├── .gitignore ├── 0.1 Car_Price_Prediction ├── Procfile ├── app.py ├── car data.csv ├── car_model.pkl ├── car_price_prediction.py ├── main.py ├── requirements.txt ├── static │ └── css │ │ └── register.css └── templates │ ├── cccc.png │ └── index.html ├── 2.0 Sentiment analysis ├── Procfile ├── app.py ├── app.yaml ├── main.py ├── requirements.txt ├── sentiment_model1.pkl ├── sentiment_vectorizer.pkl ├── static │ ├── people_photo │ │ └── plot.png │ └── register.css └── templates │ ├── index.html │ ├── nlp.jpg │ └── result.html ├── 3. Breast Cancer Wisconsin (Diagnostic) ├── Breast-Cancer-Dataset.csv ├── Breast-Cancer.ipynb ├── Breast-Cancer.sav └── Explanation.docx ├── 4. Diabete ├── Diabete.ipynb ├── Explanation.docx ├── diabetes.csv └── diabetes_model.sav ├── 5. Indian Liver Patient ├── Explanation.docx ├── Liver_disease.sav ├── indian_liver_patient.csv └── indian_liver_patient.ipynb ├── 6. Chronic KIdney Disease ├── Chronic-KIdney-Disease-dataset.csv ├── Explanation.docx ├── Kidney-Disease .ipynb └── Kidney-Disease.sav ├── 7. Maleria Cell Images ├── malaria-cell-cnn.h5 └── malaria_cells_image_classification.ipynb ├── 8. Cardiovascular_Disease ├── Cardiovascular_Disease.ipynb ├── Cardiovascular_Disease_dataset.csv ├── Cardiovascular_Disease_model.sav ├── Explanation.docx ├── cardio.csv └── notebook30ebc5777e.ipynb ├── 9. Heart_Disease_UCI ├── Explanation.docx ├── Heart_Disease_UCI.ipynb ├── Heart_Disease_UCI.sav ├── Report.PNG └── heart_disease_dataset.csv ├── DNA_classification ├── .ds_Store └── Clasification_Of_dna.ipynb ├── House_price_pridiction ├── HousePricePrediction.ipynb ├── read.md ├── test.csv └── train.csv ├── LICENSE ├── Prediction-And-Analysis-of-crime ├── crime-clean.csv ├── crime-full.csv ├── crime_prediction-checkpoint.ipynb └── read.md └── README.md /.gitignore: -------------------------------------------------------------------------------- 1 | # Object file 2 | *.o 3 | 4 | # Ada Library Information 5 | *.ali 6 | -------------------------------------------------------------------------------- /0.1 Car_Price_Prediction/Procfile: -------------------------------------------------------------------------------- 1 | web: gunicorn app:app 2 | -------------------------------------------------------------------------------- /0.1 Car_Price_Prediction/app.py: -------------------------------------------------------------------------------- 1 | # -*- coding: utf-8 -*- 2 | """ 3 | Created on Wed Jul 1 19:25:53 2020 4 | 5 | @author: Gaurav 6 | """ 7 | 8 | from flask import Flask, render_template, request 9 | import pickle 10 | import numpy as np 11 | from sklearn.preprocessing import StandardScaler 12 | from datetime import date 13 | 14 | 15 | app=Flask(__name__) 16 | model=pickle.load(open("car_model.pkl","rb")) 17 | 18 | standard_to=StandardScaler() 19 | 20 | @app.route("/",methods=['GET']) 21 | def Home(): 22 | return render_template('index.html') 23 | 24 | 25 | @app.route("/predict",methods=['POST']) 26 | def predict(): 27 | fuel_Type_Diesel=0 28 | if request.method=='POST': 29 | Year=int(request.form['Year']) 30 | Present_Price=float(request.form['Present_Price']) 31 | Kms_Driven=int(request.form['Kms_Driven']) 32 | Kms_Driven2=np.log(Kms_Driven) 33 | Owner=int(request.form['Owner']) 34 | Fuel_Type_Petrol=request.form['Fuel_Type_Petrol'] 35 | if(Fuel_Type_Petrol=='Petrol'): 36 | Fuel_Type_Petrol=1 37 | Fuel_Type_Diesel=0 38 | else: 39 | Fuel_Type_Petrol=0 40 | Fuel_Type_Diesel=1 41 | 42 | Year=date.today().year-Year 43 | # Year=2020-Year 44 | Seller_Type_Individual=request.form['Seller_Type_Individual'] 45 | if(Seller_Type_Individual=='Individual'): 46 | Seller_Type_Individual=1 47 | else: 48 | Seller_Type_Individual=0 49 | Transmission_Mannual=request.form['Transmission_Mannual'] 50 | if(Transmission_Mannual=='Mannual'): 51 | Transmission_Mannual=1 52 | else: 53 | Transmission_Mannual=0 54 | prediction=model.predict([[Present_Price,Kms_Driven2,Owner,Year,Fuel_Type_Diesel,Fuel_Type_Petrol,Seller_Type_Individual,Transmission_Mannual]]) 55 | output=round(prediction[0],2) 56 | if output<0: 57 | return render_template('index.html',prediction_texts="Sorry you cannot sell this car") 58 | else: 59 | return render_template('index.html',prediction_text="You Can Sell The Car at {}".format(output)) 60 | else: 61 | return render_template('index.html') 62 | 63 | 64 | if __name__=="__main__": 65 | app.run() 66 | 67 | 68 | 69 | 70 | -------------------------------------------------------------------------------- /0.1 Car_Price_Prediction/car data.csv: -------------------------------------------------------------------------------- 1 | Car_Name,Year,Selling_Price,Present_Price,Kms_Driven,Fuel_Type,Seller_Type,Transmission,Owner 2 | ritz,2014,3.35,5.59,27000,Petrol,Dealer,Manual,0 3 | sx4,2013,4.75,9.54,43000,Diesel,Dealer,Manual,0 4 | ciaz,2017,7.25,9.85,6900,Petrol,Dealer,Manual,0 5 | wagon r,2011,2.85,4.15,5200,Petrol,Dealer,Manual,0 6 | swift,2014,4.6,6.87,42450,Diesel,Dealer,Manual,0 7 | vitara brezza,2018,9.25,9.83,2071,Diesel,Dealer,Manual,0 8 | ciaz,2015,6.75,8.12,18796,Petrol,Dealer,Manual,0 9 | s cross,2015,6.5,8.61,33429,Diesel,Dealer,Manual,0 10 | ciaz,2016,8.75,8.89,20273,Diesel,Dealer,Manual,0 11 | ciaz,2015,7.45,8.92,42367,Diesel,Dealer,Manual,0 12 | alto 800,2017,2.85,3.6,2135,Petrol,Dealer,Manual,0 13 | ciaz,2015,6.85,10.38,51000,Diesel,Dealer,Manual,0 14 | ciaz,2015,7.5,9.94,15000,Petrol,Dealer,Automatic,0 15 | ertiga,2015,6.1,7.71,26000,Petrol,Dealer,Manual,0 16 | dzire,2009,2.25,7.21,77427,Petrol,Dealer,Manual,0 17 | ertiga,2016,7.75,10.79,43000,Diesel,Dealer,Manual,0 18 | ertiga,2015,7.25,10.79,41678,Diesel,Dealer,Manual,0 19 | ertiga,2016,7.75,10.79,43000,Diesel,Dealer,Manual,0 20 | wagon r,2015,3.25,5.09,35500,CNG,Dealer,Manual,0 21 | sx4,2010,2.65,7.98,41442,Petrol,Dealer,Manual,0 22 | alto k10,2016,2.85,3.95,25000,Petrol,Dealer,Manual,0 23 | ignis,2017,4.9,5.71,2400,Petrol,Dealer,Manual,0 24 | sx4,2011,4.4,8.01,50000,Petrol,Dealer,Automatic,0 25 | alto k10,2014,2.5,3.46,45280,Petrol,Dealer,Manual,0 26 | wagon r,2013,2.9,4.41,56879,Petrol,Dealer,Manual,0 27 | swift,2011,3,4.99,20000,Petrol,Dealer,Manual,0 28 | swift,2013,4.15,5.87,55138,Petrol,Dealer,Manual,0 29 | swift,2017,6,6.49,16200,Petrol,Individual,Manual,0 30 | alto k10,2010,1.95,3.95,44542,Petrol,Dealer,Manual,0 31 | ciaz,2015,7.45,10.38,45000,Diesel,Dealer,Manual,0 32 | ritz,2012,3.1,5.98,51439,Diesel,Dealer,Manual,0 33 | ritz,2011,2.35,4.89,54200,Petrol,Dealer,Manual,0 34 | swift,2014,4.95,7.49,39000,Diesel,Dealer,Manual,0 35 | ertiga,2014,6,9.95,45000,Diesel,Dealer,Manual,0 36 | dzire,2014,5.5,8.06,45000,Diesel,Dealer,Manual,0 37 | sx4,2011,2.95,7.74,49998,CNG,Dealer,Manual,0 38 | dzire,2015,4.65,7.2,48767,Petrol,Dealer,Manual,0 39 | 800,2003,0.35,2.28,127000,Petrol,Individual,Manual,0 40 | alto k10,2016,3,3.76,10079,Petrol,Dealer,Manual,0 41 | sx4,2003,2.25,7.98,62000,Petrol,Dealer,Manual,0 42 | baleno,2016,5.85,7.87,24524,Petrol,Dealer,Automatic,0 43 | alto k10,2014,2.55,3.98,46706,Petrol,Dealer,Manual,0 44 | sx4,2008,1.95,7.15,58000,Petrol,Dealer,Manual,0 45 | dzire,2014,5.5,8.06,45780,Diesel,Dealer,Manual,0 46 | omni,2012,1.25,2.69,50000,Petrol,Dealer,Manual,0 47 | ciaz,2014,7.5,12.04,15000,Petrol,Dealer,Automatic,0 48 | ritz,2013,2.65,4.89,64532,Petrol,Dealer,Manual,0 49 | wagon r,2006,1.05,4.15,65000,Petrol,Dealer,Manual,0 50 | ertiga,2015,5.8,7.71,25870,Petrol,Dealer,Manual,0 51 | ciaz,2017,7.75,9.29,37000,Petrol,Dealer,Automatic,0 52 | fortuner,2012,14.9,30.61,104707,Diesel,Dealer,Automatic,0 53 | fortuner,2015,23,30.61,40000,Diesel,Dealer,Automatic,0 54 | innova,2017,18,19.77,15000,Diesel,Dealer,Automatic,0 55 | fortuner,2013,16,30.61,135000,Diesel,Individual,Automatic,0 56 | innova,2005,2.75,10.21,90000,Petrol,Individual,Manual,0 57 | corolla altis,2009,3.6,15.04,70000,Petrol,Dealer,Automatic,0 58 | etios cross,2015,4.5,7.27,40534,Petrol,Dealer,Manual,0 59 | corolla altis,2010,4.75,18.54,50000,Petrol,Dealer,Manual,0 60 | etios g,2014,4.1,6.8,39485,Petrol,Dealer,Manual,1 61 | fortuner,2014,19.99,35.96,41000,Diesel,Dealer,Automatic,0 62 | corolla altis,2013,6.95,18.61,40001,Petrol,Dealer,Manual,0 63 | etios cross,2015,4.5,7.7,40588,Petrol,Dealer,Manual,0 64 | fortuner,2014,18.75,35.96,78000,Diesel,Dealer,Automatic,0 65 | fortuner,2015,23.5,35.96,47000,Diesel,Dealer,Automatic,0 66 | fortuner,2017,33,36.23,6000,Diesel,Dealer,Automatic,0 67 | etios liva,2014,4.75,6.95,45000,Diesel,Dealer,Manual,0 68 | innova,2017,19.75,23.15,11000,Petrol,Dealer,Automatic,0 69 | fortuner,2010,9.25,20.45,59000,Diesel,Dealer,Manual,0 70 | corolla altis,2011,4.35,13.74,88000,Petrol,Dealer,Manual,0 71 | corolla altis,2016,14.25,20.91,12000,Petrol,Dealer,Manual,0 72 | etios liva,2014,3.95,6.76,71000,Diesel,Dealer,Manual,0 73 | corolla altis,2011,4.5,12.48,45000,Diesel,Dealer,Manual,0 74 | corolla altis,2013,7.45,18.61,56001,Petrol,Dealer,Manual,0 75 | etios liva,2011,2.65,5.71,43000,Petrol,Dealer,Manual,0 76 | etios cross,2014,4.9,8.93,83000,Diesel,Dealer,Manual,0 77 | etios g,2015,3.95,6.8,36000,Petrol,Dealer,Manual,0 78 | corolla altis,2013,5.5,14.68,72000,Petrol,Dealer,Manual,0 79 | corolla,2004,1.5,12.35,135154,Petrol,Dealer,Automatic,0 80 | corolla altis,2010,5.25,22.83,80000,Petrol,Dealer,Automatic,0 81 | fortuner,2012,14.5,30.61,89000,Diesel,Dealer,Automatic,0 82 | corolla altis,2016,14.73,14.89,23000,Diesel,Dealer,Manual,0 83 | etios gd,2015,4.75,7.85,40000,Diesel,Dealer,Manual,0 84 | innova,2017,23,25.39,15000,Diesel,Dealer,Automatic,0 85 | innova,2015,12.5,13.46,38000,Diesel,Dealer,Manual,0 86 | innova,2005,3.49,13.46,197176,Diesel,Dealer,Manual,0 87 | camry,2006,2.5,23.73,142000,Petrol,Individual,Automatic,3 88 | land cruiser,2010,35,92.6,78000,Diesel,Dealer,Manual,0 89 | corolla altis,2012,5.9,13.74,56000,Petrol,Dealer,Manual,0 90 | etios liva,2013,3.45,6.05,47000,Petrol,Dealer,Manual,0 91 | etios g,2014,4.75,6.76,40000,Petrol,Dealer,Manual,0 92 | corolla altis,2009,3.8,18.61,62000,Petrol,Dealer,Manual,0 93 | innova,2014,11.25,16.09,58242,Diesel,Dealer,Manual,0 94 | innova,2005,3.51,13.7,75000,Petrol,Dealer,Manual,0 95 | fortuner,2015,23,30.61,40000,Diesel,Dealer,Automatic,0 96 | corolla altis,2008,4,22.78,89000,Petrol,Dealer,Automatic,0 97 | corolla altis,2012,5.85,18.61,72000,Petrol,Dealer,Manual,0 98 | innova,2016,20.75,25.39,29000,Diesel,Dealer,Automatic,0 99 | corolla altis,2017,17,18.64,8700,Petrol,Dealer,Manual,0 100 | corolla altis,2013,7.05,18.61,45000,Petrol,Dealer,Manual,0 101 | fortuner,2010,9.65,20.45,50024,Diesel,Dealer,Manual,0 102 | Royal Enfield Thunder 500,2016,1.75,1.9,3000,Petrol,Individual,Manual,0 103 | UM Renegade Mojave,2017,1.7,1.82,1400,Petrol,Individual,Manual,0 104 | KTM RC200,2017,1.65,1.78,4000,Petrol,Individual,Manual,0 105 | Bajaj Dominar 400,2017,1.45,1.6,1200,Petrol,Individual,Manual,0 106 | Royal Enfield Classic 350,2017,1.35,1.47,4100,Petrol,Individual,Manual,0 107 | KTM RC390,2015,1.35,2.37,21700,Petrol,Individual,Manual,0 108 | Hyosung GT250R,2014,1.35,3.45,16500,Petrol,Individual,Manual,1 109 | Royal Enfield Thunder 350,2013,1.25,1.5,15000,Petrol,Individual,Manual,0 110 | Royal Enfield Thunder 350,2016,1.2,1.5,18000,Petrol,Individual,Manual,0 111 | Royal Enfield Classic 350,2017,1.2,1.47,11000,Petrol,Individual,Manual,0 112 | KTM RC200,2016,1.2,1.78,6000,Petrol,Individual,Manual,0 113 | Royal Enfield Thunder 350,2016,1.15,1.5,8700,Petrol,Individual,Manual,0 114 | KTM 390 Duke ,2014,1.15,2.4,7000,Petrol,Individual,Manual,0 115 | Mahindra Mojo XT300,2016,1.15,1.4,35000,Petrol,Individual,Manual,0 116 | Royal Enfield Classic 350,2015,1.15,1.47,17000,Petrol,Individual,Manual,0 117 | Royal Enfield Classic 350,2015,1.11,1.47,17500,Petrol,Individual,Manual,0 118 | Royal Enfield Classic 350,2013,1.1,1.47,33000,Petrol,Individual,Manual,0 119 | Royal Enfield Thunder 500,2015,1.1,1.9,14000,Petrol,Individual,Manual,0 120 | Royal Enfield Classic 350,2015,1.1,1.47,26000,Petrol,Individual,Manual,0 121 | Royal Enfield Thunder 500,2013,1.05,1.9,5400,Petrol,Individual,Manual,0 122 | Bajaj Pulsar RS200,2016,1.05,1.26,5700,Petrol,Individual,Manual,0 123 | Royal Enfield Thunder 350,2011,1.05,1.5,6900,Petrol,Individual,Manual,0 124 | Royal Enfield Bullet 350,2016,1.05,1.17,6000,Petrol,Individual,Manual,0 125 | Royal Enfield Classic 350,2013,1,1.47,46500,Petrol,Individual,Manual,0 126 | Royal Enfield Classic 500,2012,0.95,1.75,11500,Petrol,Individual,Manual,0 127 | Royal Enfield Classic 500,2009,0.9,1.75,40000,Petrol,Individual,Manual,0 128 | Bajaj Avenger 220,2017,0.9,0.95,1300,Petrol,Individual,Manual,0 129 | Bajaj Avenger 150,2016,0.75,0.8,7000,Petrol,Individual,Manual,0 130 | Honda CB Hornet 160R,2017,0.8,0.87,3000,Petrol,Individual,Manual,0 131 | Yamaha FZ S V 2.0,2017,0.78,0.84,5000,Petrol,Individual,Manual,0 132 | Honda CB Hornet 160R,2017,0.75,0.87,11000,Petrol,Individual,Manual,0 133 | Yamaha FZ 16,2015,0.75,0.82,18000,Petrol,Individual,Manual,0 134 | Bajaj Avenger 220,2017,0.75,0.95,3500,Petrol,Individual,Manual,0 135 | Bajaj Avenger 220,2016,0.72,0.95,500,Petrol,Individual,Manual,0 136 | TVS Apache RTR 160,2017,0.65,0.81,11800,Petrol,Individual,Manual,0 137 | Bajaj Pulsar 150,2015,0.65,0.74,5000,Petrol,Individual,Manual,0 138 | Honda CBR 150,2014,0.65,1.2,23500,Petrol,Individual,Manual,0 139 | Hero Extreme,2013,0.65,0.787,16000,Petrol,Individual,Manual,0 140 | Honda CB Hornet 160R,2016,0.6,0.87,15000,Petrol,Individual,Manual,0 141 | Bajaj Avenger 220 dtsi,2015,0.6,0.95,16600,Petrol,Individual,Manual,0 142 | Honda CBR 150,2013,0.6,1.2,32000,Petrol,Individual,Manual,0 143 | Bajaj Avenger 150 street,2016,0.6,0.8,20000,Petrol,Individual,Manual,0 144 | Yamaha FZ v 2.0,2015,0.6,0.84,29000,Petrol,Individual,Manual,0 145 | Yamaha FZ v 2.0,2016,0.6,0.84,25000,Petrol,Individual,Manual,0 146 | Bajaj Pulsar NS 200,2014,0.6,0.99,25000,Petrol,Individual,Manual,0 147 | TVS Apache RTR 160,2012,0.6,0.81,19000,Petrol,Individual,Manual,0 148 | Hero Extreme,2014,0.55,0.787,15000,Petrol,Individual,Manual,0 149 | Yamaha FZ S V 2.0,2015,0.55,0.84,58000,Petrol,Individual,Manual,0 150 | Bajaj Pulsar 220 F,2010,0.52,0.94,45000,Petrol,Individual,Manual,0 151 | Bajaj Pulsar 220 F,2016,0.51,0.94,24000,Petrol,Individual,Manual,0 152 | TVS Apache RTR 180,2011,0.5,0.826,6000,Petrol,Individual,Manual,0 153 | Hero Passion X pro,2016,0.5,0.55,31000,Petrol,Individual,Manual,0 154 | Bajaj Pulsar NS 200,2012,0.5,0.99,13000,Petrol,Individual,Manual,0 155 | Bajaj Pulsar NS 200,2013,0.5,0.99,45000,Petrol,Individual,Manual,0 156 | Yamaha Fazer ,2014,0.5,0.88,8000,Petrol,Individual,Manual,0 157 | Honda Activa 4G,2017,0.48,0.51,4300,Petrol,Individual,Automatic,0 158 | TVS Sport ,2017,0.48,0.52,15000,Petrol,Individual,Manual,0 159 | Yamaha FZ S V 2.0,2015,0.48,0.84,23000,Petrol,Individual,Manual,0 160 | Honda Dream Yuga ,2017,0.48,0.54,8600,Petrol,Individual,Manual,0 161 | Honda Activa 4G,2017,0.45,0.51,4000,Petrol,Individual,Automatic,0 162 | Bajaj Avenger Street 220,2011,0.45,0.95,24000,Petrol,Individual,Manual,0 163 | TVS Apache RTR 180,2014,0.45,0.826,23000,Petrol,Individual,Manual,0 164 | Bajaj Pulsar NS 200,2012,0.45,0.99,14500,Petrol,Individual,Manual,0 165 | Bajaj Avenger 220 dtsi,2010,0.45,0.95,27000,Petrol,Individual,Manual,0 166 | Hero Splender iSmart,2016,0.45,0.54,14000,Petrol,Individual,Manual,0 167 | Activa 3g,2016,0.45,0.54,500,Petrol,Individual,Automatic,0 168 | Hero Passion Pro,2016,0.45,0.55,1000,Petrol,Individual,Manual,0 169 | TVS Apache RTR 