21,0
511 | 8,120,78,0,0,25,0.409,64,0
512 | 12,84,72,31,0,29.7,0.297,46,1
513 | 0,139,62,17,210,22.1,0.207,21,0
514 | 9,91,68,0,0,24.2,0.2,58,0
515 | 2,91,62,0,0,27.3,0.525,22,0
516 | 3,99,54,19,86,25.6,0.154,24,0
517 | 3,163,70,18,105,31.6,0.268,28,1
518 | 9,145,88,34,165,30.3,0.771,53,1
519 | 7,125,86,0,0,37.6,0.304,51,0
520 | 13,76,60,0,0,32.8,0.18,41,0
521 | 6,129,90,7,326,19.6,0.582,60,0
522 | 2,68,70,32,66,25,0.187,25,0
523 | 3,124,80,33,130,33.2,0.305,26,0
524 | 6,114,0,0,0,0,0.189,26,0
525 | 9,130,70,0,0,34.2,0.652,45,1
526 | 3,125,58,0,0,31.6,0.151,24,0
527 | 3,87,60,18,0,21.8,0.444,21,0
528 | 1,97,64,19,82,18.2,0.299,21,0
529 | 3,116,74,15,105,26.3,0.107,24,0
530 | 0,117,66,31,188,30.8,0.493,22,0
531 | 0,111,65,0,0,24.6,0.66,31,0
532 | 2,122,60,18,106,29.8,0.717,22,0
533 | 0,107,76,0,0,45.3,0.686,24,0
534 | 1,86,66,52,65,41.3,0.917,29,0
535 | 6,91,0,0,0,29.8,0.501,31,0
536 | 1,77,56,30,56,33.3,1.251,24,0
537 | 4,132,0,0,0,32.9,0.302,23,1
538 | 0,105,90,0,0,29.6,0.197,46,0
539 | 0,57,60,0,0,21.7,0.735,67,0
540 | 0,127,80,37,210,36.3,0.804,23,0
541 | 3,129,92,49,155,36.4,0.968,32,1
542 | 8,100,74,40,215,39.4,0.661,43,1
543 | 3,128,72,25,190,32.4,0.549,27,1
544 | 10,90,85,32,0,34.9,0.825,56,1
545 | 4,84,90,23,56,39.5,0.159,25,0
546 | 1,88,78,29,76,32,0.365,29,0
547 | 8,186,90,35,225,34.5,0.423,37,1
548 | 5,187,76,27,207,43.6,1.034,53,1
549 | 4,131,68,21,166,33.1,0.16,28,0
550 | 1,164,82,43,67,32.8,0.341,50,0
551 | 4,189,110,31,0,28.5,0.68,37,0
552 | 1,116,70,28,0,27.4,0.204,21,0
553 | 3,84,68,30,106,31.9,0.591,25,0
554 | 6,114,88,0,0,27.8,0.247,66,0
555 | 1,88,62,24,44,29.9,0.422,23,0
556 | 1,84,64,23,115,36.9,0.471,28,0
557 | 7,124,70,33,215,25.5,0.161,37,0
558 | 1,97,70,40,0,38.1,0.218,30,0
559 | 8,110,76,0,0,27.8,0.237,58,0
560 | 11,103,68,40,0,46.2,0.126,42,0
561 | 11,85,74,0,0,30.1,0.3,35,0
562 | 6,125,76,0,0,33.8,0.121,54,1
563 | 0,198,66,32,274,41.3,0.502,28,1
564 | 1,87,68,34,77,37.6,0.401,24,0
565 | 6,99,60,19,54,26.9,0.497,32,0
566 | 0,91,80,0,0,32.4,0.601,27,0
567 | 2,95,54,14,88,26.1,0.748,22,0
568 | 1,99,72,30,18,38.6,0.412,21,0
569 | 6,92,62,32,126,32,0.085,46,0
570 | 4,154,72,29,126,31.3,0.338,37,0
571 | 0,121,66,30,165,34.3,0.203,33,1
572 | 3,78,70,0,0,32.5,0.27,39,0
573 | 2,130,96,0,0,22.6,0.268,21,0
574 | 3,111,58,31,44,29.5,0.43,22,0
575 | 2,98,60,17,120,34.7,0.198,22,0
576 | 1,143,86,30,330,30.1,0.892,23,0
577 | 1,119,44,47,63,35.5,0.28,25,0
578 | 6,108,44,20,130,24,0.813,35,0
579 | 2,118,80,0,0,42.9,0.693,21,1
580 | 10,133,68,0,0,27,0.245,36,0
581 | 2,197,70,99,0,34.7,0.575,62,1
582 | 0,151,90,46,0,42.1,0.371,21,1
583 | 6,109,60,27,0,25,0.206,27,0
584 | 12,121,78,17,0,26.5,0.259,62,0
585 | 8,100,76,0,0,38.7,0.19,42,0
586 | 8,124,76,24,600,28.7,0.687,52,1
587 | 1,93,56,11,0,22.5,0.417,22,0
588 | 8,143,66,0,0,34.9,0.129,41,1
589 | 6,103,66,0,0,24.3,0.249,29,0
590 | 3,176,86,27,156,33.3,1.154,52,1
591 | 0,73,0,0,0,21.1,0.342,25,0
592 | 11,111,84,40,0,46.8,0.925,45,1
593 | 2,112,78,50,140,39.4,0.175,24,0
594 | 3,132,80,0,0,34.4,0.402,44,1
595 | 2,82,52,22,115,28.5,1.699,25,0
596 | 6,123,72,45,230,33.6,0.733,34,0
597 | 0,188,82,14,185,32,0.682,22,1
598 | 0,67,76,0,0,45.3,0.194,46,0
599 | 1,89,24,19,25,27.8,0.559,21,0
600 | 1,173,74,0,0,36.8,0.088,38,1
601 | 1,109,38,18,120,23.1,0.407,26,0
602 | 1,108,88,19,0,27.1,0.4,24,0
603 | 6,96,0,0,0,23.7,0.19,28,0
604 | 1,124,74,36,0,27.8,0.1,30,0
605 | 7,150,78,29,126,35.2,0.692,54,1
606 | 4,183,0,0,0,28.4,0.212,36,1
607 | 1,124,60,32,0,35.8,0.514,21,0
608 | 1,181,78,42,293,40,1.258,22,1
609 | 1,92,62,25,41,19.5,0.482,25,0
610 | 0,152,82,39,272,41.5,0.27,27,0
611 | 1,111,62,13,182,24,0.138,23,0
612 | 3,106,54,21,158,30.9,0.292,24,0
613 | 3,174,58,22,194,32.9,0.593,36,1
614 | 7,168,88,42,321,38.2,0.787,40,1
615 | 6,105,80,28,0,32.5,0.878,26,0
616 | 11,138,74,26,144,36.1,0.557,50,1
617 | 3,106,72,0,0,25.8,0.207,27,0
618 | 6,117,96,0,0,28.7,0.157,30,0
619 | 2,68,62,13,15,20.1,0.257,23,0
620 | 9,112,82,24,0,28.2,1.282,50,1
621 | 0,119,0,0,0,32.4,0.141,24,1
622 | 2,112,86,42,160,38.4,0.246,28,0
623 | 2,92,76,20,0,24.2,1.698,28,0
624 | 6,183,94,0,0,40.8,1.461,45,0
625 | 0,94,70,27,115,43.5,0.347,21,0
626 | 2,108,64,0,0,30.8,0.158,21,0
627 | 4,90,88,47,54,37.7,0.362,29,0
628 | 0,125,68,0,0,24.7,0.206,21,0
629 | 0,132,78,0,0,32.4,0.393,21,0
630 | 5,128,80,0,0,34.6,0.144,45,0
631 | 4,94,65,22,0,24.7,0.148,21,0
632 | 7,114,64,0,0,27.4,0.732,34,1
633 | 0,102,78,40,90,34.5,0.238,24,0
634 | 2,111,60,0,0,26.2,0.343,23,0
635 | 1,128,82,17,183,27.5,0.115,22,0
636 | 10,92,62,0,0,25.9,0.167,31,0
637 | 13,104,72,0,0,31.2,0.465,38,1
638 | 5,104,74,0,0,28.8,0.153,48,0
639 | 2,94,76,18,66,31.6,0.649,23,0
640 | 7,97,76,32,91,40.9,0.871,32,1
641 | 1,100,74,12,46,19.5,0.149,28,0
642 | 0,102,86,17,105,29.3,0.695,27,0
643 | 4,128,70,0,0,34.3,0.303,24,0
644 | 6,147,80,0,0,29.5,0.178,50,1
645 | 4,90,0,0,0,28,0.61,31,0
646 | 3,103,72,30,152,27.6,0.73,27,0
647 | 2,157,74,35,440,39.4,0.134,30,0
648 | 1,167,74,17,144,23.4,0.447,33,1
649 | 0,179,50,36,159,37.8,0.455,22,1
650 | 11,136,84,35,130,28.3,0.26,42,1
651 | 0,107,60,25,0,26.4,0.133,23,0
652 | 1,91,54,25,100,25.2,0.234,23,0
653 | 1,117,60,23,106,33.8,0.466,27,0
654 | 5,123,74,40,77,34.1,0.269,28,0
655 | 2,120,54,0,0,26.8,0.455,27,0
656 | 1,106,70,28,135,34.2,0.142,22,0
657 | 2,155,52,27,540,38.7,0.24,25,1
658 | 2,101,58,35,90,21.8,0.155,22,0
659 | 1,120,80,48,200,38.9,1.162,41,0
660 | 11,127,106,0,0,39,0.19,51,0
661 | 3,80,82,31,70,34.2,1.292,27,1
662 | 10,162,84,0,0,27.7,0.182,54,0
663 | 1,199,76,43,0,42.9,1.394,22,1
664 | 8,167,106,46,231,37.6,0.165,43,1
665 | 9,145,80,46,130,37.9,0.637,40,1
666 | 6,115,60,39,0,33.7,0.245,40,1
667 | 1,112,80,45,132,34.8,0.217,24,0
668 | 4,145,82,18,0,32.5,0.235,70,1
669 | 10,111,70,27,0,27.5,0.141,40,1
670 | 6,98,58,33,190,34,0.43,43,0
671 | 9,154,78,30,100,30.9,0.164,45,0
672 | 6,165,68,26,168,33.6,0.631,49,0
673 | 1,99,58,10,0,25.4,0.551,21,0
674 | 10,68,106,23,49,35.5,0.285,47,0
675 | 3,123,100,35,240,57.3,0.88,22,0
676 | 8,91,82,0,0,35.6,0.587,68,0
677 | 6,195,70,0,0,30.9,0.328,31,1
678 | 9,156,86,0,0,24.8,0.23,53,1
679 | 0,93,60,0,0,35.3,0.263,25,0
680 | 3,121,52,0,0,36,0.127,25,1
681 | 2,101,58,17,265,24.2,0.614,23,0
682 | 2,56,56,28,45,24.2,0.332,22,0
683 | 0,162,76,36,0,49.6,0.364,26,1
684 | 0,95,64,39,105,44.6,0.366,22,0
685 | 4,125,80,0,0,32.3,0.536,27,1
686 | 5,136,82,0,0,0,0.64,69,0
687 | 2,129,74,26,205,33.2,0.591,25,0
688 | 3,130,64,0,0,23.1,0.314,22,0
689 | 1,107,50,19,0,28.3,0.181,29,0
690 | 1,140,74,26,180,24.1,0.828,23,0
691 | 1,144,82,46,180,46.1,0.335,46,1
692 | 8,107,80,0,0,24.6,0.856,34,0
693 | 13,158,114,0,0,42.3,0.257,44,1
694 | 2,121,70,32,95,39.1,0.886,23,0
695 | 7,129,68,49,125,38.5,0.439,43,1
696 | 2,90,60,0,0,23.5,0.191,25,0
697 | 7,142,90,24,480,30.4,0.128,43,1
698 | 3,169,74,19,125,29.9,0.268,31,1
699 | 0,99,0,0,0,25,0.253,22,0
700 | 4,127,88,11,155,34.5,0.598,28,0
701 | 4,118,70,0,0,44.5,0.904,26,0
702 | 2,122,76,27,200,35.9,0.483,26,0
703 | 6,125,78,31,0,27.6,0.565,49,1
704 | 1,168,88,29,0,35,0.905,52,1
705 | 2,129,0,0,0,38.5,0.304,41,0
706 | 4,110,76,20,100,28.4,0.118,27,0
707 | 6,80,80,36,0,39.8,0.177,28,0
708 | 10,115,0,0,0,0,0.261,30,1
709 | 2,127,46,21,335,34.4,0.176,22,0
710 | 9,164,78,0,0,32.8,0.148,45,1
711 | 2,93,64,32,160,38,0.674,23,1
712 | 3,158,64,13,387,31.2,0.295,24,0
713 | 5,126,78,27,22,29.6,0.439,40,0
714 | 10,129,62,36,0,41.2,0.441,38,1
715 | 0,134,58,20,291,26.4,0.352,21,0
716 | 3,102,74,0,0,29.5,0.121,32,0
717 | 7,187,50,33,392,33.9,0.826,34,1
718 | 3,173,78,39,185,33.8,0.97,31,1
719 | 10,94,72,18,0,23.1,0.595,56,0
720 | 1,108,60,46,178,35.5,0.415,24,0
721 | 5,97,76,27,0,35.6,0.378,52,1
722 | 4,83,86,19,0,29.3,0.317,34,0
723 | 1,114,66,36,200,38.1,0.289,21,0
724 | 1,149,68,29,127,29.3,0.349,42,1
725 | 5,117,86,30,105,39.1,0.251,42,0
726 | 1,111,94,0,0,32.8,0.265,45,0
727 | 4,112,78,40,0,39.4,0.236,38,0
728 | 1,116,78,29,180,36.1,0.496,25,0
729 | 0,141,84,26,0,32.4,0.433,22,0
730 | 2,175,88,0,0,22.9,0.326,22,0
731 | 2,92,52,0,0,30.1,0.141,22,0
732 | 3,130,78,23,79,28.4,0.323,34,1
733 | 8,120,86,0,0,28.4,0.259,22,1
734 | 2,174,88,37,120,44.5,0.646,24,1
735 | 2,106,56,27,165,29,0.426,22,0
736 | 2,105,75,0,0,23.3,0.56,53,0
737 | 4,95,60,32,0,35.4,0.284,28,0
738 | 0,126,86,27,120,27.4,0.515,21,0
739 | 8,65,72,23,0,32,0.6,42,0
740 | 2,99,60,17,160,36.6,0.453,21,0
741 | 1,102,74,0,0,39.5,0.293,42,1
742 | 11,120,80,37,150,42.3,0.785,48,1
743 | 3,102,44,20,94,30.8,0.4,26,0
744 | 1,109,58,18,116,28.5,0.219,22,0
745 | 9,140,94,0,0,32.7,0.734,45,1
746 | 13,153,88,37,140,40.6,1.174,39,0
747 | 12,100,84,33,105,30,0.488,46,0
748 | 1,147,94,41,0,49.3,0.358,27,1
749 | 1,81,74,41,57,46.3,1.096,32,0
750 | 3,187,70,22,200,36.4,0.408,36,1
751 | 6,162,62,0,0,24.3,0.178,50,1
752 | 4,136,70,0,0,31.2,1.182,22,1
753 | 1,121,78,39,74,39,0.261,28,0
754 | 3,108,62,24,0,26,0.223,25,0
755 | 0,181,88,44,510,43.3,0.222,26,1
756 | 8,154,78,32,0,32.4,0.443,45,1
757 | 1,128,88,39,110,36.5,1.057,37,1
758 | 7,137,90,41,0,32,0.391,39,0
759 | 0,123,72,0,0,36.3,0.258,52,1
760 | 1,106,76,0,0,37.5,0.197,26,0
761 | 6,190,92,0,0,35.5,0.278,66,1
762 | 2,88,58,26,16,28.4,0.766,22,0
763 | 9,170,74,31,0,44,0.403,43,1
764 | 9,89,62,0,0,22.5,0.142,33,0
765 | 10,101,76,48,180,32.9,0.171,63,0
766 | 2,122,70,27,0,36.8,0.34,27,0
767 | 5,121,72,23,112,26.2,0.245,30,0
768 | 1,126,60,0,0,30.1,0.349,47,1
769 | 1,93,70,31,0,30.4,0.315,23,0
--------------------------------------------------------------------------------
/data/heart.csv:
--------------------------------------------------------------------------------
1 | age,sex,cp,trestbps,chol,fbs,restecg,thalach,exang,oldpeak,slope,ca,thal,target
2 | 63,1,3,145,233,1,0,150,0,2.3,0,0,1,1
3 | 37,1,2,130,250,0,1,187,0,3.5,0,0,2,1
4 | 41,0,1,130,204,0,0,172,0,1.4,2,0,2,1
5 | 56,1,1,120,236,0,1,178,0,0.8,2,0,2,1
6 | 57,0,0,120,354,0,1,163,1,0.6,2,0,2,1
7 | 57,1,0,140,192,0,1,148,0,0.4,1,0,1,1
8 | 56,0,1,140,294,0,0,153,0,1.3,1,0,2,1
9 | 44,1,1,120,263,0,1,173,0,0,2,0,3,1
10 | 52,1,2,172,199,1,1,162,0,0.5,2,0,3,1
11 | 57,1,2,150,168,0,1,174,0,1.6,2,0,2,1
12 | 54,1,0,140,239,0,1,160,0,1.2,2,0,2,1
13 | 48,0,2,130,275,0,1,139,0,0.2,2,0,2,1
14 | 49,1,1,130,266,0,1,171,0,0.6,2,0,2,1
15 | 64,1,3,110,211,0,0,144,1,1.8,1,0,2,1
16 | 58,0,3,150,283,1,0,162,0,1,2,0,2,1
17 | 50,0,2,120,219,0,1,158,0,1.6,1,0,2,1
18 | 58,0,2,120,340,0,1,172,0,0,2,0,2,1
19 | 66,0,3,150,226,0,1,114,0,2.6,0,0,2,1
20 | 43,1,0,150,247,0,1,171,0,1.5,2,0,2,1
21 | 69,0,3,140,239,0,1,151,0,1.8,2,2,2,1
22 | 59,1,0,135,234,0,1,161,0,0.5,1,0,3,1
23 | 44,1,2,130,233,0,1,179,1,0.4,2,0,2,1
24 | 42,1,0,140,226,0,1,178,0,0,2,0,2,1
25 | 61,1,2,150,243,1,1,137,1,1,1,0,2,1
26 | 40,1,3,140,199,0,1,178,1,1.4,2,0,3,1
27 | 71,0,1,160,302,0,1,162,0,0.4,2,2,2,1
28 | 59,1,2,150,212,1,1,157,0,1.6,2,0,2,1
29 | 51,1,2,110,175,0,1,123,0,0.6,2,0,2,1
30 | 65,0,2,140,417,1,0,157,0,0.8,2,1,2,1
31 | 53,1,2,130,197,1,0,152,0,1.2,0,0,2,1
32 | 41,0,1,105,198,0,1,168,0,0,2,1,2,1
33 | 65,1,0,120,177,0,1,140,0,0.4,2,0,3,1
34 | 44,1,1,130,219,0,0,188,0,0,2,0,2,1
35 | 54,1,2,125,273,0,0,152,0,0.5,0,1,2,1
36 | 51,1,3,125,213,0,0,125,1,1.4,2,1,2,1
37 | 46,0,2,142,177,0,0,160,1,1.4,0,0,2,1
38 | 54,0,2,135,304,1,1,170,0,0,2,0,2,1
39 | 54,1,2,150,232,0,0,165,0,1.6,2,0,3,1
40 | 65,0,2,155,269,0,1,148,0,0.8,2,0,2,1
41 | 65,0,2,160,360,0,0,151,0,0.8,2,0,2,1
42 | 51,0,2,140,308,0,0,142,0,1.5,2,1,2,1
43 | 48,1,1,130,245,0,0,180,0,0.2,1,0,2,1
44 | 45,1,0,104,208,0,0,148,1,3,1,0,2,1
45 | 53,0,0,130,264,0,0,143,0,0.4,1,0,2,1
46 | 39,1,2,140,321,0,0,182,0,0,2,0,2,1
47 | 52,1,1,120,325,0,1,172,0,0.2,2,0,2,1
48 | 44,1,2,140,235,0,0,180,0,0,2,0,2,1
49 | 47,1,2,138,257,0,0,156,0,0,2,0,2,1
50 | 53,0,2,128,216,0,0,115,0,0,2,0,0,1
51 | 53,0,0,138,234,0,0,160,0,0,2,0,2,1
52 | 51,0,2,130,256,0,0,149,0,0.5,2,0,2,1
53 | 66,1,0,120,302,0,0,151,0,0.4,1,0,2,1
54 | 62,1,2,130,231,0,1,146,0,1.8,1,3,3,1
55 | 44,0,2,108,141,0,1,175,0,0.6,1,0,2,1
56 | 63,0,2,135,252,0,0,172,0,0,2,0,2,1
57 | 52,1,1,134,201,0,1,158,0,0.8,2,1,2,1
58 | 48,1,0,122,222,0,0,186,0,0,2,0,2,1
59 | 45,1,0,115,260,0,0,185,0,0,2,0,2,1
60 | 34,1,3,118,182,0,0,174,0,0,2,0,2,1
61 | 57,0,0,128,303,0,0,159,0,0,2,1,2,1
62 | 71,0,2,110,265,1,0,130,0,0,2,1,2,1
63 | 54,1,1,108,309,0,1,156,0,0,2,0,3,1
64 | 52,1,3,118,186,0,0,190,0,0,1,0,1,1
65 | 41,1,1,135,203,0,1,132,0,0,1,0,1,1
66 | 58,1,2,140,211,1,0,165,0,0,2,0,2,1
67 | 35,0,0,138,183,0,1,182,0,1.4,2,0,2,1
68 | 51,1,2,100,222,0,1,143,1,1.2,1,0,2,1
69 | 45,0,1,130,234,0,0,175,0,0.6,1,0,2,1
70 | 44,1,1,120,220,0,1,170,0,0,2,0,2,1
71 | 62,0,0,124,209,0,1,163,0,0,2,0,2,1
72 | 54,1,2,120,258,0,0,147,0,0.4,1,0,3,1
73 | 51,1,2,94,227,0,1,154,1,0,2,1,3,1
74 | 29,1,1,130,204,0,0,202,0,0,2,0,2,1
75 | 51,1,0,140,261,0,0,186,1,0,2,0,2,1
76 | 43,0,2,122,213,0,1,165,0,0.2,1,0,2,1
77 | 55,0,1,135,250,0,0,161,0,1.4,1,0,2,1
78 | 51,1,2,125,245,1,0,166,0,2.4,1,0,2,1
79 | 59,1,1,140,221,0,1,164,1,0,2,0,2,1
80 | 52,1,1,128,205,1,1,184,0,0,2,0,2,1
81 | 58,1,2,105,240,0,0,154,1,0.6,1,0,3,1
82 | 41,1,2,112,250,0,1,179,0,0,2,0,2,1
83 | 45,1,1,128,308,0,0,170,0,0,2,0,2,1
84 | 60,0,2,102,318,0,1,160,0,0,2,1,2,1
85 | 52,1,3,152,298,1,1,178,0,1.2,1,0,3,1
86 | 42,0,0,102,265,0,0,122,0,0.6,1,0,2,1
87 | 67,0,2,115,564,0,0,160,0,1.6,1,0,3,1
88 | 68,1,2,118,277,0,1,151,0,1,2,1,3,1
89 | 46,1,1,101,197,1,1,156,0,0,2,0,3,1
90 | 54,0,2,110,214,0,1,158,0,1.6,1,0,2,1
91 | 58,0,0,100,248,0,0,122,0,1,1,0,2,1
92 | 48,1,2,124,255,1,1,175,0,0,2,2,2,1
93 | 57,1,0,132,207,0,1,168,1,0,2,0,3,1
94 | 52,1,2,138,223,0,1,169,0,0,2,4,2,1
95 | 54,0,1,132,288,1,0,159,1,0,2,1,2,1
96 | 45,0,1,112,160,0,1,138,0,0,1,0,2,1
97 | 53,1,0,142,226,0,0,111,1,0,2,0,3,1
98 | 62,0,0,140,394,0,0,157,0,1.2,1,0,2,1
99 | 52,1,0,108,233,1,1,147,0,0.1,2,3,3,1
100 | 43,1,2,130,315,0,1,162,0,1.9,2,1,2,1
101 | 53,1,2,130,246,1,0,173,0,0,2,3,2,1
102 | 42,1,3,148,244,0,0,178,0,0.8,2,2,2,1
103 | 59,1,3,178,270,0,0,145,0,4.2,0,0,3,1
104 | 63,0,1,140,195,0,1,179,0,0,2,2,2,1
105 | 42,1,2,120,240,1,1,194,0,0.8,0,0,3,1
106 | 50,1,2,129,196,0,1,163,0,0,2,0,2,1
107 | 68,0,2,120,211,0,0,115,0,1.5,1,0,2,1
108 | 69,1,3,160,234,1,0,131,0,0.1,1,1,2,1
109 | 45,0,0,138,236,0,0,152,1,0.2,1,0,2,1
110 | 50,0,1,120,244,0,1,162,0,1.1,2,0,2,1
111 | 50,0,0,110,254,0,0,159,0,0,2,0,2,1
112 | 64,0,0,180,325,0,1,154,1,0,2,0,2,1
113 | 57,1,2,150,126,1,1,173,0,0.2,2,1,3,1
114 | 64,0,2,140,313,0,1,133,0,0.2,2,0,3,1
115 | 43,1,0,110,211,0,1,161,0,0,2,0,3,1
116 | 55,1,1,130,262,0,1,155,0,0,2,0,2,1
117 | 37,0,2,120,215,0,1,170,0,0,2,0,2,1
118 | 41,1,2,130,214,0,0,168,0,2,1,0,2,1
119 | 56,1,3,120,193,0,0,162,0,1.9,1,0,3,1
120 | 46,0,1,105,204,0,1,172,0,0,2,0,2,1
121 | 46,0,0,138,243,0,0,152,1,0,1,0,2,1
122 | 64,0,0,130,303,0,1,122,0,2,1,2,2,1
123 | 59,1,0,138,271,0,0,182,0,0,2,0,2,1
124 | 41,0,2,112,268,0,0,172,1,0,2,0,2,1
125 | 54,0,2,108,267,0,0,167,0,0,2,0,2,1
126 | 39,0,2,94,199,0,1,179,0,0,2,0,2,1
127 | 34,0,1,118,210,0,1,192,0,0.7,2,0,2,1
128 | 47,1,0,112,204,0,1,143,0,0.1,2,0,2,1
129 | 67,0,2,152,277,0,1,172,0,0,2,1,2,1
130 | 52,0,2,136,196,0,0,169,0,0.1,1,0,2,1
131 | 74,0,1,120,269,0,0,121,1,0.2,2,1,2,1
132 | 54,0,2,160,201,0,1,163,0,0,2,1,2,1
133 | 49,0,1,134,271,0,1,162,0,0,1,0,2,1
134 | 42,1,1,120,295,0,1,162,0,0,2,0,2,1
135 | 41,1,1,110,235,0,1,153,0,0,2,0,2,1
136 | 41,0,1,126,306,0,1,163,0,0,2,0,2,1
137 | 49,0,0,130,269,0,1,163,0,0,2,0,2,1
138 | 60,0,2,120,178,1,1,96,0,0,2,0,2,1
139 | 62,1,1,128,208,1,0,140,0,0,2,0,2,1
140 | 57,1,0,110,201,0,1,126,1,1.5,1,0,1,1
141 | 64,1,0,128,263,0,1,105,1,0.2,1,1,3,1
142 | 51,0,2,120,295,0,0,157,0,0.6,2,0,2,1
143 | 43,1,0,115,303,0,1,181,0,1.2,1,0,2,1
144 | 42,0,2,120,209,0,1,173,0,0,1,0,2,1
145 | 67,0,0,106,223,0,1,142,0,0.3,2,2,2,1
146 | 76,0,2,140,197,0,2,116,0,1.1,1,0,2,1
147 | 70,1,1,156,245,0,0,143,0,0,2,0,2,1
148 | 44,0,2,118,242,0,1,149,0,0.3,1,1,2,1
149 | 60,0,3,150,240,0,1,171,0,0.9,2,0,2,1
150 | 44,1,2,120,226,0,1,169,0,0,2,0,2,1
151 | 42,1,2,130,180,0,1,150,0,0,2,0,2,1
152 | 66,1,0,160,228,0,0,138,0,2.3,2,0,1,1
153 | 71,0,0,112,149,0,1,125,0,1.6,1,0,2,1
154 | 64,1,3,170,227,0,0,155,0,0.6,1,0,3,1
155 | 66,0,2,146,278,0,0,152,0,0,1,1,2,1
156 | 39,0,2,138,220,0,1,152,0,0,1,0,2,1
157 | 58,0,0,130,197,0,1,131,0,0.6,1,0,2,1
158 | 47,1,2,130,253,0,1,179,0,0,2,0,2,1
159 | 35,1,1,122,192,0,1,174,0,0,2,0,2,1
160 | 58,1,1,125,220,0,1,144,0,0.4,1,4,3,1
161 | 56,1,1,130,221,0,0,163,0,0,2,0,3,1
162 | 56,1,1,120,240,0,1,169,0,0,0,0,2,1
163 | 55,0,1,132,342,0,1,166,0,1.2,2,0,2,1
164 | 41,1,1,120,157,0,1,182,0,0,2,0,2,1
165 | 38,1,2,138,175,0,1,173,0,0,2,4,2,1
166 | 38,1,2,138,175,0,1,173,0,0,2,4,2,1
167 | 67,1,0,160,286,0,0,108,1,1.5,1,3,2,0
168 | 67,1,0,120,229,0,0,129,1,2.6,1,2,3,0
169 | 62,0,0,140,268,0,0,160,0,3.6,0,2,2,0
170 | 63,1,0,130,254,0,0,147,0,1.4,1,1,3,0
171 | 53,1,0,140,203,1,0,155,1,3.1,0,0,3,0
172 | 56,1,2,130,256,1,0,142,1,0.6,1,1,1,0
173 | 48,1,1,110,229,0,1,168,0,1,0,0,3,0
174 | 58,1,1,120,284,0,0,160,0,1.8,1,0,2,0
175 | 58,1,2,132,224,0,0,173,0,3.2,2,2,3,0
176 | 60,1,0,130,206,0,0,132,1,2.4,1,2,3,0
177 | 40,1,0,110,167,0,0,114,1,2,1,0,3,0
178 | 60,1,0,117,230,1,1,160,1,1.4,2,2,3,0
179 | 64,1,2,140,335,0,1,158,0,0,2,0,2,0
180 | 43,1,0,120,177,0,0,120,1,2.5,1,0,3,0
181 | 57,1,0,150,276,0,0,112,1,0.6,1,1,1,0
182 | 55,1,0,132,353,0,1,132,1,1.2,1,1,3,0
183 | 65,0,0,150,225,0,0,114,0,1,1,3,3,0
184 | 61,0,0,130,330,0,0,169,0,0,2,0,2,0
185 | 58,1,2,112,230,0,0,165,0,2.5,1,1,3,0
186 | 50,1,0,150,243,0,0,128,0,2.6,1,0,3,0
187 | 44,1,0,112,290,0,0,153,0,0,2,1,2,0
188 | 60,1,0,130,253,0,1,144,1,1.4,2,1,3,0
189 | 54,1,0,124,266,0,0,109,1,2.2,1,1,3,0
190 | 50,1,2,140,233,0,1,163,0,0.6,1,1,3,0
191 | 41,1,0,110,172,0,0,158,0,0,2,0,3,0
192 | 51,0,0,130,305,0,1,142,1,1.2,1,0,3,0
193 | 58,1,0,128,216,0,0,131,1,2.2,1,3,3,0
194 | 54,1,0,120,188,0,1,113,0,1.4,1,1,3,0
195 | 60,1,0,145,282,0,0,142,1,2.8,1,2,3,0
196 | 60,1,2,140,185,0,0,155,0,3,1,0,2,0
197 | 59,1,0,170,326,0,0,140,1,3.4,0,0,3,0
198 | 46,1,2,150,231,0,1,147,0,3.6,1,0,2,0
199 | 67,1,0,125,254,1,1,163,0,0.2,1,2,3,0
200 | 62,1,0,120,267,0,1,99,1,1.8,1,2,3,0
201 | 65,1,0,110,248,0,0,158,0,0.6,2,2,1,0
202 | 44,1,0,110,197,0,0,177,0,0,2,1,2,0
203 | 60,1,0,125,258,0,0,141,1,2.8,1,1,3,0
204 | 58,1,0,150,270,0,0,111,1,0.8,2,0,3,0
205 | 68,1,2,180,274,1,0,150,1,1.6,1,0,3,0
206 | 62,0,0,160,164,0,0,145,0,6.2,0,3,3,0
207 | 52,1,0,128,255,0,1,161,1,0,2,1,3,0
208 | 59,1,0,110,239,0,0,142,1,1.2,1,1,3,0
209 | 60,0,0,150,258,0,0,157,0,2.6,1,2,3,0
210 | 49,1,2,120,188,0,1,139,0,2,1,3,3,0
211 | 59,1,0,140,177,0,1,162,1,0,2,1,3,0
212 | 57,1,2,128,229,0,0,150,0,0.4,1,1,3,0
213 | 61,1,0,120,260,0,1,140,1,3.6,1,1,3,0
214 | 39,1,0,118,219,0,1,140,0,1.2,1,0,3,0
215 | 61,0,0,145,307,0,0,146,1,1,1,0,3,0
216 | 56,1,0,125,249,1,0,144,1,1.2,1,1,2,0
217 | 43,0,0,132,341,1,0,136,1,3,1,0,3,0
218 | 62,0,2,130,263,0,1,97,0,1.2,1,1,3,0
219 | 63,1,0,130,330,1,0,132,1,1.8,2,3,3,0
220 | 65,1,0,135,254,0,0,127,0,2.8,1,1,3,0
221 | 48,1,0,130,256,1,0,150,1,0,2,2,3,0
222 | 63,0,0,150,407,0,0,154,0,4,1,3,3,0
223 | 55,1,0,140,217,0,1,111,1,5.6,0,0,3,0
224 | 65,1,3,138,282,1,0,174,0,1.4,1,1,2,0
225 | 56,0,0,200,288,1,0,133,1,4,0,2,3,0
226 | 54,1,0,110,239,0,1,126,1,2.8,1,1,3,0
227 | 70,1,0,145,174,0,1,125,1,2.6,0,0,3,0
228 | 62,1,1,120,281,0,0,103,0,1.4,1,1,3,0
229 | 35,1,0,120,198,0,1,130,1,1.6,1,0,3,0
230 | 59,1,3,170,288,0,0,159,0,0.2,1,0,3,0
231 | 64,1,2,125,309,0,1,131,1,1.8,1,0,3,0
232 | 47,1,2,108,243,0,1,152,0,0,2,0,2,0
233 | 57,1,0,165,289,1,0,124,0,1,1,3,3,0
234 | 55,1,0,160,289,0,0,145,1,0.8,1,1,3,0
235 | 64,1,0,120,246,0,0,96,1,2.2,0,1,2,0
236 | 70,1,0,130,322,0,0,109,0,2.4,1,3,2,0
237 | 51,1,0,140,299,0,1,173,1,1.6,2,0,3,0
238 | 58,1,0,125,300,0,0,171,0,0,2,2,3,0
239 | 60,1,0,140,293,0,0,170,0,1.2,1,2,3,0
240 | 77,1,0,125,304,0,0,162,1,0,2,3,2,0
241 | 35,1,0,126,282,0,0,156,1,0,2,0,3,0
242 | 70,1,2,160,269,0,1,112,1,2.9,1,1,3,0
243 | 59,0,0,174,249,0,1,143,1,0,1,0,2,0
244 | 64,1,0,145,212,0,0,132,0,2,1,2,1,0
245 | 57,1,0,152,274,0,1,88,1,1.2,1,1,3,0
246 | 56,1,0,132,184,0,0,105,1,2.1,1,1,1,0
247 | 48,1,0,124,274,0,0,166,0,0.5,1,0,3,0
248 | 56,0,0,134,409,0,0,150,1,1.9,1,2,3,0