160,2014,0.42,0.81,42000,Petrol,Individual,Manual,0 170 | Honda CB Trigger,2013,0.42,0.73,12000,Petrol,Individual,Manual,0 171 | Hero Splender iSmart,2015,0.4,0.54,14000,Petrol,Individual,Manual,0 172 | Yamaha FZ S ,2012,0.4,0.83,5500,Petrol,Individual,Manual,0 173 | Hero Passion Pro,2015,0.4,0.55,6700,Petrol,Individual,Manual,0 174 | Bajaj Pulsar 135 LS,2014,0.4,0.64,13700,Petrol,Individual,Manual,0 175 | Activa 4g,2017,0.4,0.51,1300,Petrol,Individual,Automatic,0 176 | Honda CB Unicorn,2015,0.38,0.72,38600,Petrol,Individual,Manual,0 177 | Hero Honda CBZ extreme,2011,0.38,0.787,75000,Petrol,Individual,Manual,0 178 | Honda Karizma,2011,0.35,1.05,30000,Petrol,Individual,Manual,0 179 | Honda Activa 125,2016,0.35,0.57,24000,Petrol,Individual,Automatic,0 180 | TVS Jupyter,2014,0.35,0.52,19000,Petrol,Individual,Automatic,0 181 | Honda Karizma,2010,0.31,1.05,213000,Petrol,Individual,Manual,0 182 | Hero Honda Passion Pro,2012,0.3,0.51,60000,Petrol,Individual,Manual,0 183 | Hero Splender Plus,2016,0.3,0.48,50000,Petrol,Individual,Manual,0 184 | Honda CB Shine,2013,0.3,0.58,30000,Petrol,Individual,Manual,0 185 | Bajaj Discover 100,2013,0.27,0.47,21000,Petrol,Individual,Manual,0 186 | Bajaj Pulsar 150,2008,0.25,0.75,26000,Petrol,Individual,Manual,1 187 | Suzuki Access 125,2008,0.25,0.58,1900,Petrol,Individual,Automatic,0 188 | TVS Wego,2010,0.25,0.52,22000,Petrol,Individual,Automatic,0 189 | Honda CB twister,2013,0.25,0.51,32000,Petrol,Individual,Manual,0 190 | Hero Glamour,2013,0.25,0.57,18000,Petrol,Individual,Manual,0 191 | Hero Super Splendor,2005,0.2,0.57,55000,Petrol,Individual,Manual,0 192 | Bajaj Pulsar 150,2008,0.2,0.75,60000,Petrol,Individual,Manual,0 193 | Bajaj Discover 125,2012,0.2,0.57,25000,Petrol,Individual,Manual,1 194 | Hero Hunk,2007,0.2,0.75,49000,Petrol,Individual,Manual,1 195 | Hero Ignitor Disc,2013,0.2,0.65,24000,Petrol,Individual,Manual,1 196 | Hero CBZ Xtreme,2008,0.2,0.787,50000,Petrol,Individual,Manual,0 197 | Bajaj ct 100,2015,0.18,0.32,35000,Petrol,Individual,Manual,0 198 | Activa 3g,2008,0.17,0.52,500000,Petrol,Individual,Automatic,0 199 | Honda CB twister,2010,0.16,0.51,33000,Petrol,Individual,Manual,0 200 | Bajaj Discover 125,2011,0.15,0.57,35000,Petrol,Individual,Manual,1 201 | Honda CB Shine,2007,0.12,0.58,53000,Petrol,Individual,Manual,0 202 | Bajaj Pulsar 150,2006,0.1,0.75,92233,Petrol,Individual,Manual,0 203 | i20,2010,3.25,6.79,58000,Diesel,Dealer,Manual,1 204 | grand i10,2015,4.4,5.7,28200,Petrol,Dealer,Manual,0 205 | i10,2011,2.95,4.6,53460,Petrol,Dealer,Manual,0 206 | eon,2015,2.75,4.43,28282,Petrol,Dealer,Manual,0 207 | grand i10,2016,5.25,5.7,3493,Petrol,Dealer,Manual,1 208 | xcent,2017,5.75,7.13,12479,Petrol,Dealer,Manual,0 209 | grand i10,2015,5.15,5.7,34797,Petrol,Dealer,Automatic,0 210 | i20,2017,7.9,8.1,3435,Petrol,Dealer,Manual,0 211 | grand i10,2015,4.85,5.7,21125,Diesel,Dealer,Manual,0 212 | i10,2012,3.1,4.6,35775,Petrol,Dealer,Manual,0 213 | elantra,2015,11.75,14.79,43535,Diesel,Dealer,Manual,0 214 | creta,2016,11.25,13.6,22671,Petrol,Dealer,Manual,0 215 | i20,2011,2.9,6.79,31604,Petrol,Dealer,Manual,0 216 | grand i10,2017,5.25,5.7,20114,Petrol,Dealer,Manual,0 217 | verna,2012,4.5,9.4,36100,Petrol,Dealer,Manual,0 218 | eon,2016,2.9,4.43,12500,Petrol,Dealer,Manual,0 219 | eon,2016,3.15,4.43,15000,Petrol,Dealer,Manual,0 220 | verna,2014,6.45,9.4,45078,Petrol,Dealer,Manual,0 221 | verna,2012,4.5,9.4,36000,Petrol,Dealer,Manual,0 222 | eon,2017,3.5,4.43,38488,Petrol,Dealer,Manual,0 223 | i20,2013,4.5,6.79,32000,Petrol,Dealer,Automatic,0 224 | i20,2014,6,7.6,77632,Diesel,Dealer,Manual,0 225 | verna,2015,8.25,9.4,61381,Diesel,Dealer,Manual,0 226 | verna,2013,5.11,9.4,36198,Petrol,Dealer,Automatic,0 227 | i10,2011,2.7,4.6,22517,Petrol,Dealer,Manual,0 228 | grand i10,2015,5.25,5.7,24678,Petrol,Dealer,Manual,0 229 | i10,2011,2.55,4.43,57000,Petrol,Dealer,Manual,0 230 | verna,2012,4.95,9.4,60000,Diesel,Dealer,Manual,0 231 | i20,2012,3.1,6.79,52132,Diesel,Dealer,Manual,0 232 | verna,2013,6.15,9.4,45000,Diesel,Dealer,Manual,0 233 | verna,2017,9.25,9.4,15001,Petrol,Dealer,Manual,0 234 | elantra,2015,11.45,14.79,12900,Petrol,Dealer,Automatic,0 235 | grand i10,2013,3.9,5.7,53000,Diesel,Dealer,Manual,0 236 | grand i10,2015,5.5,5.7,4492,Petrol,Dealer,Manual,0 237 | verna,2017,9.1,9.4,15141,Petrol,Dealer,Manual,0 238 | eon,2016,3.1,4.43,11849,Petrol,Dealer,Manual,0 239 | creta,2015,11.25,13.6,68000,Diesel,Dealer,Manual,0 240 | verna,2013,4.8,9.4,60241,Petrol,Dealer,Manual,0 241 | eon,2012,2,4.43,23709,Petrol,Dealer,Manual,0 242 | verna,2012,5.35,9.4,32322,Diesel,Dealer,Manual,0 243 | xcent,2015,4.75,7.13,35866,Petrol,Dealer,Manual,1 244 | xcent,2014,4.4,7.13,34000,Petrol,Dealer,Manual,0 245 | i20,2016,6.25,7.6,7000,Petrol,Dealer,Manual,0 246 | verna,2013,5.95,9.4,49000,Diesel,Dealer,Manual,0 247 | verna,2012,5.2,9.4,71000,Diesel,Dealer,Manual,0 248 | i20,2012,3.75,6.79,35000,Petrol,Dealer,Manual,0 249 | verna,2015,5.95,9.4,36000,Petrol,Dealer,Manual,0 250 | i10,2013,4,4.6,30000,Petrol,Dealer,Manual,0 251 | i20,2016,5.25,7.6,17000,Petrol,Dealer,Manual,0 252 | creta,2016,12.9,13.6,35934,Diesel,Dealer,Manual,0 253 | city,2013,5,9.9,56701,Petrol,Dealer,Manual,0 254 | brio,2015,5.4,6.82,31427,Petrol,Dealer,Automatic,0 255 | city,2014,7.2,9.9,48000,Diesel,Dealer,Manual,0 256 | city,2013,5.25,9.9,54242,Petrol,Dealer,Manual,0 257 | brio,2012,3,5.35,53675,Petrol,Dealer,Manual,0 258 | city,2016,10.25,13.6,49562,Petrol,Dealer,Manual,0 259 | city,2015,8.5,13.6,40324,Petrol,Dealer,Manual,0 260 | city,2015,8.4,13.6,25000,Petrol,Dealer,Manual,0 261 | amaze,2014,3.9,7,36054,Petrol,Dealer,Manual,0 262 | city,2016,9.15,13.6,29223,Petrol,Dealer,Manual,0 263 | brio,2016,5.5,5.97,5600,Petrol,Dealer,Manual,0 264 | amaze,2015,4,5.8,40023,Petrol,Dealer,Manual,0 265 | jazz,2016,6.6,7.7,16002,Petrol,Dealer,Manual,0 266 | amaze,2015,4,7,40026,Petrol,Dealer,Manual,0 267 | jazz,2017,6.5,8.7,21200,Petrol,Dealer,Manual,0 268 | amaze,2014,3.65,7,35000,Petrol,Dealer,Manual,0 269 | city,2016,8.35,9.4,19434,Diesel,Dealer,Manual,0 270 | brio,2017,4.8,5.8,19000,Petrol,Dealer,Manual,0 271 | city,2015,6.7,10,18828,Petrol,Dealer,Manual,0 272 | city,2011,4.1,10,69341,Petrol,Dealer,Manual,0 273 | city,2009,3,10,69562,Petrol,Dealer,Manual,0 274 | city,2015,7.5,10,27600,Petrol,Dealer,Manual,0 275 | jazz,2010,2.25,7.5,61203,Petrol,Dealer,Manual,0 276 | brio,2014,5.3,6.8,16500,Petrol,Dealer,Manual,0 277 | city,2016,10.9,13.6,30753,Petrol,Dealer,Automatic,0 278 | city,2015,8.65,13.6,24800,Petrol,Dealer,Manual,0 279 | city,2015,9.7,13.6,21780,Petrol,Dealer,Manual,0 280 | jazz,2016,6,8.4,4000,Petrol,Dealer,Manual,0 281 | city,2014,6.25,13.6,40126,Petrol,Dealer,Manual,0 282 | brio,2015,5.25,5.9,14465,Petrol,Dealer,Manual,0 283 | city,2006,2.1,7.6,50456,Petrol,Dealer,Manual,0 284 | city,2014,8.25,14,63000,Diesel,Dealer,Manual,0 285 | city,2016,8.99,11.8,9010,Petrol,Dealer,Manual,0 286 | brio,2013,3.5,5.9,9800,Petrol,Dealer,Manual,0 287 | jazz,2016,7.4,8.5,15059,Petrol,Dealer,Automatic,0 288 | jazz,2016,5.65,7.9,28569,Petrol,Dealer,Manual,0 289 | amaze,2015,5.75,7.5,44000,Petrol,Dealer,Automatic,0 290 | city,2015,8.4,13.6,34000,Petrol,Dealer,Manual,0 291 | city,2016,10.11,13.6,10980,Petrol,Dealer,Manual,0 292 | amaze,2014,4.5,6.4,19000,Petrol,Dealer,Manual,0 293 | brio,2015,5.4,6.1,31427,Petrol,Dealer,Manual,0 294 | jazz,2016,6.4,8.4,12000,Petrol,Dealer,Manual,0 295 | city,2010,3.25,9.9,38000,Petrol,Dealer,Manual,0 296 | amaze,2014,3.75,6.8,33019,Petrol,Dealer,Manual,0 297 | city,2015,8.55,13.09,60076,Diesel,Dealer,Manual,0 298 | city,2016,9.5,11.6,33988,Diesel,Dealer,Manual,0 299 | brio,2015,4,5.9,60000,Petrol,Dealer,Manual,0 300 | city,2009,3.35,11,87934,Petrol,Dealer,Manual,0 301 | city,2017,11.5,12.5,9000,Diesel,Dealer,Manual,0 302 | brio,2016,5.3,5.9,5464,Petrol,Dealer,Manual,0 303 | -------------------------------------------------------------------------------- /0.1 Car_Price_Prediction/car_model.pkl: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/GauravRajwada/100-Data-Science-Projects/ff69c5ea24308eaaa8356e066bfacdf42d11cfcc/0.1 Car_Price_Prediction/car_model.pkl -------------------------------------------------------------------------------- /0.1 Car_Price_Prediction/car_price_prediction.py: -------------------------------------------------------------------------------- 1 | # -*- coding: utf-8 -*- 2 | """ 3 | Created on Wed Jul 1 16:05:18 2020 4 | 5 | @author: Gaurav 6 | """ 7 | 8 | from datetime import date 9 | import pandas as pd 10 | import matplotlib.pyplot as plt 11 | import seaborn as sb 12 | from sklearn.model_selection import RandomizedSearchCV 13 | from sklearn.ensemble import RandomForestRegressor 14 | from sklearn.model_selection import train_test_split 15 | from sklearn.ensemble import ExtraTreesRegressor 16 | import numpy as np 17 | 18 | df=pd.read_csv("E:/All Data Set/Car_Datasets.csv") 19 | 20 | cur_year= date.today().year 21 | df['current_year']=cur_year 22 | df['No_of_year']=df["current_year"]-df['Year'] 23 | 24 | df.drop(['Year',"current_year","Car_Name"],axis=1,inplace=True) 25 | 26 | df=pd.get_dummies(df,drop_first=True) 27 | 28 | sb.pairplot(df) 29 | 30 | """Plotting correlation""" 31 | corrmat=df.corr() 32 | top_corr_features=corrmat.index 33 | plt.figure(figsize=(20,20)) 34 | g=sb.heatmap(df[top_corr_features].corr(),annot=True,cmap="RdYlGn") 35 | 36 | x=df.iloc[:,1:] 37 | y=df.iloc[:,0] 38 | 39 | """Finding Important features""" 40 | #Feature Importance 41 | model=ExtraTreesRegressor() 42 | model.fit(x,y) 43 | 44 | #plot graph of feature importances for better visualization 45 | feat_importances = pd.Series(model.feature_importances_, index=x.columns) 46 | feat_importances.nlargest(8).plot(kind='barh') 47 | plt.show() 48 | 49 | x_train,x_test,y_train,y_test=train_test_split(x,y,test_size=0.25) 50 | 51 | 52 | 53 | #Randomized Search CV 54 | n_estimators = [int(x) for x in np.linspace(start = 100, stop = 1200, num = 12)] 55 | max_features = ['auto', 'sqrt'] 56 | max_depth = [int(x) for x in np.linspace(5, 30, num = 6)] 57 | min_samples_split = [2, 5, 10, 15, 100] 58 | min_samples_leaf = [1, 2, 5, 10] 59 | 60 | random_grid = {'n_estimators': n_estimators, 61 | 'max_features': max_features, 62 | 'max_depth': max_depth, 63 | 'min_samples_split': min_samples_split, 64 | 'min_samples_leaf': min_samples_leaf} 65 | 66 | 67 | rf=RandomForestRegressor() 68 | regressor=RandomizedSearchCV(estimator = rf, param_distributions = random_grid,scoring='neg_mean_squared_error', n_iter = 10, cv = 5, verbose=2, random_state=42, n_jobs = -1) 69 | 70 | 71 | regressor.fit(x_train,y_train) 72 | 73 | y_pred=regressor.predict(x_test) 74 | 75 | sb.distplot(y_test-y_pred) 76 | 77 | import pickle 78 | file = open("E:/Project/Car_Dekho/car_model.pkl", 'wb') 79 | 80 | pickle.dump(regressor,file) 81 | -------------------------------------------------------------------------------- /0.1 Car_Price_Prediction/main.py: -------------------------------------------------------------------------------- 1 | # -*- coding: utf-8 -*- 2 | """ 3 | Created on Thu Jul 2 09:10:18 2020 4 | 5 | @author: Gaurav 6 | """ 7 | 8 | from flask import Flask, render_template, request 9 | import jsonify 10 | import requests 11 | import pickle 12 | import numpy as np 13 | import sklearn 14 | from sklearn.preprocessing import StandardScaler 15 | from datetime import date 16 | 17 | app=Flask(__name__) 18 | model=pickle.load(open("car_model.pkl","rb")) 19 | 20 | standard_to=StandardScaler() 21 | @app.route("/",methods=['GET']) 22 | 23 | def Home(): 24 | return render_template('index.html') 25 | 26 | 27 | @app.route("/predict",methods=['POST']) 28 | def predict(): 29 | fuel_Type_Diesel=0 30 | if request.method=='POST': 31 | Year=int(request.form['Year']) 32 | Present_Price=float(request.form['Present_Price']) 33 | Kms_Driven=int(request.form['Kms_Driven']) 34 | Kms_Driven2=np.log(Kms_Driven) 35 | Owner=int(request.form['Owner']) 36 | Fuel_Type_Petrol=request.form['Fuel_Type_Petrol'] 37 | if(Fuel_Type_Petrol=='Petrol'): 38 | Fuel_Type_Petrol=1 39 | Fuel_Type_Diesel=0 40 | else: 41 | Fuel_Type_Petrol=0 42 | Fuel_Type_Diesel=1 43 | 44 | Year=date.today().year-Year 45 | # Year=2020-Year 46 | Seller_Type_Individual=request.form['Seller_Type_Individual'] 47 | if(Seller_Type_Individual=='Individual'): 48 | Seller_Type_Individual=1 49 | else: 50 | Seller_Type_Individual=0 51 | Transmission_Mannual=request.form['Transmission_Mannual'] 52 | if(Transmission_Mannual=='Mannual'): 53 | Transmission_Mannual=1 54 | else: 55 | Transmission_Mannual=0 56 | prediction=model.predict([[Present_Price,Kms_Driven2,Owner,Year,Fuel_Type_Diesel,Fuel_Type_Petrol,Seller_Type_Individual,Transmission_Mannual]]) 57 | output=round(prediction[0],2) 58 | if output<0: 59 | return render_template('index.html',prediction_texts="Sorry you cannot sell this car") 60 | else: 61 | return render_template('index.html',prediction_text="You Can Sell The Car at {}".format(output)) 62 | else: 63 | return render_template('index.html') 64 | 65 | 66 | 67 | if __name__=="__main__": 68 | app.run() 69 | 70 | 71 | 72 | 73 | 74 | 75 | -------------------------------------------------------------------------------- /0.1 Car_Price_Prediction/requirements.txt: -------------------------------------------------------------------------------- 1 | backcall==0.2.0 2 | certifi==2020.6.20 3 | chardet==3.0.4 4 | click==7.1.2 5 | cloudpickle==1.4.1 6 | colorama==0.4.3 7 | cycler==0.10.0 8 | decorator==4.4.2 9 | Flask==1.1.2 10 | get==0.0.0 11 | idna==2.10 12 | ipykernel==5.3.0 13 | ipython==7.16.1 14 | ipython-genutils==0.2.0 15 | itsdangerous==1.1.0 16 | jedi==0.17.1 17 | Jinja2==2.11.2 18 | joblib==0.15.1 19 | jsonify==0.5 20 | jupyter-client==6.1.5 21 | jupyter-core==4.6.3 22 | kiwisolver==1.2.0 23 | MarkupSafe==1.1.1 24 | matplotlib==3.2.2 25 | nltk==3.5 26 | numpy==1.19.0 27 | pandas==1.0.5 28 | parso==0.7.0 29 | pickleshare==0.7.5 30 | post==0.0.0 31 | prompt-toolkit==3.0.5 32 | Pygments==2.6.1 33 | pyparsing==2.4.7 34 | python-dateutil==2.8.1 35 | pytz==2020.1 36 | pywin32==228 37 | pyzmq==19.0.1 38 | query-string==0.0.0 39 | regex==2020.6.8 40 | request==0.0.0 41 | requests==2.24.0 42 | scikit-learn==0.23.1 43 | scipy==1.5.0 44 | seaborn==0.10.1 45 | six==1.15.0 46 | sklearn==0.0 47 | spyder-kernels==1.9.1 48 | threadpoolctl==2.1.0 49 | tornado==6.0.4 50 | tqdm==4.47.0 51 | traitlets==4.3.3 52 | urllib3==1.25.9 53 | wcwidth==0.2.5 54 | Werkzeug==1.0.1 55 | wincertstore==0.2 56 | gunicorn 57 | -------------------------------------------------------------------------------- /0.1 Car_Price_Prediction/static/css/register.css: -------------------------------------------------------------------------------- 1 | @import url(https://fonts.googleapis.com/css?family=Poppins); 2 | body { 3 | font-family: "Poppins", sans-serif; 4 | } 5 | .light-blue-gradient { 6 | background: linear-gradient(120deg, #a1c4fd 0%, #c2e9fb 100%); 7 | } -------------------------------------------------------------------------------- /0.1 Car_Price_Prediction/templates/cccc.png: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/GauravRajwada/100-Data-Science-Projects/ff69c5ea24308eaaa8356e066bfacdf42d11cfcc/0.1 Car_Price_Prediction/templates/cccc.png -------------------------------------------------------------------------------- /0.1 Car_Price_Prediction/templates/index.html: 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Check your resell car value in few second.