249 | 66,1,1,160,246,0,1,120,1,0,1,3,1,0
250 | 54,1,1,192,283,0,0,195,0,0,2,1,3,0
251 | 69,1,2,140,254,0,0,146,0,2,1,3,3,0
252 | 51,1,0,140,298,0,1,122,1,4.2,1,3,3,0
253 | 43,1,0,132,247,1,0,143,1,0.1,1,4,3,0
254 | 62,0,0,138,294,1,1,106,0,1.9,1,3,2,0
255 | 67,1,0,100,299,0,0,125,1,0.9,1,2,2,0
256 | 59,1,3,160,273,0,0,125,0,0,2,0,2,0
257 | 45,1,0,142,309,0,0,147,1,0,1,3,3,0
258 | 58,1,0,128,259,0,0,130,1,3,1,2,3,0
259 | 50,1,0,144,200,0,0,126,1,0.9,1,0,3,0
260 | 62,0,0,150,244,0,1,154,1,1.4,1,0,2,0
261 | 38,1,3,120,231,0,1,182,1,3.8,1,0,3,0
262 | 66,0,0,178,228,1,1,165,1,1,1,2,3,0
263 | 52,1,0,112,230,0,1,160,0,0,2,1,2,0
264 | 53,1,0,123,282,0,1,95,1,2,1,2,3,0
265 | 63,0,0,108,269,0,1,169,1,1.8,1,2,2,0
266 | 54,1,0,110,206,0,0,108,1,0,1,1,2,0
267 | 66,1,0,112,212,0,0,132,1,0.1,2,1,2,0
268 | 55,0,0,180,327,0,2,117,1,3.4,1,0,2,0
269 | 49,1,2,118,149,0,0,126,0,0.8,2,3,2,0
270 | 54,1,0,122,286,0,0,116,1,3.2,1,2,2,0
271 | 56,1,0,130,283,1,0,103,1,1.6,0,0,3,0
272 | 46,1,0,120,249,0,0,144,0,0.8,2,0,3,0
273 | 61,1,3,134,234,0,1,145,0,2.6,1,2,2,0
274 | 67,1,0,120,237,0,1,71,0,1,1,0,2,0
275 | 58,1,0,100,234,0,1,156,0,0.1,2,1,3,0
276 | 47,1,0,110,275,0,0,118,1,1,1,1,2,0
277 | 52,1,0,125,212,0,1,168,0,1,2,2,3,0
278 | 58,1,0,146,218,0,1,105,0,2,1,1,3,0
279 | 57,1,1,124,261,0,1,141,0,0.3,2,0,3,0
280 | 58,0,1,136,319,1,0,152,0,0,2,2,2,0
281 | 61,1,0,138,166,0,0,125,1,3.6,1,1,2,0
282 | 42,1,0,136,315,0,1,125,1,1.8,1,0,1,0
283 | 52,1,0,128,204,1,1,156,1,1,1,0,0,0
284 | 59,1,2,126,218,1,1,134,0,2.2,1,1,1,0
285 | 40,1,0,152,223,0,1,181,0,0,2,0,3,0
286 | 61,1,0,140,207,0,0,138,1,1.9,2,1,3,0
287 | 46,1,0,140,311,0,1,120,1,1.8,1,2,3,0
288 | 59,1,3,134,204,0,1,162,0,0.8,2,2,2,0
289 | 57,1,1,154,232,0,0,164,0,0,2,1,2,0
290 | 57,1,0,110,335,0,1,143,1,3,1,1,3,0
291 | 55,0,0,128,205,0,2,130,1,2,1,1,3,0
292 | 61,1,0,148,203,0,1,161,0,0,2,1,3,0
293 | 58,1,0,114,318,0,2,140,0,4.4,0,3,1,0
294 | 58,0,0,170,225,1,0,146,1,2.8,1,2,1,0
295 | 67,1,2,152,212,0,0,150,0,0.8,1,0,3,0
296 | 44,1,0,120,169,0,1,144,1,2.8,0,0,1,0
297 | 63,1,0,140,187,0,0,144,1,4,2,2,3,0
298 | 63,0,0,124,197,0,1,136,1,0,1,0,2,0
299 | 59,1,0,164,176,1,0,90,0,1,1,2,1,0
300 | 57,0,0,140,241,0,1,123,1,0.2,1,0,3,0
301 | 45,1,3,110,264,0,1,132,0,1.2,1,0,3,0
302 | 68,1,0,144,193,1,1,141,0,3.4,1,2,3,0
303 | 57,1,0,130,131,0,1,115,1,1.2,1,1,3,0
304 | 57,0,1,130,236,0,0,174,0,0,1,1,2,0
305 |
--------------------------------------------------------------------------------
/data/indian_liver_patient.csv:
--------------------------------------------------------------------------------
1 | Age,Gender,Total_Bilirubin,Direct_Bilirubin,Alkaline_Phosphotase,Alamine_Aminotransferase,Aspartate_Aminotransferase,Total_Protiens,Albumin,Albumin_and_Globulin_Ratio,Dataset
2 | 65,Female,0.7,0.1,187,16,18,6.8,3.3,0.9,1
3 | 62,Male,10.9,5.5,699,64,100,7.5,3.2,0.74,1
4 | 62,Male,7.3,4.1,490,60,68,7,3.3,0.89,1
5 | 58,Male,1,0.4,182,14,20,6.8,3.4,1,1
6 | 72,Male,3.9,2,195,27,59,7.3,2.4,0.4,1
7 | 46,Male,1.8,0.7,208,19,14,7.6,4.4,1.3,1
8 | 26,Female,0.9,0.2,154,16,12,7,3.5,1,1
9 | 29,Female,0.9,0.3,202,14,11,6.7,3.6,1.1,1
10 | 17,Male,0.9,0.3,202,22,19,7.4,4.1,1.2,2
11 | 55,Male,0.7,0.2,290,53,58,6.8,3.4,1,1
12 | 57,Male,0.6,0.1,210,51,59,5.9,2.7,0.8,1
13 | 72,Male,2.7,1.3,260,31,56,7.4,3,0.6,1
14 | 64,Male,0.9,0.3,310,61,58,7,3.4,0.9,2
15 | 74,Female,1.1,0.4,214,22,30,8.1,4.1,1,1
16 | 61,Male,0.7,0.2,145,53,41,5.8,2.7,0.87,1
17 | 25,Male,0.6,0.1,183,91,53,5.5,2.3,0.7,2
18 | 38,Male,1.8,0.8,342,168,441,7.6,4.4,1.3,1
19 | 33,Male,1.6,0.5,165,15,23,7.3,3.5,0.92,2
20 | 40,Female,0.9,0.3,293,232,245,6.8,3.1,0.8,1
21 | 40,Female,0.9,0.3,293,232,245,6.8,3.1,0.8,1
22 | 51,Male,2.2,1,610,17,28,7.3,2.6,0.55,1
23 | 51,Male,2.9,1.3,482,22,34,7,2.4,0.5,1
24 | 62,Male,6.8,3,542,116,66,6.4,3.1,0.9,1
25 | 40,Male,1.9,1,231,16,55,4.3,1.6,0.6,1
26 | 63,Male,0.9,0.2,194,52,45,6,3.9,1.85,2
27 | 34,Male,4.1,2,289,875,731,5,2.7,1.1,1
28 | 34,Male,4.1,2,289,875,731,5,2.7,1.1,1
29 | 34,Male,6.2,3,240,1680,850,7.2,4,1.2,1
30 | 20,Male,1.1,0.5,128,20,30,3.9,1.9,0.95,2
31 | 84,Female,0.7,0.2,188,13,21,6,3.2,1.1,2
32 | 57,Male,4,1.9,190,45,111,5.2,1.5,0.4,1
33 | 52,Male,0.9,0.2,156,35,44,4.9,2.9,1.4,1
34 | 57,Male,1,0.3,187,19,23,5.2,2.9,1.2,2
35 | 38,Female,2.6,1.2,410,59,57,5.6,3,0.8,2
36 | 38,Female,2.6,1.2,410,59,57,5.6,3,0.8,2
37 | 30,Male,1.3,0.4,482,102,80,6.9,3.3,0.9,1
38 | 17,Female,0.7,0.2,145,18,36,7.2,3.9,1.18,2
39 | 46,Female,14.2,7.8,374,38,77,4.3,2,0.8,1
40 | 48,Male,1.4,0.6,263,38,66,5.8,2.2,0.61,1
41 | 47,Male,2.7,1.3,275,123,73,6.2,3.3,1.1,1
42 | 45,Male,2.4,1.1,168,33,50,5.1,2.6,1,1
43 | 62,Male,0.6,0.1,160,42,110,4.9,2.6,1.1,2
44 | 42,Male,6.8,3.2,630,25,47,6.1,2.3,0.6,2
45 | 50,Male,2.6,1.2,415,407,576,6.4,3.2,1,1
46 | 85,Female,1,0.3,208,17,15,7,3.6,1,2
47 | 35,Male,1.8,0.6,275,48,178,6.5,3.2,0.9,2
48 | 21,Male,3.9,1.8,150,36,27,6.8,3.9,1.34,1
49 | 40,Male,1.1,0.3,230,1630,960,4.9,2.8,1.3,1
50 | 32,Female,0.6,0.1,176,39,28,6,3,1,1
51 | 55,Male,18.4,8.8,206,64,178,6.2,1.8,0.4,1
52 | 45,Female,0.7,0.2,170,21,14,5.7,2.5,0.7,1
53 | 34,Female,0.6,0.1,161,15,19,6.6,3.4,1,1
54 | 38,Male,3.1,1.6,253,80,406,6.8,3.9,1.3,1
55 | 38,Male,1.1,0.3,198,86,150,6.3,3.5,1.2,1
56 | 42,Male,8.9,4.5,272,31,61,5.8,2,0.5,1
57 | 42,Male,8.9,4.5,272,31,61,5.8,2,0.5,1
58 | 33,Male,0.8,0.2,198,26,23,8,4,1,2
59 | 48,Female,0.9,0.2,175,24,54,5.5,2.7,0.9,2
60 | 51,Male,0.8,0.2,367,42,18,5.2,2,0.6,1
61 | 64,Male,1.1,0.5,145,20,24,5.5,3.2,1.39,2
62 | 31,Female,0.8,0.2,158,21,16,6,3,1,1
63 | 58,Male,1,0.5,158,37,43,7.2,3.6,1,1
64 | 58,Male,1,0.5,158,37,43,7.2,3.6,1,1
65 | 57,Male,0.7,0.2,208,35,97,5.1,2.1,0.7,1
66 | 57,Male,1.3,0.4,259,40,86,6.5,2.5,0.6,1
67 | 57,Male,1.4,0.7,470,62,88,5.6,2.5,0.8,1
68 | 54,Male,2.2,1.2,195,55,95,6,3.7,1.6,1
69 | 37,Male,1.8,0.8,215,53,58,6.4,3.8,1.4,1
70 | 66,Male,0.7,0.2,239,27,26,6.3,3.7,1.4,1
71 | 60,Male,0.8,0.2,215,24,17,6.3,3,0.9,2
72 | 19,Female,0.7,0.2,186,166,397,5.5,3,1.2,1
73 | 75,Female,0.8,0.2,188,20,29,4.4,1.8,0.6,1
74 | 75,Female,0.8,0.2,205,27,24,4.4,2,0.8,1
75 | 52,Male,0.6,0.1,171,22,16,6.6,3.6,1.2,1
76 | 68,Male,0.7,0.1,145,20,22,5.8,2.9,1,1
77 | 29,Female,0.7,0.1,162,52,41,5.2,2.5,0.9,2
78 | 31,Male,0.9,0.2,518,189,17,5.3,2.3,0.7,1
79 | 68,Female,0.6,0.1,1620,95,127,4.6,2.1,0.8,1
80 | 70,Male,1.4,0.6,146,12,24,6.2,3.8,1.58,2
81 | 58,Female,2.8,1.3,670,48,79,4.7,1.6,0.5,1
82 | 58,Female,2.4,1.1,915,60,142,4.7,1.8,0.6,1
83 | 29,Male,1,0.3,75,25,26,5.1,2.9,1.3,1
84 | 49,Male,0.7,0.1,148,14,12,5.4,2.8,1,2
85 | 33,Male,2,1,258,194,152,5.4,3,1.25,1
86 | 32,Male,0.6,0.1,237,45,31,7.5,4.3,1.34,1
87 | 14,Male,1.4,0.5,269,58,45,6.7,3.9,1.4,1
88 | 13,Male,0.6,0.1,320,28,56,7.2,3.6,1,2
89 | 58,Male,0.8,0.2,298,33,59,6.2,3.1,1,1
90 | 18,Male,0.6,0.2,538,33,34,7.5,3.2,0.7,1
91 | 60,Male,4,1.9,238,119,350,7.1,3.3,0.8,1
92 | 60,Male,5.7,2.8,214,412,850,7.3,3.2,0.78,1
93 | 60,Male,6.8,3.2,308,404,794,6.8,3,0.7,1
94 | 60,Male,8.6,4,298,412,850,7.4,3,0.6,1
95 | 60,Male,5.8,2.7,204,220,400,7,3,0.7,1
96 | 60,Male,5.2,2.4,168,126,202,6.8,2.9,0.7,1
97 | 75,Male,0.9,0.2,282,25,23,4.4,2.2,1,1
98 | 39,Male,3.8,1.5,298,102,630,7.1,3.3,0.8,1
99 | 39,Male,6.6,3,215,190,950,4,1.7,0.7,1
100 | 18,Male,0.6,0.1,265,97,161,5.9,3.1,1.1,1
101 | 18,Male,0.7,0.1,312,308,405,6.9,3.7,1.1,1
102 | 27,Male,0.6,0.2,161,27,28,3.7,1.6,0.76,2
103 | 27,Male,0.7,0.2,243,21,23,5.3,2.3,0.7,2
104 | 17,Male,0.9,0.2,224,36,45,6.9,4.2,1.55,1
105 | 55,Female,0.8,0.2,225,14,23,6.1,3.3,1.2,2
106 | 63,Male,0.5,0.1,170,21,28,5.5,2.5,0.8,1
107 | 36,Male,5.3,2.3,145,32,92,5.1,2.6,1,2
108 | 36,Male,5.3,2.3,145,32,92,5.1,2.6,1,2
109 | 36,Male,0.8,0.2,158,29,39,6,2.2,0.5,2
110 | 36,Male,0.8,0.2,158,29,39,6,2.2,0.5,2
111 | 36,Male,0.9,0.1,486,25,34,5.9,2.8,0.9,2
112 | 24,Female,0.7,0.2,188,11,10,5.5,2.3,0.71,2
113 | 48,Male,3.2,1.6,257,33,116,5.7,2.2,0.62,1
114 | 27,Male,1.2,0.4,179,63,39,6.1,3.3,1.1,2
115 | 74,Male,0.6,0.1,272,24,98,5,2,0.6,1
116 | 50,Male,5.8,3,661,181,285,5.7,2.3,0.67,2
117 | 50,Male,7.3,3.6,1580,88,64,5.6,2.3,0.6,2
118 | 48,Male,0.7,0.1,1630,74,149,5.3,2,0.6,1
119 | 32,Male,12.7,6.2,194,2000,2946,5.7,3.3,1.3,1
120 | 32,Male,15.9,7,280,1350,1600,5.6,2.8,1,1
121 | 32,Male,18,8.2,298,1250,1050,5.4,2.6,0.9,1
122 | 32,Male,23,11.3,300,482,275,7.1,3.5,0.9,1
123 | 32,Male,22.7,10.2,290,322,113,6.6,2.8,0.7,1
124 | 58,Male,1.7,0.8,188,60,84,5.9,3.5,1.4,2
125 | 64,Female,0.8,0.2,178,17,18,6.3,3.1,0.9,1
126 | 28,Male,0.6,0.1,177,36,29,6.9,4.1,1.4,2
127 | 60,Male,1.8,0.5,201,45,25,3.9,1.7,0.7,2
128 | 48,Male,5.8,2.5,802,133,88,6,2.8,0.8,1
129 | 64,Male,3,1.4,248,46,40,6.5,3.2,0.9,1
130 | 58,Female,1.7,0.8,1896,61,83,8,3.9,0.95,1
131 | 45,Male,2.8,1.7,263,57,65,5.1,2.3,0.8,1
132 | 45,Male,3.2,1.4,512,50,58,6,2.7,0.8,1
133 | 70,Female,0.7,0.2,237,18,28,5.8,2.5,0.75,2
134 | 18,Female,0.8,0.2,199,34,31,6.5,3.5,1.16,2
135 | 53,Male,0.9,0.4,238,17,14,6.6,2.9,0.8,1
136 | 18,Male,1.8,0.7,178,35,36,6.8,3.6,1.1,1
137 | 66,Male,11.3,5.6,1110,1250,4929,7,2.4,0.5,1
138 | 46,Female,4.7,2.2,310,62,90,6.4,2.5,0.6,1
139 | 18,Male,0.8,0.2,282,72,140,5.5,2.5,0.8,1
140 | 18,Male,0.8,0.2,282,72,140,5.5,2.5,0.8,1
141 | 15,Male,0.8,0.2,380,25,66,6.1,3.7,1.5,1
142 | 60,Male,0.6,0.1,186,20,21,6.2,3.3,1.1,2
143 | 66,Female,4.2,2.1,159,15,30,7.1,2.2,0.4,1
144 | 30,Male,1.6,0.4,332,84,139,5.6,2.7,0.9,1
145 | 30,Male,1.6,0.4,332,84,139,5.6,2.7,0.9,1
146 | 45,Female,3.5,1.5,189,63,87,5.6,2.9,1,1
147 | 65,Male,0.8,0.2,201,18,22,5.4,2.9,1.1,2
148 | 66,Female,2.9,1.3,168,21,38,5.5,1.8,0.4,1
149 | 65,Male,0.7,0.1,392,20,30,5.3,2.8,1.1,1
150 | 50,Male,0.9,0.2,202,20,26,7.2,4.5,1.66,1
151 | 60,Male,0.8,0.2,286,21,27,7.1,4,1.2,1
152 | 56,Male,1.1,0.5,180,30,42,6.9,3.8,1.2,2
153 | 50,Male,1.6,0.8,218,18,20,5.9,2.9,0.96,1
154 | 46,Female,0.8,0.2,182,20,40,6,2.9,0.9,1
155 | 52,Male,0.6,0.1,178,26,27,6.5,3.6,1.2,2
156 | 34,Male,5.9,2.5,290,45,233,5.6,2.7,0.9,1
157 | 34,Male,8.7,4,298,58,138,5.8,2.4,0.7,1
158 | 32,Male,0.9,0.3,462,70,82,6.2,3.1,1,1
159 | 72,Male,0.7,0.1,196,20,35,5.8,2,0.5,1
160 | 72,Male,0.7,0.1,196,20,35,5.8,2,0.5,1