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102 | 103 | 104 | 105 | 106 | 107 | 108 | 109 | 110 | 111 | -------------------------------------------------------------------------------- /2.0 Sentiment analysis/Procfile: -------------------------------------------------------------------------------- 1 | web: gunicorn app:app 2 | -------------------------------------------------------------------------------- /2.0 Sentiment analysis/app.py: -------------------------------------------------------------------------------- 1 | 2 | 3 | from flask import Flask, render_template, request 4 | import pickle 5 | import re 6 | from nltk.stem import WordNetLemmatizer 7 | from nltk.corpus import stopwords 8 | import os 9 | import nltk 10 | import seaborn as sb 11 | import pandas as pd 12 | import sys 13 | import logging 14 | # nltk.download("wordnet", "whatever_the_absolute_path_to_myapp_is/nltk_data/") 15 | classifier=pickle.load(open("sentiment_model1.pkl","rb")) 16 | cv=pickle.load(open("sentiment_vectorizer.pkl","rb")) 17 | 18 | 19 | lm=WordNetLemmatizer() 20 | 21 | emotions={0:"Angry",1:"Sad",2:'Fear',3:"Surprise",4:"Joy",5:"Love"} 22 | 23 | app=Flask(__name__) 24 | 25 | app.logger.addHandler(logging.StreamHandler(sys.stdout)) 26 | app.logger.setLevel(logging.ERROR) 27 | app.config['SEND_FILE_MAX_AGE_DEFAULT'] = 0 28 | @app.route("/",methods=['GET']) 29 | 30 | def Home(): 31 | return render_template('index.html') 32 | @app.after_request 33 | def after_request(response): 34 | response.headers["Cache-Control"] = "no-cache, no-store, must-revalidate, public, max-age=0" 35 | response.headers["Expires"] = '0' 36 | response.headers["Pragma"] = "no-cache" 37 | return response 38 | 39 | @app.route("/predict",methods=['POST']) 40 | def predict(): 41 | 42 | if request.method=='POST': 43 | review=request.form['review'] 44 | if review.isnumeric(): 45 | return render_template('index.html',prediction_texts="Sorry you cannot sell this car") 46 | 47 | else: 48 | corpus=[] 49 | # review="i hate you you are vary bad" 50 | text=re.sub('[^a-zA-Z]'," ",review) 51 | text_lower=text.lower() 52 | lower_list=text_lower.split() 53 | print(lower_list) 54 | lower_list=[lm.lemmatize(i) for i in lower_list if i not in set(stopwords.words('english'))] 55 | clean_text=" ".join(lower_list) 56 | corpus.append(clean_text) 57 | x=cv.transform(corpus).toarray() 58 | output=classifier.predict(x) 59 | output=emotions[output[0]] 60 | 61 | string=""" 62 | Natural language process take following steps:\n 63 | 64 | 1. Removing special charecter: "{}" \n 65 | 2. Lowering all words: "{}"\n 66 | 3. Lemmatization- It usually refers to remove inflectional endings only 67 | and to return the base: "{}"\n 68 | 4. Making Corpus: "{}"\n 69 | 5. Making Bag of words: "{}"\n 70 | 6. Finally prediction: "{}"\n 71 | """.format(text,text_lower,clean_text,corpus,x,output) 72 | print(string) 73 | prob=pd.DataFrame({'Emotions': ['Angry',"Sad","Fear","Surprise","Joy","Love"], 'Probability': classifier.predict_proba(x)[0,:]}) 74 | prob=prob.sort_values(by='Probability',ascending=False) 75 | print(prob) 76 | import matplotlib 77 | matplotlib.use('Agg') 78 | import matplotlib.pyplot as plt 79 | fig = plt.figure(figsize =(10, 7)) 80 | 81 | plt.bar(prob['Emotions'], prob['Probability']) 82 | plt.savefig("static/people_photo/plot.png") 83 | 84 | path="static/people_photo/plot.png" 85 | 86 | return render_template('result.html',review=review, 87 | text=text, 88 | text_lower=text_lower, 89 | lower_list=lower_list, 90 | clean_text=clean_text, 91 | corpus=corpus, 92 | vector=x, 93 | output=output,ax=path 94 | ) 95 | else: 96 | return render_template('index.html') 97 | 98 | 99 | 100 | if __name__=="__main__": 101 | app.run() 102 | 103 | 104 | 105 | 106 | 107 | 108 | 109 | 110 | -------------------------------------------------------------------------------- /2.0 Sentiment analysis/app.yaml: -------------------------------------------------------------------------------- 1 | runtime:python37 -------------------------------------------------------------------------------- /2.0 Sentiment analysis/main.py: -------------------------------------------------------------------------------- 1 | # -*- coding: utf-8 -*- 2 | """ 3 | Created on Sun Jul 12 08:56:34 2020 4 | 5 | @author: Gaurav 6 | """ 7 | 8 | from flask import Flask, render_template, request 9 | import pickle 10 | import re 11 | from nltk.stem import WordNetLemmatizer 12 | from nltk.corpus import stopwords 13 | import os 14 | import nltk 15 | import seaborn as sb 16 | import pandas as pd 17 | # nltk.download("wordnet", "whatever_the_absolute_path_to_myapp_is/nltk_data/") 18 | classifier=pickle.load(open("sentiment_model1.pkl","rb")) 19 | cv=pickle.load(open("sentiment_vectorizer.pkl","rb")) 20 | 21 | 22 | lm=WordNetLemmatizer() 23 | 24 | emotions={0:"Angry",1:"Sad",2:'Fear',3:"Surprise",4:"Joy",5:"Love"} 25 | 26 | app=Flask(__name__) 27 | app.config['SEND_FILE_MAX_AGE_DEFAULT'] = 0 28 | @app.route("/",methods=['GET']) 29 | 30 | def Home(): 31 | return render_template('index.html') 32 | @app.after_request 33 | def after_request(response): 34 | response.headers["Cache-Control"] = "no-cache, no-store, must-revalidate, public, max-age=0" 35 | response.headers["Expires"] = '0' 36 | response.headers["Pragma"] = "no-cache" 37 | return response 38 | 39 | @app.route("/predict",methods=['POST']) 40 | def predict(): 41 | 42 | if request.method=='POST': 43 | review=request.form['review'] 44 | if review.isnumeric(): 45 | return render_template('index.html',prediction_texts="Sorry you cannot sell this car") 46 | 47 | else: 48 | corpus=[] 49 | # review="i hate you you are vary bad" 50 | text=re.sub('[^a-zA-Z]'," ",review) 51 | text_lower=text.lower() 52 | lower_list=text_lower.split() 53 | lower_list=[lm.lemmatize(i) for i in lower_list if i not in set(stopwords.words('english'))] 54 | clean_text=" ".join(lower_list) 55 | corpus.append(clean_text) 56 | x=cv.transform(corpus).toarray() 57 | output=classifier.predict(x) 58 | output=emotions[output[0]] 59 | string=""" 60 | Natural language process take following steps:\n 61 | 62 | 1. Removing special charecter: "{}" \n 63 | 2. Lowering all words: "{}"\n 64 | 3. Lemmatization- It usually refers to remove inflectional endings only 65 | and to return the base: "{}"\n 66 | 4. Making Corpus: "{}"\n 67 | 5. Making Bag of words: "{}"\n 68 | 6. Finally prediction: "{}"\n 69 | """.format(text,text_lower,clean_text,corpus,x,output) 70 | print(string) 71 | prob=pd.DataFrame({'Emotions': ['Angry',"Sad","Fear","Surprise","Joy","Love"], 'Probability': classifier.predict_proba(x)[0,:]}) 72 | prob=prob.sort_values(by='Probability',ascending=False) 73 | import matplotlib 74 | matplotlib.use('Agg') 75 | import matplotlib.pyplot as plt 76 | fig = plt.figure(figsize =(10, 7)) 77 | 78 | plt.bar(prob['Emotions'], prob['Probability']) 79 | plt.savefig("static/people_photo/plot.png") 80 | 81 | path="static/people_photo/plot.png" 82 | 83 | return render_template('result.html',review=review, 84 | text=text, 85 | text_lower=text_lower, 86 | lower_list=lower_list, 87 | clean_text=clean_text, 88 | corpus=corpus, 89 | vector=x, 90 | output=output,ax=path 91 | ) 92 | else: 93 | return render_template('index.html') 94 | 95 | 96 | 97 | if __name__=="__main__": 98 | app.run() 99 | 100 | 101 | 102 | 103 | 104 | 105 | 106 | 107 | -------------------------------------------------------------------------------- /2.0 Sentiment analysis/requirements.txt: -------------------------------------------------------------------------------- 1 | backcall==0.2.0 2 | certifi==2020.6.20 3 | click==7.1.2 4 | cloudpickle==1.5.0 5 | colorama==0.4.3 6 | cycler==0.10.0 7 | decorator==4.4.2 8 | Flask==1.1.2 9 | get==0.0.0 10 | gunicorn==19.9.0 11 | ipykernel==5.3.0 12 | ipython==7.16.1 13 | ipython-genutils==0.2.0 14 | itsdangerous==1.1.0 15 | jedi==0.17.1 16 | Jinja2==2.11.2 17 | joblib==0.16.0 18 | jsonify==0.5 19 | jupyter-client==6.1.5 20 | jupyter-core==4.6.3 21 | kiwisolver==1.2.0 22 | MarkupSafe==1.1.1 23 | matplotlib==3.2.2 24 | nltk==3.5 25 | numpy==1.19.0 26 | pandas==1.0.5 27 | parso==0.7.0 28 | pickleshare==0.7.5 29 | post==0.0.0 30 | prompt-toolkit==3.0.5 31 | Pygments==2.6.1 32 | pyparsing==2.4.7 33 | python-dateutil==2.8.1 34 | pytz==2020.1 35 | pywin32==228 36 | pyzmq==19.0.1 37 | query-string==0.0.0 38 | regex==2020.6.8 39 | request==0.0.0 40 | scikit-learn==0.23.1 41 | scipy==1.5.0 42 | seaborn==0.10.1 43 | six==1.15.0 44 | sklearn==0.0 45 | spyder-kernels==1.9.1 46 | threadpoolctl==2.1.0 47 | tornado==6.0.4 48 | tqdm==4.47.0 49 | traitlets==4.3.3 50 | wcwidth==0.2.5 51 | Werkzeug==1.0.1 52 | wincertstore==0.2 53 | gunicorn -------------------------------------------------------------------------------- /2.0 Sentiment analysis/sentiment_model1.pkl: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/GauravRajwada/100-Data-Science-Projects/ff69c5ea24308eaaa8356e066bfacdf42d11cfcc/2.0 Sentiment analysis/sentiment_model1.pkl -------------------------------------------------------------------------------- /2.0 Sentiment analysis/sentiment_vectorizer.pkl: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/GauravRajwada/100-Data-Science-Projects/ff69c5ea24308eaaa8356e066bfacdf42d11cfcc/2.0 Sentiment analysis/sentiment_vectorizer.pkl -------------------------------------------------------------------------------- /2.0 Sentiment analysis/static/people_photo/plot.png: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/GauravRajwada/100-Data-Science-Projects/ff69c5ea24308eaaa8356e066bfacdf42d11cfcc/2.0 Sentiment analysis/static/people_photo/plot.png -------------------------------------------------------------------------------- /2.0 Sentiment analysis/static/register.css: -------------------------------------------------------------------------------- 1 | /* devanagari */ 2 | @font-face { 3 | font-family: 'Poppins'; 4 | font-style: normal; 5 | font-weight: 400; 6 | src: local('Poppins Regular'), local('Poppins-Regular'), url(https://fonts.gstatic.com/s/poppins/v9/pxiEyp8kv8JHgFVrJJbecmNE.woff2) format('woff2'); 7 | unicode-range: U+0900-097F, U+1CD0-1CF6, U+1CF8-1CF9, U+200C-200D, U+20A8, U+20B9, U+25CC, U+A830-A839, U+A8E0-A8FB; 8 | } 9 | /* latin-ext */ 10 | @font-face { 11 | font-family: 'Poppins'; 12 | font-style: normal; 13 | font-weight: 400; 14 | src: local('Poppins Regular'), local('Poppins-Regular'), url(https://fonts.gstatic.com/s/poppins/v9/pxiEyp8kv8JHgFVrJJnecmNE.woff2) format('woff2'); 15 | unicode-range: U+0100-024F, U+0259, U+1E00-1EFF, U+2020, U+20A0-20AB, U+20AD-20CF, U+2113, U+2C60-2C7F, U+A720-A7FF; 16 | } 17 | /* latin */ 18 | @font-face { 19 | font-family: 'Poppins'; 20 | font-style: normal; 21 | font-weight: 400; 22 | src: local('Poppins Regular'), local('Poppins-Regular'), url(https://fonts.gstatic.com/s/poppins/v9/pxiEyp8kv8JHgFVrJJfecg.woff2) format('woff2'); 23 | unicode-range: U+0000-00FF, U+0131, U+0152-0153, U+02BB-02BC, U+02C6, U+02DA, U+02DC, U+2000-206F, U+2074, U+20AC, U+2122, U+2191, U+2193, U+2212, U+2215, U+FEFF, U+FFFD; 24 | } 25 | body { 26 | font-family: "Poppins", sans-serif; 27 | } 28 | .light-blue-gradient { 29 | background: linear-gradient(120deg, #a1c4fd 0%, #1ba1df 100%); 30 | } -------------------------------------------------------------------------------- /2.0 Sentiment analysis/templates/index.html: -------------------------------------------------------------------------------- 1 | 2 | 3 | 4 | 5 | 6 | 7 | 8 | 9 | 10 | 11 | 12 | 13 | N L P 14 | 28 | 29 | 30 | 31 |
32 |
33 |

Natural Language Processing

34 | Welcome to my NLP project. Here you can enter any sentence and 35 | it will tell what is the sentiment of that text. 36 | You can to learn some basic of natural language processing.

37 |
38 |
39 |
40 |
41 |

Give Input

42 |
43 |
44 |
45 | 46 | 47 | 48 | 49 |
50 | 51 |

52 | 53 | 54 |
55 |
56 |
57 |
58 |
59 |
60 |
Created by Gaurav Singh
61 |
62 | 63 | 64 | 65 | 66 | 67 |
68 | 69 | 70 | -------------------------------------------------------------------------------- /2.0 Sentiment analysis/templates/nlp.jpg: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/GauravRajwada/100-Data-Science-Projects/ff69c5ea24308eaaa8356e066bfacdf42d11cfcc/2.0 Sentiment analysis/templates/nlp.jpg -------------------------------------------------------------------------------- /2.0 Sentiment analysis/templates/result.html: -------------------------------------------------------------------------------- 1 | 2 | 3 | 4 | 5 | 6 | 7 | 8 | 9 | 10 | 11 | 12 | 13 | 14 | N L P 15 | 29 | 30 | 31 |
32 | 33 |
34 |
35 | 36 |
37 |
38 |
39 | Natural language process take following steps:
40 |
    41 |
  1. Your Sentence:
    {{review}}
    2. 42 |
  2. Removing special charecter:
    {{text}}
    3. 43 |
  3. Lowering all words:
    {{text_lower}}
    4. 44 |
  4. Lemmatization
    45 | It usually refers to remove inflectional endings only 46 | and to return the base:
    {{clean_text}}
    5. 47 |
  5. Making Corpus:
    {{corpus}}
    6. 48 |
  6. Making Bag of words:
    {{x}}
    7. 49 |
  7. Finally prediction:
    {{output}}
    8. 50 |
51 |
52 | 53 | 54 |
55 | Note- The results may not be 100% true. 56 | 57 |
58 |
59 |
60 |
61 |
62 |
63 |
64 |
Created by Gaurav Singh
65 |
66 | 67 | 68 | 69 | 70 | 71 | 72 |
73 | 74 | 75 | -------------------------------------------------------------------------------- /3. Breast Cancer Wisconsin (Diagnostic)/Breast-Cancer.sav: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/GauravRajwada/100-Data-Science-Projects/ff69c5ea24308eaaa8356e066bfacdf42d11cfcc/3. Breast Cancer Wisconsin (Diagnostic)/Breast-Cancer.sav -------------------------------------------------------------------------------- /3. Breast Cancer Wisconsin (Diagnostic)/Explanation.docx: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/GauravRajwada/100-Data-Science-Projects/ff69c5ea24308eaaa8356e066bfacdf42d11cfcc/3. Breast Cancer Wisconsin (Diagnostic)/Explanation.docx -------------------------------------------------------------------------------- /4. Diabete/Explanation.docx: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/GauravRajwada/100-Data-Science-Projects/ff69c5ea24308eaaa8356e066bfacdf42d11cfcc/4. Diabete/Explanation.docx -------------------------------------------------------------------------------- /4. Diabete/diabetes.csv: -------------------------------------------------------------------------------- 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 -------------------------------------------------------------------------------- /4. Diabete/diabetes_model.sav: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/GauravRajwada/100-Data-Science-Projects/ff69c5ea24308eaaa8356e066bfacdf42d11cfcc/4. Diabete/diabetes_model.sav -------------------------------------------------------------------------------- /5. Indian Liver Patient/Explanation.docx: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/GauravRajwada/100-Data-Science-Projects/ff69c5ea24308eaaa8356e066bfacdf42d11cfcc/5. Indian Liver Patient/Explanation.docx -------------------------------------------------------------------------------- /5. Indian Liver Patient/Liver_disease.sav: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/GauravRajwada/100-Data-Science-Projects/ff69c5ea24308eaaa8356e066bfacdf42d11cfcc/5. Indian Liver Patient/Liver_disease.sav -------------------------------------------------------------------------------- /5. Indian Liver Patient/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 | -------------------------------------------------------------------------------- /6. Chronic KIdney Disease/Chronic-KIdney-Disease-dataset.csv: -------------------------------------------------------------------------------- 1 | id,age,bp,sg,al,su,rbc,pc,pcc,ba,bgr,bu,sc,sod,pot,hemo,pcv,wc,rc,htn,dm,cad,appet,pe,ane,classification 2 | 0,48.0,80.0,1.02,1.0,0.0,,normal,notpresent,notpresent,121.0,36.0,1.2,,,15.4,44,7800,5.2,yes,yes,no,good,no,no,ckd 3 | 1,7.0,50.0,1.02,4.0,0.0,,normal,notpresent,notpresent,,18.0,0.8,,,11.3,38,6000,,no,no,no,good,no,no,ckd 4 | 2,62.0,80.0,1.01,2.0,3.0,normal,normal,notpresent,notpresent,423.0,53.0,1.8,,,9.6,31,7500,,no,yes,no,poor,no,yes,ckd 5 | 3,48.0,70.0,1.005,4.0,0.0,normal,abnormal,present,notpresent,117.0,56.0,3.8,111.0,2.5,11.2,32,6700,3.9,yes,no,no,poor,yes,yes,ckd 6 | 4,51.0,80.0,1.01,2.0,0.0,normal,normal,notpresent,notpresent,106.0,26.0,1.4,,,11.6,35,7300,4.6,no,no,no,good,no,no,ckd 7 | 5,60.0,90.0,1.015,3.0,0.0,,,notpresent,notpresent,74.0,25.0,1.1,142.0,3.2,12.2,39,7800,4.4,yes,yes,no,good,yes,no,ckd 8 | 6,68.0,70.0,1.01,0.0,0.0,,normal,notpresent,notpresent,100.0,54.0,24.0,104.0,4.0,12.4,36,,,no,no,no,good,no,no,ckd 9 | 7,24.0,,1.015,2.0,4.0,normal,abnormal,notpresent,notpresent,410.0,31.0,1.1,,,12.4,44,6900,5,no,yes,no,good,yes,no,ckd 10 | 8,52.0,100.0,1.015,3.0,0.0,normal,abnormal,present,notpresent,138.0,60.0,1.9,,,10.8,33,9600,4.0,yes,yes,no,good,no,yes,ckd 11 | 9,53.0,90.0,1.02,2.0,0.0,abnormal,abnormal,present,notpresent,70.0,107.0,7.2,114.0,3.7,9.5,29,12100,3.7,yes,yes,no,poor,no,yes,ckd 12 | 10,50.0,60.0,1.01,2.0,4.0,,abnormal,present,notpresent,490.0,55.0,4.0,,,9.4,28,,,yes,yes,no,good,no,yes,ckd 13 | 11,63.0,70.0,1.01,3.0,0.0,abnormal,abnormal,present,notpresent,380.0,60.0,2.7,131.0,4.2,10.8,32,4500,3.8,yes,yes,no,poor,yes,no,ckd 14 | 12,68.0,70.0,1.015,3.0,1.0,,normal,present,notpresent,208.0,72.0,2.1,138.0,5.8,9.7,28,12200,3.4,yes,yes,yes,poor,yes,no,ckd 15 | 13,68.0,70.0,,,,,,notpresent,notpresent,98.0,86.0,4.6,135.0,3.4,9.8,,,,yes,yes,yes,poor,yes,no,ckd 16 | 14,68.0,80.0,1.01,3.0,2.0,normal,abnormal,present,present,157.0,90.0,4.1,130.0,6.4,5.6,16,11000,2.6,yes,yes,yes,poor,yes,no,ckd 17 | 15,40.0,80.0,1.015,3.0,0.0,,normal,notpresent,notpresent,76.0,162.0,9.6,141.0,4.9,7.6,24,3800,2.8,yes,no,no,good,no,yes,ckd 18 | 16,47.0,70.0,1.015,2.0,0.0,,normal,notpresent,notpresent,99.0,46.0,2.2,138.0,4.1,12.6,,,,no,no,no,good,no,no,ckd 19 | 17,47.0,80.0,,,,,,notpresent,notpresent,114.0,87.0,5.2,139.0,3.7,12.1,,,,yes,no,no,poor,no,no,ckd 20 | 18,60.0,100.0,1.025,0.0,3.0,,normal,notpresent,notpresent,263.0,27.0,1.3,135.0,4.3,12.7,37,11400,4.3,yes,yes,yes,good,no,no,ckd 21 | 19,62.0,60.0,1.015,1.0,0.0,,abnormal,present,notpresent,100.0,31.0,1.6,,,10.3,30,5300,3.7,yes,no,yes,good,no,no,ckd 22 | 20,61.0,80.0,1.015,2.0,0.0,abnormal,abnormal,notpresent,notpresent,173.0,148.0,3.9,135.0,5.2,7.7,24,9200,3.2,yes,yes,yes,poor,yes,yes,ckd 23 | 21,60.0,90.0,,,,,,notpresent,notpresent,,180.0,76.0,4.5,,10.9,32,6200,3.6,yes,yes,yes,good,no,no,ckd 24 | 22,48.0,80.0,1.025,4.0,0.0,normal,abnormal,notpresent,notpresent,95.0,163.0,7.7,136.0,3.8,9.8,32,6900,3.4,yes,no,no,good,no,yes,ckd 25 | 23,21.0,70.0,1.01,0.0,0.0,,normal,notpresent,notpresent,,,,,,,,,,no,no,no,poor,no,yes,ckd 26 | 24,42.0,100.0,1.015,4.0,0.0,normal,abnormal,notpresent,present,,50.0,1.4,129.0,4.0,11.1,39,8300,4.6,yes,no,no,poor,no,no,ckd 27 | 25,61.0,60.0,1.025,0.0,0.0,,normal,notpresent,notpresent,108.0,75.0,1.9,141.0,5.2,9.9,29,8400,3.7,yes,yes,no,good,no,yes,ckd 28 | 26,75.0,80.0,1.015,0.0,0.0,,normal,notpresent,notpresent,156.0,45.0,2.4,140.0,3.4,11.6,35,10300,4,yes,yes,no,poor,no,no,ckd 29 | 27,69.0,70.0,1.01,3.0,4.0,normal,abnormal,notpresent,notpresent,264.0,87.0,2.7,130.0,4.0,12.5,37,9600,4.1,yes,yes,yes,good,yes,no,ckd 30 | 28,75.0,70.0,,1.0,3.0,,,notpresent,notpresent,123.0,31.0,1.4,,,,,,,no,yes,no,good,no,no,ckd 31 | 29,68.0,70.0,1.005,1.0,0.0,abnormal,abnormal,present,notpresent,,28.0,1.4,,,12.9,38,,,no,no,yes,good,no,no,ckd 32 | 30,,70.0,,,,,,notpresent,notpresent,93.0,155.0,7.3,132.0,4.9,,,,,yes, yes,no,good,no,no,ckd 33 | 31,73.0,90.0,1.015,3.0,0.0,,abnormal,present,notpresent,107.0,33.0,1.5,141.0,4.6,10.1,30,7800,4,no,no,no,poor,no,no,ckd 34 | 32,61.0,90.0,1.01,1.0,1.0,,normal,notpresent,notpresent,159.0,39.0,1.5,133.0,4.9,11.3,34,9600,4.0,yes,yes,no,poor,no,no,ckd 35 | 33,60.0,100.0,1.02,2.0,0.0,abnormal,abnormal,notpresent,notpresent,140.0,55.0,2.5,,,10.1,29,,,yes,no,no,poor,no,no,ckd 36 | 34,70.0,70.0,1.01,1.0,0.0,normal,,present,present,171.0,153.0,5.2,,,,,,,no,yes,no,poor,no,no,ckd 37 | 35,65.0,90.0,1.02,2.0,1.0,abnormal,normal,notpresent,notpresent,270.0,39.0,2.0,,,12.0,36,9800,4.9,yes,yes,no,poor,no,yes,ckd 38 | 36,76.0,70.0,1.015,1.0,0.0,normal,normal,notpresent,notpresent,92.0,29.0,1.8,133.0,3.9,10.3,32,,,yes,no,no,good,no,no,ckd 39 | 37,72.0,80.0,,,,,,notpresent,notpresent,137.0,65.0,3.4,141.0,4.7,9.7,28,6900,2.5,yes,yes,no,poor,no,yes,ckd 40 | 38,69.0,80.0,1.02,3.0,0.0,abnormal,normal,notpresent,notpresent,,103.0,4.1,132.0,5.9,12.5,,,,yes,no,no,good,no,no,ckd 41 | 39,82.0,80.0,1.01,2.0,2.0,normal,,notpresent,notpresent,140.0,70.0,3.4,136.0,4.2,13.0,40,9800,4.2,yes,yes,no,good,no,no,ckd 42 | 40,46.0,90.0,1.01,2.0,0.0,normal,abnormal,notpresent,notpresent,99.0,80.0,2.1,,,11.1,32,9100,4.1,yes,no, no,good,no,no,ckd 43 | 41,45.0,70.0,1.01,0.0,0.0,,normal,notpresent,notpresent,,20.0,0.7,,,,,,,no,no,no,good,yes,no,ckd 44 | 42,47.0,100.0,1.01,0.0,0.0,,normal,notpresent,notpresent,204.0,29.0,1.0,139.0,4.2,9.7,33,9200,4.5,yes,no,no,good,no,yes,ckd 45 | 43,35.0,80.0,1.01,1.0,0.0,abnormal,,notpresent,notpresent,79.0,202.0,10.8,134.0,3.4,7.9,24,7900,3.1,no,yes,no,good,no,no,ckd 46 | 44,54.0,80.0,1.01,3.0,0.0,abnormal,abnormal,notpresent,notpresent,207.0,77.0,6.3,134.0,4.8,9.7,28,,,yes,yes,no,poor,yes,no,ckd 47 | 45,54.0,80.0,1.02,3.0,0.0,,abnormal,notpresent,notpresent,208.0,89.0,5.9,130.0,4.9,9.3,,,,yes,yes,no,poor,yes,no,ckd 48 | 46,48.0,70.0,1.015,0.0,0.0,,normal,notpresent,notpresent,124.0,24.0,1.2,142.0,4.2,12.4,37,6400,4.7,no,yes,no,good,no,no,ckd 49 | 47,11.0,80.0,1.01,3.0,0.0,,normal,notpresent,notpresent,,17.0,0.8,,,15.0,45,8600,,no,no,no,good,no,no,ckd 50 | 48,73.0,70.0,1.005,0.0,0.0,normal,normal,notpresent,notpresent,70.0,32.0,0.9,125.0,4.0,10.0,29,18900,3.5,yes,yes,no,good,yes,no,ckd 51 | 49,60.0,70.0,1.01,2.0,0.0,normal,abnormal,present,notpresent,144.0,72.0,3.0,,,9.7,29,21600,3.5,yes,yes,no,poor,no,yes,ckd 52 | 50,53.0,60.0,,,,,,notpresent,notpresent,91.0,114.0,3.25,142.0,4.3,8.6,28,11000,3.8,yes,yes,no,poor,yes,yes,ckd 53 | 