161 | 50,Male,1.2,0.4,282,36,32,7.2,3.9,1.1,1
162 | 60,Male,11,4.9,750,140,350,5.5,2.1,0.6,1
163 | 60,Male,11.5,5,1050,99,187,6.2,2.8,0.8,1
164 | 60,Male,5.8,2.7,599,43,66,5.4,1.8,0.5,1
165 | 39,Male,1.9,0.9,180,42,62,7.4,4.3,1.38,1
166 | 39,Male,1.9,0.9,180,42,62,7.4,4.3,1.38,1
167 | 48,Male,4.5,2.3,282,13,74,7,2.4,0.52,1
168 | 55,Male,75,3.6,332,40,66,6.2,2.5,0.6,1
169 | 47,Female,3,1.5,292,64,67,5.6,1.8,0.47,1
170 | 60,Male,22.8,12.6,962,53,41,6.9,3.3,0.9,1
171 | 60,Male,8.9,4,950,33,32,6.8,3.1,0.8,1
172 | 72,Male,1.7,0.8,200,28,37,6.2,3,0.93,1
173 | 44,Female,1.9,0.6,298,378,602,6.6,3.3,1,1
174 | 55,Male,14.1,7.6,750,35,63,5,1.6,0.47,1
175 | 31,Male,0.6,0.1,175,48,34,6,3.7,1.6,1
176 | 31,Male,0.6,0.1,175,48,34,6,3.7,1.6,1
177 | 31,Male,0.8,0.2,198,43,31,7.3,4,1.2,1
178 | 55,Male,0.8,0.2,482,112,99,5.7,2.6,0.8,1
179 | 75,Male,14.8,9,1020,71,42,5.3,2.2,0.7,1
180 | 75,Male,10.6,5,562,37,29,5.1,1.8,0.5,1
181 | 75,Male,8,4.6,386,30,25,5.5,1.8,0.48,1
182 | 75,Male,2.8,1.3,250,23,29,2.7,0.9,0.5,1
183 | 75,Male,2.9,1.3,218,33,37,3,1.5,1,1
184 | 65,Male,1.9,0.8,170,36,43,3.8,1.4,0.58,2
185 | 40,Male,0.6,0.1,171,20,17,5.4,2.5,0.8,1
186 | 64,Male,1.1,0.4,201,18,19,6.9,4.1,1.4,1
187 | 38,Male,1.5,0.4,298,60,103,6,3,1,2
188 | 60,Male,3.2,1.8,750,79,145,7.8,3.2,0.69,1
189 | 60,Male,2.1,1,191,114,247,4,1.6,0.6,1
190 | 60,Male,1.9,0.8,614,42,38,4.5,1.8,0.6,1
191 | 48,Female,0.8,0.2,218,32,28,5.2,2.5,0.9,2
192 | 60,Male,6.3,3.2,314,118,114,6.6,3.7,1.27,1
193 | 60,Male,5.8,3,257,107,104,6.6,3.5,1.12,1
194 | 60,Male,2.3,0.6,272,79,51,6.6,3.5,1.1,1
195 | 49,Male,1.3,0.4,206,30,25,6,3.1,1.06,2
196 | 49,Male,2,0.6,209,48,32,5.7,3,1.1,2
197 | 60,Male,2.4,1,1124,30,54,5.2,1.9,0.5,1
198 | 60,Male,2,1.1,664,52,104,6,2.1,0.53,1
199 | 26,Female,0.6,0.2,142,12,32,5.7,2.4,0.75,1
200 | 41,Male,0.9,0.2,169,22,18,6.1,3,0.9,2
201 | 7,Female,27.2,11.8,1420,790,1050,6.1,2,0.4,1
202 | 49,Male,0.6,0.1,218,50,53,5,2.4,0.9,1
203 | 49,Male,0.6,0.1,218,50,53,5,2.4,0.9,1
204 | 38,Female,0.8,0.2,145,19,23,6.1,3.1,1.03,2
205 | 21,Male,1,0.3,142,27,21,6.4,3.5,1.2,2
206 | 21,Male,0.7,0.2,135,27,26,6.4,3.3,1,2
207 | 45,Male,2.5,1.2,163,28,22,7.6,4,1.1,1
208 | 40,Male,3.6,1.8,285,50,60,7,2.9,0.7,1
209 | 40,Male,3.9,1.7,350,950,1500,6.7,3.8,1.3,1
210 | 70,Female,0.9,0.3,220,53,95,6.1,2.8,0.68,1
211 | 45,Female,0.9,0.3,189,23,33,6.6,3.9,,1
212 | 28,Male,0.8,0.3,190,20,14,4.1,2.4,1.4,1
213 | 42,Male,2.7,1.3,219,60,180,7,3.2,0.8,1
214 | 22,Male,2.7,1,160,82,127,5.5,3.1,1.2,2
215 | 8,Female,0.9,0.2,401,25,58,7.5,3.4,0.8,1
216 | 38,Male,1.7,1,180,18,34,7.2,3.6,1,1
217 | 66,Male,0.6,0.2,100,17,148,5,3.3,1.9,2
218 | 55,Male,0.9,0.2,116,36,16,6.2,3.2,1,2
219 | 49,Male,1.1,0.5,159,30,31,7,4.3,1.5,1
220 | 6,Male,0.6,0.1,289,38,30,4.8,2,0.7,2
221 | 37,Male,0.8,0.2,125,41,39,6.4,3.4,1.1,1
222 | 37,Male,0.8,0.2,147,27,46,5,2.5,1,1
223 | 47,Male,0.9,0.2,192,38,24,7.3,4.3,1.4,1
224 | 47,Male,0.9,0.2,265,40,28,8,4,1,1
225 | 50,Male,1.1,0.3,175,20,19,7.1,4.5,1.7,2
226 | 70,Male,1.7,0.5,400,56,44,5.7,3.1,1.1,1
227 | 26,Male,0.6,0.2,120,45,51,7.9,4,1,1
228 | 26,Male,1.3,0.4,173,38,62,8,4,1,1
229 | 68,Female,0.7,0.2,186,18,15,6.4,3.8,1.4,1
230 | 65,Female,1,0.3,202,26,13,5.3,2.6,0.9,2
231 | 46,Male,0.6,0.2,290,26,21,6,3,1,1
232 | 61,Male,1.5,0.6,196,61,85,6.7,3.8,1.3,2
233 | 61,Male,0.8,0.1,282,85,231,8.5,4.3,1,1
234 | 50,Male,2.7,1.6,157,149,156,7.9,3.1,0.6,1
235 | 33,Male,2,1.4,2110,48,89,6.2,3,0.9,1
236 | 40,Female,0.9,0.2,285,32,27,7.7,3.5,0.8,1
237 | 60,Male,1.5,0.6,360,230,298,4.5,2,0.8,1
238 | 22,Male,0.8,0.2,300,57,40,7.9,3.8,0.9,2
239 | 35,Female,0.9,0.3,158,20,16,8,4,1,1
240 | 35,Female,0.9,0.2,190,40,35,7.3,4.7,1.8,2
241 | 40,Male,0.9,0.3,196,69,48,6.8,3.1,0.8,1
242 | 48,Male,0.7,0.2,165,32,30,8,4,1,2
243 | 51,Male,0.8,0.2,230,24,46,6.5,3.1,,1
244 | 29,Female,0.8,0.2,205,30,23,8.2,4.1,1,1
245 | 28,Female,0.9,0.2,316,25,23,8.5,5.5,1.8,1
246 | 54,Male,0.8,0.2,218,20,19,6.3,2.5,0.6,1
247 | 54,Male,0.9,0.2,290,15,18,6.1,2.8,0.8,1
248 | 55,Male,1.8,9,272,22,79,6.1,2.7,0.7,1
249 | 55,Male,0.9,0.2,190,25,28,5.9,2.7,0.8,1
250 | 40,Male,0.7,0.1,202,37,29,5,2.6,1,1
251 | 33,Male,1.2,0.3,498,28,25,7,3,0.7,1
252 | 33,Male,2.1,1.3,480,38,22,6.5,3,0.8,1
253 | 33,Male,0.9,0.8,680,37,40,5.9,2.6,0.8,1
254 | 65,Male,1.1,0.3,258,48,40,7,3.9,1.2,2
255 | 35,Female,0.6,0.2,180,12,15,5.2,2.7,,2
256 | 38,Female,0.7,0.1,152,90,21,7.1,4.2,1.4,2
257 | 38,Male,1.7,0.7,859,89,48,6,3,1,1
258 | 50,Male,0.9,0.3,901,23,17,6.2,3.5,1.2,1
259 | 44,Male,0.8,0.2,335,148,86,5.6,3,1.1,1
260 | 36,Male,0.8,0.2,182,31,34,6.4,3.8,1.4,2
261 | 42,Male,30.5,14.2,285,65,130,5.2,2.1,0.6,1
262 | 42,Male,16.4,8.9,245,56,87,5.4,2,0.5,1
263 | 33,Male,1.5,7,505,205,140,7.5,3.9,1,1
264 | 18,Male,0.8,0.2,228,55,54,6.9,4,1.3,1
265 | 38,Female,0.8,0.2,185,25,21,7,3,0.7,1
266 | 38,Male,0.8,0.2,247,55,92,7.4,4.3,1.38,2
267 | 4,Male,0.9,0.2,348,30,34,8,4,1,2
268 | 62,Male,1.2,0.4,195,38,54,6.3,3.8,1.5,1
269 | 43,Female,0.9,0.3,140,12,29,7.4,3.5,1.8,1
270 | 40,Male,14.5,6.4,358,50,75,5.7,2.1,0.5,1
271 | 26,Male,0.6,0.1,110,15,20,2.8,1.6,1.3,1
272 | 37,Male,0.7,0.2,235,96,54,9.5,4.9,1,1
273 | 4,Male,0.8,0.2,460,152,231,6.5,3.2,0.9,2
274 | 21,Male,18.5,9.5,380,390,500,8.2,4.1,1,1
275 | 30,Male,0.7,0.2,262,15,18,9.6,4.7,1.2,1
276 | 33,Male,1.8,0.8,196,25,22,8,4,1,1
277 | 26,Male,1.9,0.8,180,22,19,8.2,4.1,1,2
278 | 35,Male,0.9,0.2,190,25,20,6.4,3.6,1.2,2
279 | 60,Male,2,0.8,190,45,40,6,2.8,0.8,1
280 | 45,Male,2.2,0.8,209,25,20,8,4,1,1
281 | 48,Female,1,1.4,144,18,14,8.3,4.2,1,1
282 | 58,Male,0.8,0.2,123,56,48,6,3,1,1
283 | 50,Male,0.7,0.2,192,18,15,7.4,4.2,1.3,2
284 | 50,Male,0.7,0.2,188,12,14,7,3.4,0.9,1
285 | 18,Male,1.3,0.7,316,10,21,6,2.1,0.5,2
286 | 18,Male,0.9,0.3,300,30,48,8,4,1,1
287 | 13,Male,1.5,0.5,575,29,24,7.9,3.9,0.9,1
288 | 34,Female,0.8,0.2,192,15,12,8.6,4.7,1.2,1
289 | 43,Male,1.3,0.6,155,15,20,8,4,1,2
290 | 50,Female,1,0.5,239,16,39,7.5,3.7,0.9,1
291 | 57,Male,4.5,2.3,315,120,105,7,4,1.3,1
292 | 45,Female,1,0.3,250,48,44,8.6,4.3,1,1
293 | 60,Male,0.7,0.2,174,32,14,7.8,4.2,1.1,2
294 | 45,Male,0.6,0.2,245,22,24,7.1,3.4,0.9,1
295 | 23,Male,1.1,0.5,191,37,41,7.7,4.3,1.2,2
296 | 22,Male,2.4,1,340,25,21,8.3,4.5,1.1,1
297 | 22,Male,0.6,0.2,202,78,41,8,3.9,0.9,1
298 | 74,Female,0.9,0.3,234,16,19,7.9,4,1,1
299 | 25,Female,0.9,0.3,159,24,25,6.9,4.4,1.7,2
300 | 31,Female,1.1,0.3,190,26,15,7.9,3.8,0.9,1
301 | 24,Female,0.9,0.2,195,40,35,7.4,4.1,1.2,2
302 | 58,Male,0.8,0.2,180,32,25,8.2,4.4,1.1,2
303 | 51,Female,0.9,0.2,280,21,30,6.7,3.2,0.8,1
304 | 50,Female,1.7,0.6,430,28,32,6.8,3.5,1,1
305 | 50,Male,0.7,0.2,206,18,17,8.4,4.2,1,2
306 | 55,Female,0.8,0.2,155,21,17,6.9,3.8,1.4,1
307 | 54,Female,1.4,0.7,195,36,16,7.9,3.7,0.9,2
308 | 48,Male,1.6,1,588,74,113,7.3,2.4,0.4,1
309 | 30,Male,0.8,0.2,174,21,47,4.6,2.3,1,1
310 | 45,Female,0.8,0.2,165,22,18,8.2,4.1,1,1
311 | 48,Female,1.1,0.7,527,178,250,8,4.2,1.1,1
312 | 51,Male,0.8,0.2,175,48,22,8.1,4.6,1.3,1
313 | 54,Female,23.2,12.6,574,43,47,7.2,3.5,0.9,1
314 | 27,Male,1.3,0.6,106,25,54,8.5,4.8,,2
315 | 30,Female,0.8,0.2,158,25,22,7.9,4.5,1.3,2
316 | 26,Male,2,0.9,195,24,65,7.8,4.3,1.2,1
317 | 22,Male,0.9,0.3,179,18,21,6.7,3.7,1.2,2
318 | 44,Male,0.9,0.2,182,29,82,7.1,3.7,1,2
319 | 35,Male,0.7,0.2,198,42,30,6.8,3.4,1,1
320 | 38,Male,3.7,2.2,216,179,232,7.8,4.5,1.3,1
321 | 14,Male,0.9,0.3,310,21,16,8.1,4.2,1,2
322 | 30,Female,0.7,0.2,63,31,27,5.8,3.4,1.4,1
323 | 30,Female,0.8,0.2,198,30,58,5.2,2.8,1.1,1
324 | 36,Male,1.7,0.5,205,36,34,7.1,3.9,1.2,1
325 | 12,Male,0.8,0.2,302,47,67,6.7,3.5,1.1,2
326 | 60,Male,2.6,1.2,171,42,37,5.4,2.7,1,1
327 | 42,Male,0.8,0.2,158,27,23,6.7,3.1,0.8,2
328 | 36,Female,1.2,0.4,358,160,90,8.3,4.4,1.1,2
329 | 24,Male,3.3,1.6,174,11,33,7.6,3.9,1,2
330 | 43,Male,0.8,0.2,192,29,20,6,2.9,0.9,2
331 | 21,Male,0.7,0.2,211,14,23,7.3,4.1,1.2,2
332 | 26,Male,2,0.9,157,54,68,6.1,2.7,0.8,1
333 | 26,Male,1.7,0.6,210,62,56,5.4,2.2,0.6,1
334 | 26,Male,7.1,3.3,258,80,113,6.2,2.9,0.8,1
335 | 36,Female,0.7,0.2,152,21,25,5.9,3.1,1.1,2
336 | 13,Female,0.7,0.2,350,17,24,7.4,4,1.1,1
337 | 13,Female,0.7,0.1,182,24,19,8.9,4.9,1.2,1
338 | 75,Male,6.7,3.6,458,198,143,6.2,3.2,1,1
339 | 75,Male,2.5,1.2,375,85,68,6.4,2.9,0.8,1
340 | 75,Male,1.8,0.8,405,79,50,6.1,2.9,0.9,1
341 | 75,Male,1.4,0.4,215,50,30,5.9,2.6,0.7,1
342 | 75,Male,0.9,0.2,206,44,33,6.2,2.9,0.8,1
343 | 36,Female,0.8,0.2,650,70,138,6.6,3.1,0.8,1
344 | 35,Male,0.8,0.2,198,36,32,7,4,1.3,2
345 | 70,Male,3.1,1.6,198,40,28,5.6,2,0.5,1
346 | 37,Male,0.8,0.2,195,60,40,8.2,5,1.5,2
347 | 60,Male,2.9,1.3,230,32,44,5.6,2,0.5,1
348 | 46,Male,0.6,0.2,115,14,11,6.9,3.4,0.9,1
349 | 38,Male,0.7,0.2,216,349,105,7,3.5,1,1
350 | 70,Male,1.3,0.4,358,19,14,6.1,2.8,0.8,1
351 | 49,Female,0.8,0.2,158,19,15,6.6,3.6,1.2,2
352 | 37,Male,1.8,0.8,145,62,58,5.7,2.9,1,1
353 | 37,Male,1.3,0.4,195,41,38,5.3,2.1,0.6,1
354 | 26,Female,0.7,0.2,144,36,33,8.2,4.3,1.1,1
355 | 48,Female,1.4,0.8,621,110,176,7.2,3.9,1.1,1
356 | 48,Female,0.8,0.2,150,25,23,7.5,3.9,1,1
357 | 19,Male,1.4,0.8,178,13,26,8,4.6,1.3,2
358 | 33,Male,0.7,0.2,256,21,30,8.5,3.9,0.8,1
359 | 33,Male,2.1,0.7,205,50,38,6.8,3,0.7,1
360 | 37,Male,0.7,0.2,176,28,34,5.6,2.6,0.8,1
361 | 69,Female,0.8,0.2,146,42,70,8.4,4.9,1.4,2
362 | 24,Male,0.7,0.2,218,47,26,6.6,3.3,1,1
363 | 65,Female,0.7,0.2,182,23,28,6.8,2.9,0.7,2
364 | 55,Male,1.1,0.3,215,21,15,6.2,2.9,0.8,2
365 | 42,Female,0.9,0.2,165,26,29,8.5,4.4,1,2
366 | 21,Male,0.8,0.2,183,33,57,6.8,3.5,1,2
367 | 40,Male,0.7,0.2,176,28,43,5.3,2.4,0.8,2
368 | 16,Male,0.7,0.2,418,28,35,7.2,4.1,1.3,2
369 | 60,Male,2.2,1,271,45,52,6.1,2.9,0.9,2
370 | 42,Female,0.8,0.2,182,22,20,7.2,3.9,1.1,1
371 | 58,Female,0.8,0.2,130,24,25,7,4,1.3,1
372 | 54,Female,22.6,11.4,558,30,37,7.8,3.4,0.8,1
373 | 33,Male,0.8,0.2,135,30,29,7.2,4.4,1.5,2
374 | 48,Male,0.7,0.2,326,29,17,8.7,5.5,1.7,1
375 | 25,Female,0.7,0.1,140,32,25,7.6,4.3,1.3,2
376 | 56,Female,0.7,0.1,145,26,23,7,4,1.3,2
377 | 47,Male,3.5,1.6,206,32,31,6.8,3.4,1,1
378 | 33,Male,0.7,0.1,168,35,33,7,3.7,1.1,1
379 | 20,Female,0.6,0.2,202,12,13,6.1,3,0.9,2
380 | 50,Female,0.7,0.1,192,20,41,7.3,3.3,0.8,1
381 | 72,Male,0.7,0.2,185,16,22,7.3,3.7,1,2
382 | 50,Male,1.7,0.8,331,36,53,7.3,3.4,0.9,1
383 | 39,Male,0.6,0.2,188,28,43,8.1,3.3,0.6,1
384 | 58,Female,0.7,0.1,172,27,22,6.7,3.2,0.9,1
385 | 60,Female,1.4,0.7,159,10,12,4.9,2.5,1,2
386 | 34,Male,3.7,2.1,490,115,91,6.5,2.8,0.7,1
387 | 50,Male,0.8,0.2,152,29,30,7.4,4.1,1.3,1
388 | 38,Male,2.7,1.4,105,25,21,7.5,4.2,1.2,2
389 | 51,Male,0.8,0.2,160,34,20,6.9,3.7,1.1,1
390 | 46,Male,0.8,0.2,160,31,40,7.3,3.8,1.1,1
391 | 72,Male,0.6,0.1,102,31,35,6.3,3.2,1,1
392 | 72,Male,0.8,0.2,148,23,35,6,3,1,1
393 | 75,Male,0.9,0.2,162,25,20,6.9,3.7,1.1,1
394 | 41,Male,7.5,4.3,149,94,92,6.3,3.1,0.9,1
395 | 41,Male,2.7,1.3,580,142,68,8,4,1,1
396 | 48,Female,1,0.3,310,37,56,5.9,2.5,0.7,1
397 | 45,Male,0.8,0.2,140,24,20,6.3,3.2,1,2
398 | 74,Male,1,0.3,175,30,32,6.4,3.4,1.1,1
399 | 78,Male,1,0.3,152,28,70,6.3,3.1,0.9,1
400 | 38,Male,0.8,0.2,208,25,50,7.1,3.7,1,1
401 | 27,Male,1,0.2,205,137,145,6,3,1,1
402 | 66,Female,0.7,0.2,162,24,20,6.4,3.2,1,2
403 | 50,Male,7.3,3.7,92,44,236,6.8,1.6,0.3,1
404 | 42,Female,0.5,0.1,162,155,108,8.1,4,0.9,1
405 | 65,Male,0.7,0.2,199,19,22,6.3,3.6,1.3,2
406 | 22,Male,0.8,0.2,198,20,26,6.8,3.9,1.3,1
407 | 31,Female,0.8,0.2,215,15,21,7.6,4,1.1,1
408 | 45,Male,0.7,0.2,180,18,58,6.7,3.7,1.2,2
409 | 12,Male,1,0.2,719,157,108,7.2,3.7,1,1
410 | 48,Male,2.4,1.1,554,141,73,7.5,3.6,0.9,1
411 | 48,Male,5,2.6,555,284,190,6.5,3.3,1,1
412 | 18,Male,1.4,0.6,215,440,850,5,1.9,0.6,1
413 | 23,Female,2.3,0.8,509,28,44,6.9,2.9,0.7,2
414 | 65,Male,4.9,2.7,190,33,71,7.1,2.9,0.7,1
415 | 48,Male,0.7,0.2,208,15,30,4.6,2.1,0.8,2
416 | 65,Male,1.4,0.6,260,28,24,5.2,2.2,0.7,2
417 | 70,Male,1.3,0.3,690,93,40,3.6,2.7,0.7,1
418 | 70,Male,0.6,0.1,862,76,180,6.3,2.7,0.75,1
419 | 11,Male,0.7,0.1,592,26,29,7.1,4.2,1.4,2
420 | 50,Male,4.2,2.3,450,69,50,7,3,0.7,1
421 | 55,Female,8.2,3.9,1350,52,65,6.7,2.9,0.7,1
422 | 55,Female,10.9,5.1,1350,48,57,6.4,2.3,0.5,1
423 | 26,Male,1,0.3,163,48,71,7.1,3.7,1,2
424 | 41,Male,1.2,0.5,246,34,42,6.9,3.4,0.97,1
425 | 53,Male,1.6,0.9,178,44,59,6.5,3.9,1.5,2
426 | 32,Female,0.7,0.1,240,12,15,7,3,0.7,1
427 | 58,Male,0.4,0.1,100,59,126,4.3,2.5,1.4,1
428 | 45,Male,1.3,0.6,166,49,42,5.6,2.5,0.8,2
429 | 65,Male,0.9,0.2,170,33,66,7,3,0.75,1
430 | 52,Female,0.6,0.1,194,10,12,6.9,3.3,0.9,2
431 | 73,Male,1.9,0.7,1750,102,141,5.5,2,0.5,1
432 | 53,Female,0.7,0.1,182,20,33,4.8,1.9,0.6,1
433 | 47,Female,0.8,0.2,236,10,13,6.7,2.9,0.76,2
434 | 29,Male,0.7,0.2,165,55,87,7.5,4.6,1.58,1
435 | 41,Female,0.9,0.2,201,31,24,7.6,3.8,1,2
436 | 30,Female,0.7,0.2,194,32,36,7.5,3.6,0.92,2
437 | 17,Female,0.5,0.1,206,28,21,7.1,4.5,1.7,2
438 | 23,Male,1,0.3,212,41,80,6.2,3.1,1,1
439 | 35,Male,1.6,0.7,157,15,44,5.2,2.5,0.9,1
440 | 65,Male,0.8,0.2,162,30,90,3.8,1.4,0.5,1
441 | 42,Female,0.8,0.2,168,25,18,6.2,3.1,1,1
442 | 49,Female,0.8,0.2,198,23,20,7,4.3,1.5,1
443 | 42,Female,2.3,1.1,292,29,39,4.1,1.8,0.7,1
444 | 42,Female,7.4,3.6,298,52,102,4.6,1.9,0.7,1
445 | 42,Female,0.7,0.2,152,35,81,6.2,3.2,1.06,1
446 | 61,Male,0.8,0.2,163,18,19,6.3,2.8,0.8,2
447 | 17,Male,0.9,0.2,279,40,46,7.3,4,1.2,2
448 | 54,Male,0.8,0.2,181,35,20,5.5,2.7,0.96,1
449 | 45,Female,23.3,12.8,1550,425,511,7.7,3.5,0.8,1
450 | 48,Female,0.8,0.2,142,26,25,6,2.6,0.7,1
451 | 48,Female,0.9,0.2,173,26,27,6.2,3.1,1,1
452 | 65,Male,7.9,4.3,282,50,72,6,3,1,1
453 | 35,Male,0.8,0.2,279,20,25,7.2,3.2,0.8,1
454 | 58,Male,0.9,0.2,1100,25,36,7.1,3.5,0.9,1
455 | 46,Male,0.7,0.2,224,40,23,7.1,3,0.7,1
456 | 28,Male,0.6,0.2,159,15,16,7,3.5,1,2
457 | 21,Female,0.6,0.1,186,25,22,6.8,3.4,1,1
458 | 32,Male,0.7,0.2,189,22,43,7.4,3.1,0.7,2
459 | 61,Male,0.8,0.2,192,28,35,6.9,3.4,0.9,2
460 | 26,Male,6.8,3.2,140,37,19,3.6,0.9,0.3,1
461 | 65,Male,1.1,0.5,686,16,46,5.7,1.5,0.35,1
462 | 22,Female,2.2,1,215,159,51,5.5,2.5,0.8,1
463 | 28,Female,0.8,0.2,309,55,23,6.8,4.1,1.51,1
464 | 38,Male,0.7,0.2,110,22,18,6.4,2.5,0.64,1
465 | 25,Male,0.8,0.1,130,23,42,8,4,1,1
466 | 45,Female,0.7,0.2,164,21,53,4.5,1.4,0.45,2
467 | 45,Female,0.6,0.1,270,23,42,5.1,2,0.5,2
468 | 28,Female,0.6,0.1,137,22,16,4.9,1.9,0.6,2
469 | 28,Female,1,0.3,90,18,108,6.8,3.1,0.8,2
470 | 66,Male,1,0.3,190,30,54,5.3,2.1,0.6,1
471 | 66,Male,0.8,0.2,165,22,32,4.4,2,0.8,1
472 | 66,Male,1.1,0.5,167,13,56,7.1,4.1,1.36,1
473 | 49,Female,0.6,0.1,185,17,26,6.6,2.9,0.7,2
474 | 42,Male,0.7,0.2,197,64,33,5.8,2.4,0.7,2
475 | 42,Male,1,0.3,154,38,21,6.8,3.9,1.3,2
476 | 35,Male,2,1.1,226,33,135,6,2.7,0.8,2
477 | 38,Male,2.2,1,310,119,42,7.9,4.1,1,2
478 | 38,Male,0.9,0.3,310,15,25,5.5,2.7,1,1
479 | 55,Male,0.6,0.2,220,24,32,5.1,2.4,0.88,1
480 | 33,Male,7.1,3.7,196,622,497,6.9,3.6,1.09,1
481 | 33,Male,3.4,1.6,186,779,844,7.3,3.2,0.7,1
482 | 7,Male,0.5,0.1,352,28,51,7.9,4.2,1.1,2
483 | 45,Male,2.3,1.3,282,132,368,7.3,4,1.2,1
484 | 45,Male,1.1,0.4,92,91,188,7.2,3.8,1.11,1
485 | 30,Male,0.8,0.2,182,46,57,7.8,4.3,1.2,2
486 | 62,Male,5,2.1,103,18,40,5,2.1,1.72,1
487 | 22,Female,6.7,3.2,850,154,248,6.2,2.8,0.8,1
488 | 42,Female,0.8,0.2,195,18,15,6.7,3,0.8,1
489 | 32,Male,0.7,0.2,276,102,190,6,2.9,0.93,1
490 | 60,Male,0.7,0.2,171,31,26,7,3.5,1,2
491 | 65,Male,0.8,0.1,146,17,29,5.9,3.2,1.18,2
492 | 53,Female,0.8,0.2,193,96,57,6.7,3.6,1.16,1
493 | 27,Male,1,0.3,180,56,111,6.8,3.9,1.85,2
494 | 35,Female,1,0.3,805,133,103,7.9,3.3,0.7,1
495 | 65,Male,0.7,0.2,265,30,28,5.2,1.8,0.52,2
496 | 25,Male,0.7,0.2,185,196,401,6.5,3.9,1.5,1
497 | 32,Male,0.7,0.2,165,31,29,6.1,3,0.96,2
498 | 24,Male,1,0.2,189,52,31,8,4.8,1.5,1
499 | 67,Male,2.2,1.1,198,42,39,7.2,3,0.7,1
500 | 68,Male,1.8,0.5,151,18,22,6.5,4,1.6,1
501 | 55,Male,3.6,1.6,349,40,70,7.2,2.9,0.6,1
502 | 70,Male,2.7,1.2,365,62,55,6,2.4,0.6,1
503 | 36,Male,2.8,1.5,305,28,76,5.9,2.5,0.7,1
504 | 42,Male,0.8,0.2,127,29,30,4.9,2.7,1.2,1
505 | 53,Male,19.8,10.4,238,39,221,8.1,2.5,0.4,1
506 | 32,Male,30.5,17.1,218,39,79,5.5,2.7,0.9,1
507 | 32,Male,32.6,14.1,219,95,235,5.8,3.1,1.1,1
508 | 56,Male,17.7,8.8,239,43,185,5.6,2.4,0.7,1
509 | 50,Male,0.9,0.3,194,190,73,7.5,3.9,1,1
510 | 46,Male,18.4,8.5,450,119,230,7.5,3.3,0.7,1
511 | 46,Male,20,10,254,140,540,5.4,3,1.2,1
512 | 37,Female,0.8,0.2,205,31,36,9.2,4.6,1,2
513 | 45,Male,2.2,1.6,320,37,48,6.8,3.4,1,1
514 | 56,Male,1,0.3,195,22,28,5.8,2.6,0.8,2
515 | 69,Male,0.9,0.2,215,32,24,6.9,3,0.7,1
516 | 49,Male,1,0.3,230,48,58,8.4,4.2,1,1
517 | 49,Male,3.9,2.1,189,65,181,6.9,3,0.7,1
518 | 60,Male,0.9,0.3,168,16,24,6.7,3,0.8,1
519 | 28,Male,0.9,0.2,215,50,28,8,4,1,1
520 | 45,Male,2.9,1.4,210,74,68,7.2,3.6,1,1
521 | 35,Male,26.3,12.1,108,168,630,9.2,2,0.3,1
522 | 62,Male,1.8,0.9,224,69,155,8.6,4,0.8,1
523 | 55,Male,4.4,2.9,230,14,25,7.1,2.1,0.4,1
524 | 46,Female,0.8,0.2,185,24,15,7.9,3.7,0.8,1
525 | 50,Male,0.6,0.2,137,15,16,4.8,2.6,1.1,1
526 | 29,Male,0.8,0.2,156,12,15,6.8,3.7,1.1,2
527 | 53,Female,0.9,0.2,210,35,32,8,3.9,0.9,2
528 | 46,Male,9.4,5.2,268,21,63,6.4,2.8,0.8,1
529 | 40,Male,3.5,1.6,298,68,200,7.1,3.4,0.9,1
530 | 45,Male,1.7,0.8,315,12,38,6.3,2.1,0.5,1
531 | 55,Male,3.3,1.5,214,54,152,5.1,1.8,0.5,1
532 | 22,Female,1.1,0.3,138,14,21,7,3.8,1.1,2
533 | 40,Male,30.8,18.3,285,110,186,7.9,2.7,0.5,1
534 | 62,Male,0.7,0.2,162,12,17,8.2,3.2,0.6,2
535 | 46,Female,1.4,0.4,298,509,623,3.6,1,0.3,1
536 | 39,Male,1.6,0.8,230,88,74,8,4,1,2
537 | 60,Male,19.6,9.5,466,46,52,6.1,2,0.4,1
538 | 46,Male,15.8,7.2,227,67,220,6.9,2.6,0.6,1
539 | 10,Female,0.8,0.1,395,25,75,7.6,3.6,0.9,1
540 | 52,Male,1.8,0.8,97,85,78,6.4,2.7,0.7,1
541 | 65,Female,0.7,0.2,406,24,45,7.2,3.5,0.9,2
542 | 42,Male,0.8,0.2,114,21,23,7,3,0.7,2
543 | 42,Male,0.8,0.2,198,29,19,6.6,3,0.8,2
544 | 62,Male,0.7,0.2,173,46,47,7.3,4.1,1.2,2
545 | 40,Male,1.2,0.6,204,23,27,7.6,4,1.1,1
546 | 54,Female,5.5,3.2,350,67,42,7,3.2,0.8,1
547 | 45,Female,0.7,0.2,153,41,42,4.5,2.2,0.9,2
548 | 45,Male,20.2,11.7,188,47,32,5.4,2.3,0.7,1
549 | 50,Female,27.7,10.8,380,39,348,7.1,2.3,0.4,1
550 | 42,Male,11.1,6.1,214,60,186,6.9,2.8,2.8,1
551 | 40,Female,2.1,1,768,74,141,7.8,4.9,1.6,1
552 | 46,Male,3.3,1.5,172,25,41,5.6,2.4,0.7,1
553 | 29,Male,1.2,0.4,160,20,22,6.2,3,0.9,2
554 | 45,Male,0.6,0.1,196,29,30,5.8,2.9,1,1
555 | 46,Male,10.2,4.2,232,58,140,7,2.7,0.6,1
556 | 73,Male,1.8,0.9,220,20,43,6.5,3,0.8,1
557 | 55,Male,0.8,0.2,290,139,87,7,3,0.7,1
558 | 51,Male,0.7,0.1,180,25,27,6.1,3.1,1,1
559 | 51,Male,2.9,1.2,189,80,125,6.2,3.1,1,1
560 | 51,Male,4,2.5,275,382,330,7.5,4,1.1,1
561 | 26,Male,42.8,19.7,390,75,138,7.5,2.6,0.5,1
562 | 66,Male,15.2,7.7,356,321,562,6.5,2.2,0.4,1
563 | 66,Male,16.6,7.6,315,233,384,6.9,2,0.4,1
564 | 66,Male,17.3,8.5,388,173,367,7.8,2.6,0.5,1
565 | 64,Male,1.4,0.5,298,31,83,7.2,2.6,0.5,1
566 | 38,Female,0.6,0.1,165,22,34,5.9,2.9,0.9,2
567 | 43,Male,22.5,11.8,143,22,143,6.6,2.1,0.46,1
568 | 50,Female,1,0.3,191,22,31,7.8,4,1,2
569 | 52,Male,2.7,1.4,251,20,40,6,1.7,0.39,1
570 | 20,Female,16.7,8.4,200,91,101,6.9,3.5,1.02,1
571 | 16,Male,7.7,4.1,268,213,168,7.1,4,1.2,1
572 | 16,Male,2.6,1.2,236,131,90,5.4,2.6,0.9,1
573 | 90,Male,1.1,0.3,215,46,134,6.9,3,0.7,1
574 | 32,Male,15.6,9.5,134,54,125,5.6,4,2.5,1
575 | 32,Male,3.7,1.6,612,50,88,6.2,1.9,0.4,1
576 | 32,Male,12.1,6,515,48,92,6.6,2.4,0.5,1
577 | 32,Male,25,13.7,560,41,88,7.9,2.5,2.5,1
578 | 32,Male,15,8.2,289,58,80,5.3,2.2,0.7,1
579 | 32,Male,12.7,8.4,190,28,47,5.4,2.6,0.9,1
580 | 60,Male,0.5,0.1,500,20,34,5.9,1.6,0.37,2
581 | 40,Male,0.6,0.1,98,35,31,6,3.2,1.1,1
582 | 52,Male,0.8,0.2,245,48,49,6.4,3.2,1,1
583 | 31,Male,1.3,0.5,184,29,32,6.8,3.4,1,1
584 | 38,Male,1,0.3,216,21,24,7.3,4.4,1.5,2
585 |
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/index.html:
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9 | Health App
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19 | Health App
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Experience the power of AI in HealthCare
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Application of AI in the healthcare can have a major impact on the daily working routine of the Doctors. It can reduce the workload of the doctors immensly.
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Making the Big World for Healthcare Smaller
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Still in 2020 many people are not able to get proper healthcare services because of not a wide reach of the doctors and hospitals. Using AI people can have a personal doctor at home.
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AI is the new electricity
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AI is powering personal devices in our homes and offices similarly to electricity. AI will be the next big thing after the electricity that will hugely impact our daily life
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Quick Prediction
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We have used light ML model that can load within no time without compromising on the accuracy of the prediction. Light models quickly load which helps in better response of the page.
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Wide Range
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This Web-App provides a wide range of predicting the contamination multiple infectious diseases like Kidney disease, Diabetes, Liver Infection, Heart Disease & Breast Cancer.
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Highly Accurate
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Though this app provides a wide range and high speed the accuracy has not been compromised. Hyper-parameters have been selected very carefully so that the accuracy is not hampered.
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Less Doctors: India is facing a huge shortage of Doctors
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The shortage of professionals like doctors, nurses, technicians and infrastructure as given by the report of WHO that 0.76 doctors and 2.09 nurses are present per 1000 people. Additionally, Indian healthcare is also facing acute shortage of hospital beds with 1.3 hospital beds per 1000 population.
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Low Accessibility: Non-uniform Accessibility to Healthcare across nation
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The physical access to hospitals is still the major barrier to both preventive and curative health services, and also the major differences between the Rural and the Urban India. By making use of web technology and AI people in remote areas can also get access to the health-care.
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Affordability: Good healthcare facilities still a dream of the common people in India
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It is the biggest problem many Poor and marginalised are hit the most as by the government estimates, approx. 63 million people are faced with poverty every year because of their healthcare expenditure. Using the technology in a better way healthcare can be made affordable for everyone
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/model_codes/cancer.py:
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1 | import numpy as np
2 | import pandas as pd
3 | from sklearn import ensemble
4 | from sklearn.model_selection import train_test_split
5 | from sklearn.metrics import accuracy_score, classification_report, confusion_matrix
6 | import joblib
7 |
8 | df = pd.read_csv(r"C:\Users\Mahesh Sharma\Desktop\HealthApp\data\cancer.csv")
9 | df.drop(df.columns[[0,-1]], axis=1, inplace=True)
10 | # Split the features data and the target
11 | Xdata = df.drop(['diagnosis'], axis=1)
12 | ydata = df['diagnosis']
13 |
14 | # Encoding the target value
15 | yenc = np.asarray([1 if c == 'M' else 0 for c in ydata])
16 | cols = ['concave points_mean','area_mean','radius_mean','perimeter_mean','concavity_mean',]
17 | Xdata = df[cols]
18 | print(Xdata.columns)
19 |
20 | X_train, X_test, y_train, y_test = train_test_split(Xdata, yenc,
21 | test_size=0.3,
22 | random_state=43)
23 | print('Shape training set: X:{}, y:{}'.format(X_train.shape, y_train.shape))
24 | print('Shape test set: X:{}, y:{}'.format(X_test.shape, y_test.shape))
25 |
26 | model = ensemble.RandomForestClassifier()
27 | model.fit(X_train, y_train)
28 | y_pred = model.predict(X_test)
29 | print('Accuracy : {}'.format(accuracy_score(y_test, y_pred)))
30 |
31 | clf_report = classification_report(y_test, y_pred)
32 | print('Classification report')
33 | print("---------------------")
34 | print(clf_report)
35 | print("_____________________")
36 |
37 | joblib.dump(model,r"C:\Users\Mahesh Sharma\Desktop\HealthApp\Indivisual_Deployment\Breast\cancer_model.pkl")
38 |
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/model_codes/diabetes.py:
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1 | import numpy as np
2 | import pandas as pd
3 | from sklearn import ensemble
4 | from sklearn.model_selection import train_test_split
5 | from sklearn.metrics import accuracy_score, classification_report, confusion_matrix
6 | import joblib
7 |
8 | df = pd.read_csv(r"C:\Users\Mahesh Sharma\Desktop\HealthApp\data\diabetes.csv")
9 |
10 | df['Glucose'].replace(0, np.nan, inplace=True)
11 | df['BloodPressure'].replace(0, np.nan, inplace=True)
12 | df['SkinThickness'].replace(0, np.nan, inplace=True)
13 | df['Insulin'].replace(0, np.nan, inplace=True)
14 | df['BMI'].replace(0, np.nan, inplace=True)
15 | df.drop(['SkinThickness' ,'Insulin'], axis=1, inplace=True)
16 | df = df.dropna(axis=0)
17 |
18 | cols = ['Pregnancies', 'Glucose', 'BloodPressure', 'BMI',
19 | 'DiabetesPedigreeFunction', 'Age', 'Outcome']
20 |
21 | df_outcome_1 = df[df['Outcome'] == 1].copy()
22 | i = len(df_outcome_1)
23 | df_outcome_0 = df[df['Outcome'] == 0].sample(i, random_state=1)
24 | df_balanced = df_outcome_0.append(df_outcome_1)
25 | df_balanced = df[cols]
26 |
27 |
28 | y = df_balanced['Outcome']
29 | X = df_balanced.drop('Outcome', axis=1)
30 | X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2
31 | , stratify=y, random_state=1)
32 | print('Shape training set: X:{}, y:{}'.format(X_train.shape, y_train.shape))
33 | print('Shape test set: X:{}, y:{}'.format(X_test.shape, y_test.shape))
34 |
35 | model = ensemble.RandomForestClassifier()
36 | model.fit(X_train, y_train)
37 | y_pred = model.predict(X_test)
38 | print('Accuracy : {}'.format(accuracy_score(y_test, y_pred)))
39 |
40 | clf_report = classification_report(y_test, y_pred)
41 | print('Classification report')
42 | print("---------------------")
43 | print(clf_report)
44 | print("_____________________")
45 |
46 | joblib.dump(model,r"C:\Users\Mahesh Sharma\Desktop\HealthApp\Indivisual_Deployment\Diabetes_API\diabetes_model.pkl")
47 |
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/model_codes/heart.py:
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1 | import numpy as np
2 | import pandas as pd
3 | from sklearn import ensemble
4 | from sklearn.model_selection import train_test_split
5 | from sklearn.metrics import accuracy_score, classification_report, confusion_matrix
6 | import joblib
7 |
8 | df = pd.read_csv("../data/heart.csv")
9 |
10 | categorical_val = []
11 | continous_val = []
12 | for column in df.columns:
13 | if len(df[column].unique()) <= 10:
14 | categorical_val.append(column)
15 | else:
16 | continous_val.append(column)
17 |
18 | categorical_val.remove('target')
19 | dataset = pd.get_dummies(df, columns = categorical_val)
20 |
21 | cols = ['cp', 'trestbps', 'chol', 'fbs', 'restecg', 'thalach', 'exang']
22 | X = df[cols]
23 | y = dataset.target
24 |
25 | X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.3, random_state=42)
26 |
27 | print('Shape training set: X:{}, y:{}'.format(X_train.shape, y_train.shape))
28 | print('Shape test set: X:{}, y:{}'.format(X_test.shape, y_test.shape))
29 |
30 | model = ensemble.RandomForestClassifier()
31 | model.fit(X_train, y_train)
32 | y_pred = model.predict(X_test)
33 | print('Accuracy : {}'.format(accuracy_score(y_test, y_pred)))
34 |
35 | clf_report = classification_report(y_test, y_pred)
36 | print('Classification report')
37 | print("---------------------")
38 | print(clf_report)
39 | print("_____________________")
40 |
41 | joblib.dump(model,r"C:\Users\Mahesh Sharma\Desktop\HealthApp\Indivisual_Deployment\Heart_API\heart_model.pkl")
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/model_codes/kidney.py:
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1 | import numpy as np
2 | import pandas as pd
3 | from sklearn import ensemble
4 | from sklearn.model_selection import train_test_split
5 | from sklearn.metrics import accuracy_score, classification_report, confusion_matrix
6 | import joblib
7 |
8 | df = pd.read_csv("../data/kidney.csv")
9 |
10 | df[['htn','dm','cad','pe','ane']] = df[['htn','dm','cad','pe','ane']].replace(to_replace={'yes':1,'no':0})
11 | df[['rbc','pc']] = df[['rbc','pc']].replace(to_replace={'abnormal':1,'normal':0})
12 | df[['pcc','ba']] = df[['pcc','ba']].replace(to_replace={'present':1,'notpresent':0})
13 | df[['appet']] = df[['appet']].replace(to_replace={'good':1,'poor':0,'no':np.nan})
14 | df['classification'] = df['classification'].replace(to_replace={'ckd':1.0,'ckd\t':1.0,'notckd':0.0,'no':0.0})
15 | df.rename(columns={'classification':'class'},inplace=True)
16 | df['pe'] = df['pe'].replace(to_replace='good',value=0)
17 | df['appet'] = df['appet'].replace(to_replace='no',value=0)
18 | df['cad'] = df['cad'].replace(to_replace='\tno',value=0)
19 | df['dm'] = df['dm'].replace(to_replace={'\tno':0,'\tyes':1,' yes':1, '':np.nan})
20 |
21 | df.drop('id',axis=1,inplace=True)
22 | df = df.dropna(axis=0)
23 |
24 | cols = ['bp', 'sg', 'al', 'su', 'rbc', 'pc', 'pcc']
25 | X = df[cols]
26 | y = df['class']
27 | X_train, X_test, y_train, y_test = train_test_split(X, y, test_size = 0.33, random_state=44, stratify= y)
28 | print('Shape training set: X:{}, y:{}'.format(X_train.shape, y_train.shape))
29 | print('Shape test set: X:{}, y:{}'.format(X_test.shape, y_test.shape))
30 |
31 | model = ensemble.RandomForestClassifier()
32 | model.fit(X_train, y_train)
33 | y_pred = model.predict(X_test)
34 | print('Accuracy : {}'.format(accuracy_score(y_test, y_pred)))
35 |
36 | clf_report = classification_report(y_test, y_pred)
37 | print('Classification report')
38 | print("---------------------")
39 | print(clf_report)
40 | print("_____________________")
41 |
42 | joblib.dump(model,r"C:\Users\Mahesh Sharma\Desktop\HealthApp\Indivisual_Deployment\Kidney_API\kidney_model.pkl")
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/model_codes/liver.py:
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1 | import numpy as np
2 | import pandas as pd
3 | from sklearn import ensemble
4 | from sklearn.model_selection import train_test_split
5 | from sklearn.metrics import accuracy_score, classification_report, confusion_matrix
6 | import joblib
7 |
8 | patients=pd.read_csv('../data/indian_liver_patient.csv')
9 | patients['Gender']=patients['Gender'].apply(lambda x:1 if x=='Male' else 0)
10 | patients=patients.fillna(0.94)
11 |
12 | X=patients[['Total_Bilirubin', 'Direct_Bilirubin',
13 | 'Alkaline_Phosphotase', 'Alamine_Aminotransferase',
14 | 'Total_Protiens', 'Albumin', 'Albumin_and_Globulin_Ratio']]
15 | y=patients['Dataset']
16 |
17 | X_train,X_test,y_train,y_test=train_test_split(X,y,test_size=0.3,random_state=123)
18 |
19 | print('Shape training set: X:{}, y:{}'.format(X_train.shape, y_train.shape))
20 | print('Shape test set: X:{}, y:{}'.format(X_test.shape, y_test.shape))
21 |
22 | model = ensemble.RandomForestClassifier()
23 | model.fit(X_train, y_train)
24 | y_pred = model.predict(X_test)
25 | print('Accuracy : {}'.format(accuracy_score(y_test, y_pred)))
26 |
27 | clf_report = classification_report(y_test, y_pred)
28 | print('Classification report')
29 | print("---------------------")
30 | print(clf_report)
31 | print("_____________________")
32 |
33 | joblib.dump(model,r"C:\Users\Mahesh Sharma\Desktop\HealthApp\Indivisual_Deployment\Liver_API\liver_model.pkl")
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/screenupdated.gif:
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7 | /*# sourceMappingURL=bootstrap-grid.min.css.map */
--------------------------------------------------------------------------------
/static/css/bootstrap-reboot.css:
--------------------------------------------------------------------------------
1 | /*!
2 | * Bootstrap Reboot v4.0.0 (https://getbootstrap.com)
3 | * Copyright 2011-2018 The Bootstrap Authors
4 | * Copyright 2011-2018 Twitter, Inc.
5 | * Licensed under MIT (https://github.com/twbs/bootstrap/blob/master/LICENSE)
6 | * Forked from Normalize.css, licensed MIT (https://github.com/necolas/normalize.css/blob/master/LICENSE.md)
7 | */
8 | *,
9 | *::before,
10 | *::after {
11 | box-sizing: border-box;
12 | }
13 |
14 | html {
15 | font-family: sans-serif;
16 | line-height: 1.15;
17 | -webkit-text-size-adjust: 100%;
18 | -ms-text-size-adjust: 100%;
19 | -ms-overflow-style: scrollbar;
20 | -webkit-tap-highlight-color: transparent;
21 | }
22 |
23 | @-ms-viewport {
24 | width: device-width;
25 | }
26 |
27 | article, aside, dialog, figcaption, figure, footer, header, hgroup, main, nav, section {
28 | display: block;
29 | }
30 |
31 | body {
32 | margin: 0;
33 | font-family: -apple-system, BlinkMacSystemFont, "Segoe UI", Roboto, "Helvetica Neue", Arial, sans-serif, "Apple Color Emoji", "Segoe UI Emoji", "Segoe UI Symbol";
34 | font-size: 1rem;
35 | font-weight: 400;
36 | line-height: 1.5;
37 | color: #212529;
38 | text-align: left;
39 | background-color: #fff;
40 | }
41 |
42 | [tabindex="-1"]:focus {
43 | outline: 0 !important;
44 | }
45 |
46 | hr {
47 | box-sizing: content-box;
48 | height: 0;
49 | overflow: visible;
50 | }
51 |
52 | h1, h2, h3, h4, h5, h6 {
53 | margin-top: 0;
54 | margin-bottom: 0.5rem;
55 | }
56 |
57 | p {
58 | margin-top: 0;
59 | margin-bottom: 1rem;
60 | }
61 |
62 | abbr[title],
63 | abbr[data-original-title] {
64 | text-decoration: underline;
65 | -webkit-text-decoration: underline dotted;
66 | text-decoration: underline dotted;
67 | cursor: help;
68 | border-bottom: 0;
69 | }
70 |
71 | address {
72 | margin-bottom: 1rem;
73 | font-style: normal;
74 | line-height: inherit;
75 | }
76 |
77 | ol,
78 | ul,
79 | dl {
80 | margin-top: 0;
81 | margin-bottom: 1rem;
82 | }
83 |
84 | ol ol,
85 | ul ul,
86 | ol ul,
87 | ul ol {
88 | margin-bottom: 0;
89 | }
90 |
91 | dt {
92 | font-weight: 700;
93 | }
94 |
95 | dd {
96 | margin-bottom: .5rem;
97 | margin-left: 0;
98 | }
99 |
100 | blockquote {
101 | margin: 0 0 1rem;
102 | }
103 |
104 | dfn {
105 | font-style: italic;
106 | }
107 |
108 | b,
109 | strong {
110 | font-weight: bolder;
111 | }
112 |
113 | small {
114 | font-size: 80%;
115 | }
116 |
117 | sub,
118 | sup {
119 | position: relative;
120 | font-size: 75%;
121 | line-height: 0;
122 | vertical-align: baseline;
123 | }
124 |
125 | sub {
126 | bottom: -.25em;
127 | }
128 |
129 | sup {
130 | top: -.5em;
131 | }
132 |
133 | a {
134 | color: #007bff;
135 | text-decoration: none;
136 | background-color: transparent;
137 | -webkit-text-decoration-skip: objects;
138 | }
139 |
140 | a:hover {
141 | color: #0056b3;
142 | text-decoration: underline;
143 | }
144 |
145 | a:not([href]):not([tabindex]) {
146 | color: inherit;
147 | text-decoration: none;
148 | }
149 |
150 | a:not([href]):not([tabindex]):hover, a:not([href]):not([tabindex]):focus {
151 | color: inherit;
152 | text-decoration: none;
153 | }
154 |
155 | a:not([href]):not([tabindex]):focus {
156 | outline: 0;
157 | }
158 |
159 | pre,
160 | code,
161 | kbd,
162 | samp {
163 | font-family: monospace, monospace;
164 | font-size: 1em;
165 | }
166 |
167 | pre {
168 | margin-top: 0;
169 | margin-bottom: 1rem;
170 | overflow: auto;
171 | -ms-overflow-style: scrollbar;
172 | }
173 |
174 | figure {
175 | margin: 0 0 1rem;
176 | }
177 |
178 | img {
179 | vertical-align: middle;
180 | border-style: none;
181 | }
182 |
183 | svg:not(:root) {
184 | overflow: hidden;
185 | }
186 |
187 | table {
188 | border-collapse: collapse;
189 | }
190 |
191 | caption {
192 | padding-top: 0.75rem;
193 | padding-bottom: 0.75rem;
194 | color: #6c757d;
195 | text-align: left;
196 | caption-side: bottom;
197 | }
198 |
199 | th {
200 | text-align: inherit;
201 | }
202 |
203 | label {
204 | display: inline-block;
205 | margin-bottom: .5rem;
206 | }
207 |
208 | button {
209 | border-radius: 0;
210 | }
211 |
212 | button:focus {
213 | outline: 1px dotted;
214 | outline: 5px auto -webkit-focus-ring-color;
215 | }
216 |
217 | input,
218 | button,
219 | select,
220 | optgroup,
221 | textarea {
222 | margin: 0;
223 | font-family: inherit;
224 | font-size: inherit;
225 | line-height: inherit;
226 | }
227 |
228 | button,
229 | input {
230 | overflow: visible;
231 | }
232 |
233 | button,
234 | select {
235 | text-transform: none;
236 | }
237 |
238 | button,
239 | html [type="button"],
240 | [type="reset"],
241 | [type="submit"] {
242 | -webkit-appearance: button;
243 | }
244 |
245 | button::-moz-focus-inner,
246 | [type="button"]::-moz-focus-inner,
247 | [type="reset"]::-moz-focus-inner,
248 | [type="submit"]::-moz-focus-inner {
249 | padding: 0;
250 | border-style: none;
251 | }
252 |
253 | input[type="radio"],
254 | input[type="checkbox"] {
255 | box-sizing: border-box;
256 | padding: 0;
257 | }
258 |
259 | input[type="date"],
260 | input[type="time"],
261 | input[type="datetime-local"],
262 | input[type="month"] {
263 | -webkit-appearance: listbox;
264 | }
265 |
266 | textarea {
267 | overflow: auto;
268 | resize: vertical;
269 | }
270 |
271 | fieldset {
272 | min-width: 0;
273 | padding: 0;
274 | margin: 0;
275 | border: 0;
276 | }
277 |
278 | legend {
279 | display: block;
280 | width: 100%;
281 | max-width: 100%;
282 | padding: 0;
283 | margin-bottom: .5rem;
284 | font-size: 1.5rem;
285 | line-height: inherit;
286 | color: inherit;
287 | white-space: normal;
288 | }
289 |
290 | progress {
291 | vertical-align: baseline;
292 | }
293 |
294 | [type="number"]::-webkit-inner-spin-button,
295 | [type="number"]::-webkit-outer-spin-button {
296 | height: auto;
297 | }
298 |
299 | [type="search"] {
300 | outline-offset: -2px;
301 | -webkit-appearance: none;
302 | }
303 |
304 | [type="search"]::-webkit-search-cancel-button,
305 | [type="search"]::-webkit-search-decoration {
306 | -webkit-appearance: none;
307 | }
308 |
309 | ::-webkit-file-upload-button {
310 | font: inherit;
311 | -webkit-appearance: button;
312 | }
313 |
314 | output {
315 | display: inline-block;
316 | }
317 |
318 | summary {
319 | display: list-item;
320 | cursor: pointer;
321 | }
322 |
323 | template {
324 | display: none;
325 | }
326 |
327 | [hidden] {
328 | display: none !important;
329 | }
330 | /*# sourceMappingURL=bootstrap-reboot.css.map */
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/static/css/bootstrap-reboot.min.css:
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1 | /*!
2 | * Bootstrap Reboot v4.0.0 (https://getbootstrap.com)
3 | * Copyright 2011-2018 The Bootstrap Authors
4 | * Copyright 2011-2018 Twitter, Inc.
5 | * Licensed under MIT (https://github.com/twbs/bootstrap/blob/master/LICENSE)
6 | * Forked from Normalize.css, licensed MIT (https://github.com/necolas/normalize.css/blob/master/LICENSE.md)
7 | */*,::after,::before{box-sizing:border-box}html{font-family:sans-serif;line-height:1.15;-webkit-text-size-adjust:100%;-ms-text-size-adjust:100%;-ms-overflow-style:scrollbar;-webkit-tap-highlight-color:transparent}@-ms-viewport{width:device-width}article,aside,dialog,figcaption,figure,footer,header,hgroup,main,nav,section{display:block}body{margin:0;font-family:-apple-system,BlinkMacSystemFont,"Segoe UI",Roboto,"Helvetica Neue",Arial,sans-serif,"Apple Color Emoji","Segoe UI Emoji","Segoe UI Symbol";font-size:1rem;font-weight:400;line-height:1.5;color:#212529;text-align:left;background-color:#fff}[tabindex="-1"]:focus{outline:0!important}hr{box-sizing:content-box;height:0;overflow:visible}h1,h2,h3,h4,h5,h6{margin-top:0;margin-bottom:.5rem}p{margin-top:0;margin-bottom:1rem}abbr[data-original-title],abbr[title]{text-decoration:underline;-webkit-text-decoration:underline dotted;text-decoration:underline dotted;cursor:help;border-bottom:0}address{margin-bottom:1rem;font-style:normal;line-height:inherit}dl,ol,ul{margin-top:0;margin-bottom:1rem}ol ol,ol ul,ul ol,ul ul{margin-bottom:0}dt{font-weight:700}dd{margin-bottom:.5rem;margin-left:0}blockquote{margin:0 0 1rem}dfn{font-style:italic}b,strong{font-weight:bolder}small{font-size:80%}sub,sup{position:relative;font-size:75%;line-height:0;vertical-align:baseline}sub{bottom:-.25em}sup{top:-.5em}a{color:#007bff;text-decoration:none;background-color:transparent;-webkit-text-decoration-skip:objects}a:hover{color:#0056b3;text-decoration:underline}a:not([href]):not([tabindex]){color:inherit;text-decoration:none}a:not([href]):not([tabindex]):focus,a:not([href]):not([tabindex]):hover{color:inherit;text-decoration:none}a:not([href]):not([tabindex]):focus{outline:0}code,kbd,pre,samp{font-family:monospace,monospace;font-size:1em}pre{margin-top:0;margin-bottom:1rem;overflow:auto;-ms-overflow-style:scrollbar}figure{margin:0 0 1rem}img{vertical-align:middle;border-style:none}svg:not(:root){overflow:hidden}table{border-collapse:collapse}caption{padding-top:.75rem;padding-bottom:.75rem;color:#6c757d;text-align:left;caption-side:bottom}th{text-align:inherit}label{display:inline-block;margin-bottom:.5rem}button{border-radius:0}button:focus{outline:1px dotted;outline:5px auto -webkit-focus-ring-color}button,input,optgroup,select,textarea{margin:0;font-family:inherit;font-size:inherit;line-height:inherit}button,input{overflow:visible}button,select{text-transform:none}[type=reset],[type=submit],button,html [type=button]{-webkit-appearance:button}[type=button]::-moz-focus-inner,[type=reset]::-moz-focus-inner,[type=submit]::-moz-focus-inner,button::-moz-focus-inner{padding:0;border-style:none}input[type=checkbox],input[type=radio]{box-sizing:border-box;padding:0}input[type=date],input[type=datetime-local],input[type=month],input[type=time]{-webkit-appearance:listbox}textarea{overflow:auto;resize:vertical}fieldset{min-width:0;padding:0;margin:0;border:0}legend{display:block;width:100%;max-width:100%;padding:0;margin-bottom:.5rem;font-size:1.5rem;line-height:inherit;color:inherit;white-space:normal}progress{vertical-align:baseline}[type=number]::-webkit-inner-spin-button,[type=number]::-webkit-outer-spin-button{height:auto}[type=search]{outline-offset:-2px;-webkit-appearance:none}[type=search]::-webkit-search-cancel-button,[type=search]::-webkit-search-decoration{-webkit-appearance:none}::-webkit-file-upload-button{font:inherit;-webkit-appearance:button}output{display:inline-block}summary{display:list-item;cursor:pointer}template{display:none}[hidden]{display:none!important}
8 | /*# sourceMappingURL=bootstrap-reboot.min.css.map */
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Change from `box-sizing: content-box` so that `width` is not affected by `padding` or `border`.\n// 2. Change the default font family in all browsers.\n// 3. Correct the line height in all browsers.\n// 4. Prevent adjustments of font size after orientation changes in IE on Windows Phone and in iOS.\n// 5. Setting @viewport causes scrollbars to overlap content in IE11 and Edge, so\n// we force a non-overlapping, non-auto-hiding scrollbar to counteract.\n// 6. Change the default tap highlight to be completely transparent in iOS.\n\n*,\n*::before,\n*::after {\n box-sizing: border-box; // 1\n}\n\nhtml {\n font-family: sans-serif; // 2\n line-height: 1.15; // 3\n -webkit-text-size-adjust: 100%; // 4\n -ms-text-size-adjust: 100%; // 4\n -ms-overflow-style: scrollbar; // 5\n -webkit-tap-highlight-color: rgba(0, 0, 0, 0); // 6\n}\n\n// IE10+ doesn't honor ` ` in some cases.\n@at-root {\n @-ms-viewport {\n width: device-width;\n }\n}\n\n// stylelint-disable selector-list-comma-newline-after\n// Shim for \"new\" HTML5 structural elements to display correctly (IE10, older browsers)\narticle, aside, dialog, figcaption, figure, footer, header, hgroup, main, nav, section {\n display: block;\n}\n// stylelint-enable selector-list-comma-newline-after\n\n// Body\n//\n// 1. Remove the margin in all browsers.\n// 2. As a best practice, apply a default `background-color`.\n// 3. Set an explicit initial text-align value so that we can later use the\n// the `inherit` value on things like `` elements.\n\nbody {\n margin: 0; // 1\n font-family: $font-family-base;\n font-size: $font-size-base;\n font-weight: $font-weight-base;\n line-height: $line-height-base;\n color: $body-color;\n text-align: left; // 3\n background-color: $body-bg; // 2\n}\n\n// Suppress the focus outline on elements that cannot be accessed via keyboard.\n// This prevents an unwanted focus outline from appearing around elements that\n// might still respond to pointer events.\n//\n// Credit: https://github.com/suitcss/base\n[tabindex=\"-1\"]:focus {\n outline: 0 !important;\n}\n\n\n// Content grouping\n//\n// 1. Add the correct box sizing in Firefox.\n// 2. Show the overflow in Edge and IE.\n\nhr {\n box-sizing: content-box; // 1\n height: 0; // 1\n overflow: visible; // 2\n}\n\n\n//\n// Typography\n//\n\n// Remove top margins from headings\n//\n// By default, ``-`` all receive top and bottom margins. We nuke the top\n// margin for easier control within type scales as it avoids margin collapsing.\n// stylelint-disable selector-list-comma-newline-after\nh1, h2, h3, h4, h5, h6 {\n margin-top: 0;\n margin-bottom: $headings-margin-bottom;\n}\n// stylelint-enable selector-list-comma-newline-after\n\n// Reset margins on paragraphs\n//\n// Similarly, the top margin on ` `s get reset. However, we also reset the\n// bottom margin to use `rem` units instead of `em`.\np {\n margin-top: 0;\n margin-bottom: $paragraph-margin-bottom;\n}\n\n// Abbreviations\n//\n// 1. Remove the bottom border in Firefox 39-.\n// 2. Add the correct text decoration in Chrome, Edge, IE, Opera, and Safari.\n// 3. Add explicit cursor to indicate changed behavior.\n// 4. Duplicate behavior to the data-* attribute for our tooltip plugin\n\nabbr[title],\nabbr[data-original-title] { // 4\n text-decoration: underline; // 2\n text-decoration: underline dotted; // 2\n cursor: help; // 3\n border-bottom: 0; // 1\n}\n\naddress {\n margin-bottom: 1rem;\n font-style: normal;\n line-height: inherit;\n}\n\nol,\nul,\ndl {\n margin-top: 0;\n margin-bottom: 1rem;\n}\n\nol ol,\nul ul,\nol ul,\nul ol {\n margin-bottom: 0;\n}\n\ndt {\n font-weight: $dt-font-weight;\n}\n\ndd {\n margin-bottom: .5rem;\n margin-left: 0; // Undo browser default\n}\n\nblockquote {\n margin: 0 0 1rem;\n}\n\ndfn {\n font-style: italic; // Add the correct font style in Android 4.3-\n}\n\n// stylelint-disable font-weight-notation\nb,\nstrong {\n font-weight: bolder; // Add the correct font weight in Chrome, Edge, and Safari\n}\n// stylelint-enable font-weight-notation\n\nsmall {\n font-size: 80%; // Add the correct font size in all browsers\n}\n\n//\n// Prevent `sub` and `sup` elements from affecting the line height in\n// all browsers.\n//\n\nsub,\nsup {\n position: relative;\n font-size: 75%;\n line-height: 0;\n vertical-align: baseline;\n}\n\nsub { bottom: -.25em; }\nsup { top: -.5em; }\n\n\n//\n// Links\n//\n\na {\n color: $link-color;\n text-decoration: $link-decoration;\n background-color: transparent; // Remove the gray background on active links in IE 10.\n -webkit-text-decoration-skip: objects; // Remove gaps in links underline in iOS 8+ and Safari 8+.\n\n @include hover {\n color: $link-hover-color;\n text-decoration: $link-hover-decoration;\n }\n}\n\n// And undo these styles for placeholder links/named anchors (without href)\n// which have not been made explicitly keyboard-focusable (without tabindex).\n// It would be more straightforward to just use a[href] in previous block, but that\n// causes specificity issues in many other styles that are too complex to fix.\n// See https://github.com/twbs/bootstrap/issues/19402\n\na:not([href]):not([tabindex]) {\n color: inherit;\n text-decoration: none;\n\n @include hover-focus {\n color: inherit;\n text-decoration: none;\n }\n\n &:focus {\n outline: 0;\n }\n}\n\n\n//\n// Code\n//\n\n// stylelint-disable font-family-no-duplicate-names\npre,\ncode,\nkbd,\nsamp {\n font-family: monospace, monospace; // Correct the inheritance and scaling of font size in all browsers.\n font-size: 1em; // Correct the odd `em` font sizing in all browsers.\n}\n// stylelint-enable font-family-no-duplicate-names\n\npre {\n // Remove browser default top margin\n margin-top: 0;\n // Reset browser default of `1em` to use `rem`s\n margin-bottom: 1rem;\n // Don't allow content to break outside\n overflow: auto;\n // We have @viewport set which causes scrollbars to overlap content in IE11 and Edge, so\n // we force a non-overlapping, non-auto-hiding scrollbar to counteract.\n -ms-overflow-style: scrollbar;\n}\n\n\n//\n// Figures\n//\n\nfigure {\n // Apply a consistent margin strategy (matches our type styles).\n margin: 0 0 1rem;\n}\n\n\n//\n// Images and content\n//\n\nimg {\n vertical-align: middle;\n border-style: none; // Remove the border on images inside links in IE 10-.\n}\n\nsvg:not(:root) {\n overflow: hidden; // Hide the overflow in IE\n}\n\n\n//\n// Tables\n//\n\ntable {\n border-collapse: collapse; // Prevent double borders\n}\n\ncaption {\n padding-top: $table-cell-padding;\n padding-bottom: $table-cell-padding;\n color: $text-muted;\n text-align: left;\n caption-side: bottom;\n}\n\nth {\n // Matches default `
` alignment by inheriting from the ``, or the\n // closest parent with a set `text-align`.\n text-align: inherit;\n}\n\n\n//\n// Forms\n//\n\nlabel {\n // Allow labels to use `margin` for spacing.\n display: inline-block;\n margin-bottom: .5rem;\n}\n\n// Remove the default `border-radius` that macOS Chrome adds.\n//\n// Details at https://github.com/twbs/bootstrap/issues/24093\nbutton {\n border-radius: 0;\n}\n\n// Work around a Firefox/IE bug where the transparent `button` background\n// results in a loss of the default `button` focus styles.\n//\n// Credit: https://github.com/suitcss/base/\nbutton:focus {\n outline: 1px dotted;\n outline: 5px auto -webkit-focus-ring-color;\n}\n\ninput,\nbutton,\nselect,\noptgroup,\ntextarea {\n margin: 0; // Remove the margin in Firefox and Safari\n font-family: inherit;\n font-size: inherit;\n line-height: inherit;\n}\n\nbutton,\ninput {\n overflow: visible; // Show the overflow in Edge\n}\n\nbutton,\nselect {\n text-transform: none; // Remove the inheritance of text transform in Firefox\n}\n\n// 1. Prevent a WebKit bug where (2) destroys native `audio` and `video`\n// controls in Android 4.\n// 2. Correct the inability to style clickable types in iOS and Safari.\nbutton,\nhtml [type=\"button\"], // 1\n[type=\"reset\"],\n[type=\"submit\"] {\n -webkit-appearance: button; // 2\n}\n\n// Remove inner border and padding from Firefox, but don't restore the outline like Normalize.\nbutton::-moz-focus-inner,\n[type=\"button\"]::-moz-focus-inner,\n[type=\"reset\"]::-moz-focus-inner,\n[type=\"submit\"]::-moz-focus-inner {\n padding: 0;\n border-style: none;\n}\n\ninput[type=\"radio\"],\ninput[type=\"checkbox\"] {\n box-sizing: border-box; // 1. Add the correct box sizing in IE 10-\n padding: 0; // 2. Remove the padding in IE 10-\n}\n\n\ninput[type=\"date\"],\ninput[type=\"time\"],\ninput[type=\"datetime-local\"],\ninput[type=\"month\"] {\n // Remove the default appearance of temporal inputs to avoid a Mobile Safari\n // bug where setting a custom line-height prevents text from being vertically\n // centered within the input.\n // See https://bugs.webkit.org/show_bug.cgi?id=139848\n // and https://github.com/twbs/bootstrap/issues/11266\n -webkit-appearance: listbox;\n}\n\ntextarea {\n overflow: auto; // Remove the default vertical scrollbar in IE.\n // Textareas should really only resize vertically so they don't break their (horizontal) containers.\n resize: vertical;\n}\n\nfieldset {\n // Browsers set a default `min-width: min-content;` on fieldsets,\n // unlike e.g. ``s, which have `min-width: 0;` by default.\n // So we reset that to ensure fieldsets behave more like a standard block element.\n // See https://github.com/twbs/bootstrap/issues/12359\n // and https://html.spec.whatwg.org/multipage/#the-fieldset-and-legend-elements\n min-width: 0;\n // Reset the default outline behavior of fieldsets so they don't affect page layout.\n padding: 0;\n margin: 0;\n border: 0;\n}\n\n// 1. Correct the text wrapping in Edge and IE.\n// 2. Correct the color inheritance from `fieldset` elements in IE.\nlegend {\n display: block;\n width: 100%;\n max-width: 100%; // 1\n padding: 0;\n margin-bottom: .5rem;\n font-size: 1.5rem;\n line-height: inherit;\n color: inherit; // 2\n white-space: normal; // 1\n}\n\nprogress {\n vertical-align: baseline; // Add the correct vertical alignment in Chrome, Firefox, and Opera.\n}\n\n// Correct the cursor style of increment and decrement buttons in Chrome.\n[type=\"number\"]::-webkit-inner-spin-button,\n[type=\"number\"]::-webkit-outer-spin-button {\n height: auto;\n}\n\n[type=\"search\"] {\n // This overrides the extra rounded corners on search inputs in iOS so that our\n // `.form-control` class can properly style them. Note that this cannot simply\n // be added to `.form-control` as it's not specific enough. For details, see\n // https://github.com/twbs/bootstrap/issues/11586.\n outline-offset: -2px; // 2. Correct the outline style in Safari.\n -webkit-appearance: none;\n}\n\n//\n// Remove the inner padding and cancel buttons in Chrome and Safari on macOS.\n//\n\n[type=\"search\"]::-webkit-search-cancel-button,\n[type=\"search\"]::-webkit-search-decoration {\n -webkit-appearance: none;\n}\n\n//\n// 1. Correct the inability to style clickable types in iOS and Safari.\n// 2. Change font properties to `inherit` in Safari.\n//\n\n::-webkit-file-upload-button {\n font: inherit; // 2\n -webkit-appearance: button; // 1\n}\n\n//\n// Correct element displays\n//\n\noutput {\n display: inline-block;\n}\n\nsummary {\n display: list-item; // Add the correct display in all browsers\n cursor: pointer;\n}\n\ntemplate {\n display: none; // Add the correct display in IE\n}\n\n// Always hide an element with the `hidden` HTML attribute (from PureCSS).\n// Needed for proper display in IE 10-.\n[hidden] {\n display: none !important;\n}\n","/*!\n * Bootstrap Reboot v4.0.0 (https://getbootstrap.com)\n * Copyright 2011-2018 The Bootstrap Authors\n * Copyright 2011-2018 Twitter, Inc.\n * Licensed under MIT (https://github.com/twbs/bootstrap/blob/master/LICENSE)\n * Forked from Normalize.css, licensed MIT (https://github.com/necolas/normalize.css/blob/master/LICENSE.md)\n */\n*,\n*::before,\n*::after {\n box-sizing: border-box;\n}\n\nhtml {\n font-family: sans-serif;\n line-height: 1.15;\n -webkit-text-size-adjust: 100%;\n -ms-text-size-adjust: 100%;\n -ms-overflow-style: scrollbar;\n -webkit-tap-highlight-color: transparent;\n}\n\n@-ms-viewport {\n width: device-width;\n}\n\narticle, aside, dialog, figcaption, figure, footer, header, hgroup, main, nav, section {\n display: block;\n}\n\nbody {\n margin: 0;\n font-family: -apple-system, BlinkMacSystemFont, \"Segoe UI\", Roboto, \"Helvetica Neue\", Arial, sans-serif, \"Apple Color Emoji\", \"Segoe UI Emoji\", \"Segoe UI Symbol\";\n font-size: 1rem;\n font-weight: 400;\n line-height: 1.5;\n color: #212529;\n text-align: left;\n background-color: #fff;\n}\n\n[tabindex=\"-1\"]:focus {\n outline: 0 !important;\n}\n\nhr {\n box-sizing: content-box;\n height: 0;\n overflow: visible;\n}\n\nh1, h2, h3, h4, h5, h6 {\n margin-top: 0;\n margin-bottom: 0.5rem;\n}\n\np {\n margin-top: 0;\n margin-bottom: 1rem;\n}\n\nabbr[title],\nabbr[data-original-title] {\n text-decoration: underline;\n -webkit-text-decoration: underline dotted;\n text-decoration: underline dotted;\n cursor: help;\n border-bottom: 0;\n}\n\naddress {\n margin-bottom: 1rem;\n font-style: normal;\n line-height: inherit;\n}\n\nol,\nul,\ndl {\n margin-top: 0;\n margin-bottom: 1rem;\n}\n\nol ol,\nul ul,\nol ul,\nul ol {\n margin-bottom: 0;\n}\n\ndt {\n font-weight: 700;\n}\n\ndd {\n margin-bottom: .5rem;\n margin-left: 0;\n}\n\nblockquote {\n margin: 0 0 1rem;\n}\n\ndfn {\n font-style: italic;\n}\n\nb,\nstrong {\n font-weight: bolder;\n}\n\nsmall {\n font-size: 80%;\n}\n\nsub,\nsup {\n position: relative;\n font-size: 75%;\n line-height: 0;\n vertical-align: baseline;\n}\n\nsub {\n bottom: -.25em;\n}\n\nsup {\n top: -.5em;\n}\n\na {\n color: #007bff;\n text-decoration: none;\n background-color: transparent;\n -webkit-text-decoration-skip: objects;\n}\n\na:hover {\n color: #0056b3;\n text-decoration: underline;\n}\n\na:not([href]):not([tabindex]) {\n color: inherit;\n text-decoration: none;\n}\n\na:not([href]):not([tabindex]):hover, a:not([href]):not([tabindex]):focus {\n color: inherit;\n text-decoration: none;\n}\n\na:not([href]):not([tabindex]):focus {\n outline: 0;\n}\n\npre,\ncode,\nkbd,\nsamp {\n font-family: monospace, monospace;\n font-size: 1em;\n}\n\npre {\n margin-top: 0;\n margin-bottom: 1rem;\n overflow: auto;\n -ms-overflow-style: scrollbar;\n}\n\nfigure {\n margin: 0 0 1rem;\n}\n\nimg {\n vertical-align: middle;\n border-style: none;\n}\n\nsvg:not(:root) {\n overflow: hidden;\n}\n\ntable {\n border-collapse: collapse;\n}\n\ncaption {\n padding-top: 0.75rem;\n padding-bottom: 0.75rem;\n color: #6c757d;\n text-align: left;\n caption-side: bottom;\n}\n\nth {\n text-align: inherit;\n}\n\nlabel {\n display: inline-block;\n margin-bottom: .5rem;\n}\n\nbutton {\n border-radius: 0;\n}\n\nbutton:focus {\n outline: 1px dotted;\n outline: 5px auto -webkit-focus-ring-color;\n}\n\ninput,\nbutton,\nselect,\noptgroup,\ntextarea {\n margin: 0;\n font-family: inherit;\n font-size: inherit;\n line-height: inherit;\n}\n\nbutton,\ninput {\n overflow: visible;\n}\n\nbutton,\nselect {\n text-transform: none;\n}\n\nbutton,\nhtml [type=\"button\"],\n[type=\"reset\"],\n[type=\"submit\"] {\n -webkit-appearance: button;\n}\n\nbutton::-moz-focus-inner,\n[type=\"button\"]::-moz-focus-inner,\n[type=\"reset\"]::-moz-focus-inner,\n[type=\"submit\"]::-moz-focus-inner {\n padding: 0;\n border-style: none;\n}\n\ninput[type=\"radio\"],\ninput[type=\"checkbox\"] {\n box-sizing: border-box;\n padding: 0;\n}\n\ninput[type=\"date\"],\ninput[type=\"time\"],\ninput[type=\"datetime-local\"],\ninput[type=\"month\"] {\n -webkit-appearance: listbox;\n}\n\ntextarea {\n overflow: auto;\n resize: vertical;\n}\n\nfieldset {\n min-width: 0;\n padding: 0;\n margin: 0;\n border: 0;\n}\n\nlegend {\n display: block;\n width: 100%;\n max-width: 100%;\n padding: 0;\n margin-bottom: .5rem;\n font-size: 1.5rem;\n line-height: inherit;\n color: inherit;\n white-space: normal;\n}\n\nprogress {\n vertical-align: baseline;\n}\n\n[type=\"number\"]::-webkit-inner-spin-button,\n[type=\"number\"]::-webkit-outer-spin-button {\n height: auto;\n}\n\n[type=\"search\"] {\n outline-offset: -2px;\n -webkit-appearance: none;\n}\n\n[type=\"search\"]::-webkit-search-cancel-button,\n[type=\"search\"]::-webkit-search-decoration {\n -webkit-appearance: none;\n}\n\n::-webkit-file-upload-button {\n font: inherit;\n -webkit-appearance: button;\n}\n\noutput {\n display: inline-block;\n}\n\nsummary {\n display: list-item;\n cursor: pointer;\n}\n\ntemplate {\n display: none;\n}\n\n[hidden] {\n display: none !important;\n}\n/*# sourceMappingURL=bootstrap-reboot.css.map */","/*!\n * Bootstrap Reboot v4.0.0 (https://getbootstrap.com)\n * Copyright 2011-2018 The Bootstrap Authors\n * Copyright 2011-2018 Twitter, Inc.\n * Licensed under MIT (https://github.com/twbs/bootstrap/blob/master/LICENSE)\n * Forked from Normalize.css, licensed MIT (https://github.com/necolas/normalize.css/blob/master/LICENSE.md)\n */\n*,\n*::before,\n*::after {\n box-sizing: border-box;\n}\n\nhtml {\n font-family: sans-serif;\n line-height: 1.15;\n -webkit-text-size-adjust: 100%;\n -ms-text-size-adjust: 100%;\n -ms-overflow-style: scrollbar;\n -webkit-tap-highlight-color: transparent;\n}\n\n@-ms-viewport {\n width: device-width;\n}\n\narticle, aside, dialog, figcaption, figure, footer, header, hgroup, main, nav, section {\n display: block;\n}\n\nbody {\n margin: 0;\n font-family: -apple-system, BlinkMacSystemFont, \"Segoe UI\", Roboto, \"Helvetica Neue\", Arial, sans-serif, \"Apple Color Emoji\", \"Segoe UI Emoji\", \"Segoe UI Symbol\";\n font-size: 1rem;\n font-weight: 400;\n line-height: 1.5;\n color: #212529;\n text-align: left;\n background-color: #fff;\n}\n\n[tabindex=\"-1\"]:focus {\n outline: 0 !important;\n}\n\nhr {\n box-sizing: content-box;\n height: 0;\n overflow: visible;\n}\n\nh1, h2, h3, h4, h5, h6 {\n margin-top: 0;\n margin-bottom: 0.5rem;\n}\n\np {\n margin-top: 0;\n margin-bottom: 1rem;\n}\n\nabbr[title],\nabbr[data-original-title] {\n text-decoration: underline;\n text-decoration: underline dotted;\n cursor: help;\n border-bottom: 0;\n}\n\naddress {\n margin-bottom: 1rem;\n font-style: normal;\n line-height: inherit;\n}\n\nol,\nul,\ndl {\n margin-top: 0;\n margin-bottom: 1rem;\n}\n\nol ol,\nul ul,\nol ul,\nul ol {\n margin-bottom: 0;\n}\n\ndt {\n font-weight: 700;\n}\n\ndd {\n margin-bottom: .5rem;\n margin-left: 0;\n}\n\nblockquote {\n margin: 0 0 1rem;\n}\n\ndfn {\n font-style: italic;\n}\n\nb,\nstrong {\n font-weight: bolder;\n}\n\nsmall {\n font-size: 80%;\n}\n\nsub,\nsup {\n position: relative;\n font-size: 75%;\n line-height: 0;\n vertical-align: baseline;\n}\n\nsub {\n bottom: -.25em;\n}\n\nsup {\n top: -.5em;\n}\n\na {\n color: #007bff;\n text-decoration: none;\n background-color: transparent;\n -webkit-text-decoration-skip: objects;\n}\n\na:hover {\n color: #0056b3;\n text-decoration: underline;\n}\n\na:not([href]):not([tabindex]) {\n color: inherit;\n text-decoration: none;\n}\n\na:not([href]):not([tabindex]):hover, a:not([href]):not([tabindex]):focus {\n color: inherit;\n text-decoration: none;\n}\n\na:not([href]):not([tabindex]):focus {\n outline: 0;\n}\n\npre,\ncode,\nkbd,\nsamp {\n font-family: monospace, monospace;\n font-size: 1em;\n}\n\npre {\n margin-top: 0;\n margin-bottom: 1rem;\n overflow: auto;\n -ms-overflow-style: scrollbar;\n}\n\nfigure {\n margin: 0 0 1rem;\n}\n\nimg {\n vertical-align: middle;\n border-style: none;\n}\n\nsvg:not(:root) {\n overflow: hidden;\n}\n\ntable {\n border-collapse: collapse;\n}\n\ncaption {\n padding-top: 0.75rem;\n padding-bottom: 0.75rem;\n color: #6c757d;\n text-align: left;\n caption-side: bottom;\n}\n\nth {\n text-align: inherit;\n}\n\nlabel {\n display: inline-block;\n margin-bottom: .5rem;\n}\n\nbutton {\n border-radius: 0;\n}\n\nbutton:focus {\n outline: 1px dotted;\n outline: 5px auto -webkit-focus-ring-color;\n}\n\ninput,\nbutton,\nselect,\noptgroup,\ntextarea {\n margin: 0;\n font-family: inherit;\n font-size: inherit;\n line-height: inherit;\n}\n\nbutton,\ninput {\n overflow: visible;\n}\n\nbutton,\nselect {\n text-transform: none;\n}\n\nbutton,\nhtml [type=\"button\"],\n[type=\"reset\"],\n[type=\"submit\"] {\n -webkit-appearance: button;\n}\n\nbutton::-moz-focus-inner,\n[type=\"button\"]::-moz-focus-inner,\n[type=\"reset\"]::-moz-focus-inner,\n[type=\"submit\"]::-moz-focus-inner {\n padding: 0;\n border-style: none;\n}\n\ninput[type=\"radio\"],\ninput[type=\"checkbox\"] {\n box-sizing: border-box;\n padding: 0;\n}\n\ninput[type=\"date\"],\ninput[type=\"time\"],\ninput[type=\"datetime-local\"],\ninput[type=\"month\"] {\n -webkit-appearance: listbox;\n}\n\ntextarea {\n overflow: auto;\n resize: vertical;\n}\n\nfieldset {\n min-width: 0;\n padding: 0;\n margin: 0;\n border: 0;\n}\n\nlegend {\n display: block;\n width: 100%;\n max-width: 100%;\n padding: 0;\n margin-bottom: .5rem;\n font-size: 1.5rem;\n line-height: inherit;\n color: inherit;\n white-space: normal;\n}\n\nprogress {\n vertical-align: baseline;\n}\n\n[type=\"number\"]::-webkit-inner-spin-button,\n[type=\"number\"]::-webkit-outer-spin-button {\n height: auto;\n}\n\n[type=\"search\"] {\n outline-offset: -2px;\n -webkit-appearance: none;\n}\n\n[type=\"search\"]::-webkit-search-cancel-button,\n[type=\"search\"]::-webkit-search-decoration {\n -webkit-appearance: none;\n}\n\n::-webkit-file-upload-button {\n font: inherit;\n -webkit-appearance: button;\n}\n\noutput {\n display: inline-block;\n}\n\nsummary {\n display: list-item;\n cursor: pointer;\n}\n\ntemplate {\n display: none;\n}\n\n[hidden] {\n display: none !important;\n}\n\n/*# sourceMappingURL=bootstrap-reboot.css.map */","// stylelint-disable indentation\n\n// Hover mixin and `$enable-hover-media-query` are deprecated.\n//\n// Origally added during our alphas and maintained during betas, this mixin was\n// designed to prevent `:hover` stickiness on iOS—an issue where hover styles\n// would persist after initial touch.\n//\n// For backward compatibility, we've kept these mixins and updated them to\n// always return their regular psuedo-classes instead of a shimmed media query.\n//\n// Issue: https://github.com/twbs/bootstrap/issues/25195\n\n@mixin hover {\n &:hover { @content; }\n}\n\n@mixin hover-focus {\n &:hover,\n &:focus {\n @content;\n }\n}\n\n@mixin plain-hover-focus {\n &,\n &:hover,\n &:focus {\n @content;\n }\n}\n\n@mixin hover-focus-active {\n &:hover,\n &:focus,\n &:active {\n @content;\n }\n}\n"]}
--------------------------------------------------------------------------------
/static/css/carousel.css:
--------------------------------------------------------------------------------
1 | /* GLOBAL STYLES
2 | -------------------------------------------------- */
3 | /* Padding below the footer and lighter body text */
4 |
5 | body {
6 | padding-top: 3rem;
7 | padding-bottom: 3rem;
8 | color: #5a5a5a;
9 | }
10 |
11 |
12 | /* CUSTOMIZE THE CAROUSEL
13 | -------------------------------------------------- */
14 |
15 | /* Carousel base class */
16 | .carousel {
17 | margin-bottom: 4rem;
18 | }
19 | /* Since positioning the image, we need to help out the caption */
20 | .carousel-caption {
21 | bottom: 3rem;
22 | z-index: 10;
23 | }
24 |
25 | /* Declare heights because of positioning of img element */
26 | .carousel-item {
27 | height: 32rem;
28 | background-color: #777;
29 | }
30 | .carousel-item > img {
31 | position: absolute;
32 | top: 0;
33 | left: 0;
34 | min-width: 100%;
35 | height: 32rem;
36 | }
37 |
38 |
39 | /* MARKETING CONTENT
40 | -------------------------------------------------- */
41 |
42 | /* Center align the text within the three columns below the carousel */
43 | .marketing .col-lg-4 {
44 | margin-bottom: 1.5rem;
45 | text-align: center;
46 | }
47 | .marketing h2 {
48 | font-weight: 400;
49 | }
50 | .marketing .col-lg-4 p {
51 | margin-right: .75rem;
52 | margin-left: .75rem;
53 | }
54 |
55 |
56 | /* Featurettes
57 | ------------------------- */
58 |
59 | .featurette-divider {
60 | margin: 5rem 0; /* Space out the Bootstrap
more */
61 | }
62 |
63 | /* Thin out the marketing headings */
64 | .featurette-heading {
65 | font-weight: 300;
66 | line-height: 1;
67 | letter-spacing: -.05rem;
68 | }
69 |
70 |
71 | /* RESPONSIVE CSS
72 | -------------------------------------------------- */
73 |
74 | @media (min-width: 40em) {
75 | /* Bump up size of carousel content */
76 | .carousel-caption p {
77 | margin-bottom: 1.25rem;
78 | font-size: 1.25rem;
79 | line-height: 1.4;
80 | }
81 |
82 | .featurette-heading {
83 | font-size: 50px;
84 | }
85 | }
86 |
87 | @media (min-width: 62em) {
88 | .featurette-heading {
89 | margin-top: 7rem;
90 | }
91 | }
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