51,54.0,100.0,1.015,3.0,0.0,,normal,present,notpresent,162.0,66.0,1.6,136.0,4.4,10.3,33,,,yes,yes,no,poor,yes,no,ckd 54 | 52,53.0,90.0,1.015,0.0,0.0,,normal,notpresent,notpresent,,38.0,2.2,,,10.9,34,4300,3.7,no,no,no,poor,no,yes,ckd 55 | 53,62.0,80.0,1.015,0.0,5.0,,,notpresent,notpresent,246.0,24.0,1.0,,,13.6,40,8500,4.7,yes,yes,no,good,no,no,ckd 56 | 54,63.0,80.0,1.01,2.0,2.0,normal,,notpresent,notpresent,,,3.4,136.0,4.2,13.0,40,9800,4.2,yes,no,yes,good,no,no,ckd 57 | 55,35.0,80.0,1.005,3.0,0.0,abnormal,normal,notpresent,notpresent,,,,,,9.5,28,,,no,no,no,good,yes,no,ckd 58 | 56,76.0,70.0,1.015,3.0,4.0,normal,abnormal,present,notpresent,,164.0,9.7,131.0,4.4,10.2,30,11300,3.4,yes,yes,yes,poor,yes,no,ckd 59 | 57,76.0,90.0,,,,,normal,notpresent,notpresent,93.0,155.0,7.3,132.0,4.9,,,,,yes,yes,yes,poor,no,no,ckd 60 | 58,73.0,80.0,1.02,2.0,0.0,abnormal,abnormal,notpresent,notpresent,253.0,142.0,4.6,138.0,5.8,10.5,33,7200,4.3,yes,yes,yes,good,no,no,ckd 61 | 59,59.0,100.0,,,,,,notpresent,notpresent,,96.0,6.4,,,6.6,,,,yes,yes,no,good,no,yes,ckd 62 | 60,67.0,90.0,1.02,1.0,0.0,,abnormal,present,notpresent,141.0,66.0,3.2,138.0,6.6,,,,,yes,no,no,good,no,no,ckd 63 | 61,67.0,80.0,1.01,1.0,3.0,normal,abnormal,notpresent,notpresent,182.0,391.0,32.0,163.0,39.0,,,,,no,no,no,good,yes,no,ckd 64 | 62,15.0,60.0,1.02,3.0,0.0,,normal,notpresent,notpresent,86.0,15.0,0.6,138.0,4.0,11.0,33,7700,3.8,yes,yes,no,good,no,no,ckd 65 | 63,46.0,70.0,1.015,1.0,0.0,abnormal,normal,notpresent,notpresent,150.0,111.0,6.1,131.0,3.7,7.5,27,,,no,no,no,good,no,yes,ckd 66 | 64,55.0,80.0,1.01,0.0,0.0,,normal,notpresent,notpresent,146.0,,,,,9.8,,,,no,no, no,good,no,no,ckd 67 | 65,44.0,90.0,1.01,1.0,0.0,,normal,notpresent,notpresent,,20.0,1.1,,,15.0,48,,,no, no,no,good,no,no,ckd 68 | 66,67.0,70.0,1.02,2.0,0.0,abnormal,normal,notpresent,notpresent,150.0,55.0,1.6,131.0,4.8,, ?,,,yes,yes,no,good,yes,no,ckd 69 | 67,45.0,80.0,1.02,3.0,0.0,normal,abnormal,notpresent,notpresent,425.0,,,,,,,,,no,no,no,poor,no,no,ckd 70 | 68,65.0,70.0,1.01,2.0,0.0,,normal,present,notpresent,112.0,73.0,3.3,,,10.9,37,,,no,no,no,good,no,no,ckd 71 | 69,26.0,70.0,1.015,0.0,4.0,,normal,notpresent,notpresent,250.0,20.0,1.1,,,15.6,52,6900,6.0,no,yes,no,good,no,no,ckd 72 | 70,61.0,80.0,1.015,0.0,4.0,,normal,notpresent,notpresent,360.0,19.0,0.7,137.0,4.4,15.2,44,8300,5.2,yes,yes,no,good,no,no,ckd 73 | 71,46.0,60.0,1.01,1.0,0.0,normal,normal,notpresent,notpresent,163.0,92.0,3.3,141.0,4.0,9.8,28,14600,3.2,yes,yes,no,good,no,no,ckd 74 | 72,64.0,90.0,1.01,3.0,3.0,,abnormal,present,notpresent,,35.0,1.3,,,10.3,,,,yes,yes,no,good,yes,no,ckd 75 | 73,,100.0,1.015,2.0,0.0,abnormal,abnormal,notpresent,notpresent,129.0,107.0,6.7,132.0,4.4,4.8,14,6300,,yes,no,no,good,yes,yes,ckd 76 | 74,56.0,90.0,1.015,2.0,0.0,abnormal,abnormal,notpresent,notpresent,129.0,107.0,6.7,131.0,4.8,9.1,29,6400,3.4,yes,no,no,good,no,no,ckd 77 | 75,5.0,,1.015,1.0,0.0,,normal,notpresent,notpresent,,16.0,0.7,138.0,3.2,8.1,,,,no,no,no,good,no,yes,ckd 78 | 76,48.0,80.0,1.005,4.0,0.0,abnormal,abnormal,notpresent,present,133.0,139.0,8.5,132.0,5.5,10.3,36, 6200,4,no,yes,no,good,yes,no,ckd 79 | 77,67.0,70.0,1.01,1.0,0.0,,normal,notpresent,notpresent,102.0,48.0,3.2,137.0,5.0,11.9,34,7100,3.7,yes,yes,no,good,yes,no,ckd 80 | 78,70.0,80.0,,,,,,notpresent,notpresent,158.0,85.0,3.2,141.0,3.5,10.1,30,,,yes,no,no,good,yes,no,ckd 81 | 79,56.0,80.0,1.01,1.0,0.0,,normal,notpresent,notpresent,165.0,55.0,1.8,,,13.5,40,11800,5.0,yes,yes,no,poor,yes,no,ckd 82 | 80,74.0,80.0,1.01,0.0,0.0,,normal,notpresent,notpresent,132.0,98.0,2.8,133.0,5.0,10.8,31,9400,3.8,yes,yes,no,good,no,no,ckd 83 | 81,45.0,90.0,,,,,,notpresent,notpresent,360.0,45.0,2.4,128.0,4.4,8.3,29,5500,3.7,yes,yes,no,good,no,no,ckd 84 | 82,38.0,70.0,,,,,,notpresent,notpresent,104.0,77.0,1.9,140.0,3.9,,,,,yes,no,no,poor,yes,no,ckd 85 | 83,48.0,70.0,1.015,1.0,0.0,normal,normal,notpresent,notpresent,127.0,19.0,1.0,134.0,3.6,,,,,yes,yes,no,good,no,no,ckd 86 | 84,59.0,70.0,1.01,3.0,0.0,normal,abnormal,notpresent,notpresent,76.0,186.0,15.0,135.0,7.6,7.1,22,3800,2.1,yes,no,no,poor,yes,yes,ckd 87 | 85,70.0,70.0,1.015,2.0,,,,notpresent,notpresent,,46.0,1.5,,,9.9,,,,no,yes,no,poor,yes,no,ckd 88 | 86,56.0,80.0,,,,,,notpresent,notpresent,415.0,37.0,1.9,,,,,,,no,yes,no,good,no,no,ckd 89 | 87,70.0,100.0,1.005,1.0,0.0,normal,abnormal,present,notpresent,169.0,47.0,2.9,,,11.1,32,5800,5,yes,yes,no,poor,no,no,ckd 90 | 88,58.0,110.0,1.01,4.0,0.0,,normal,notpresent,notpresent,251.0,52.0,2.2,,,,,13200,4.7,yes, yes,no,good,no,no,ckd 91 | 89,50.0,70.0,1.02,0.0,0.0,,normal,notpresent,notpresent,109.0,32.0,1.4,139.0,4.7,,,,,no,no,no,poor,no,no,ckd 92 | 90,63.0,100.0,1.01,2.0,2.0,normal,normal,notpresent,present,280.0,35.0,3.2,143.0,3.5,13.0,40,9800,4.2,yes,no,yes,good,no,no,ckd 93 | 91,56.0,70.0,1.015,4.0,1.0,abnormal,normal,notpresent,notpresent,210.0,26.0,1.7,136.0,3.8,16.1,52,12500,5.6,no,no,no,good,no,no,ckd 94 | 92,71.0,70.0,1.01,3.0,0.0,normal,abnormal,present,present,219.0,82.0,3.6,133.0,4.4,10.4,33,5600,3.6,yes,yes,yes,good,no,no,ckd 95 | 93,73.0,100.0,1.01,3.0,2.0,abnormal,abnormal,present,notpresent,295.0,90.0,5.6,140.0,2.9,9.2,30,7000,3.2,yes,yes,yes,poor,no,no,ckd 96 | 94,65.0,70.0,1.01,0.0,0.0,,normal,notpresent,notpresent,93.0,66.0,1.6,137.0,4.5,11.6,36,11900,3.9,no,yes,no,good,no,no,ckd 97 | 95,62.0,90.0,1.015,1.0,0.0,,normal,notpresent,notpresent,94.0,25.0,1.1,131.0,3.7,,,,,yes,no,no,good,yes,yes,ckd 98 | 96,60.0,80.0,1.01,1.0,1.0,,normal,notpresent,notpresent,172.0,32.0,2.7,,,11.2,36,,,no,yes,yes,poor,no,no,ckd 99 | 97,65.0,60.0,1.015,1.0,0.0,,normal,notpresent,notpresent,91.0,51.0,2.2,132.0,3.8,10.0,32,9100,4.0,yes,yes,no,poor,yes,no,ckd 100 | 98,50.0,140.0,,,,,,notpresent,notpresent,101.0,106.0,6.5,135.0,4.3,6.2,18,5800,2.3,yes,yes,no,poor,no,yes,ckd 101 | 99,56.0,180.0,,0.0,4.0,,abnormal,notpresent,notpresent,298.0,24.0,1.2,139.0,3.9,11.2,32,10400,4.2,yes,yes,no,poor,yes,no,ckd 102 | 100,34.0,70.0,1.015,4.0,0.0,abnormal,abnormal,notpresent,notpresent,153.0,22.0,0.9,133.0,3.8,,,,,no,no,no,good,yes,no,ckd 103 | 101,71.0,90.0,1.015,2.0,0.0,,abnormal,present,present,88.0,80.0,4.4,139.0,5.7,11.3,33,10700,3.9,no,no,no,good,no,no,ckd 104 | 102,17.0,60.0,1.01,0.0,0.0,,normal,notpresent,notpresent,92.0,32.0,2.1,141.0,4.2,13.9,52,7000,,no,no,no,good,no,no,ckd 105 | 103,76.0,70.0,1.015,2.0,0.0,normal,abnormal,present,notpresent,226.0,217.0,10.2,,,10.2,36,12700,4.2,yes,no,no,poor,yes,yes,ckd 106 | 104,55.0,90.0,,,,,,notpresent,notpresent,143.0,88.0,2.0,,,,,,,yes,yes,no,poor,yes,no,ckd 107 | 105,65.0,80.0,1.015,0.0,0.0,,normal,notpresent,notpresent,115.0,32.0,11.5,139.0,4.0,14.1,42,6800,5.2,no,no,no,good,no,no,ckd 108 | 106,50.0,90.0,,,,,,notpresent,notpresent,89.0,118.0,6.1,127.0,4.4,6.0,17,6500,,yes,yes,no,good,yes,yes,ckd 109 | 107,55.0,100.0,1.015,1.0,4.0,normal,,notpresent,notpresent,297.0,53.0,2.8,139.0,4.5,11.2,34,13600,4.4,yes,yes,no,good,no,no,ckd 110 | 108,45.0,80.0,1.015,0.0,0.0,,abnormal,notpresent,notpresent,107.0,15.0,1.0,141.0,4.2,11.8,37,10200,4.2,no,no,no,good,no,no,ckd 111 | 109,54.0,70.0,,,,,,notpresent,notpresent,233.0,50.1,1.9,,,11.7,,,,no,yes,no,good,no,no,ckd 112 | 110,63.0,90.0,1.015,0.0,0.0,,normal,notpresent,notpresent,123.0,19.0,2.0,142.0,3.8,11.7,34,11400,4.7,no,no,no,good,no,no,ckd 113 | 111,65.0,80.0,1.01,3.0,3.0,,normal,notpresent,notpresent,294.0,71.0,4.4,128.0,5.4,10.0,32,9000,3.9,yes,yes,yes,good,no,no,ckd 114 | 112,,60.0,1.015,3.0,0.0,abnormal,abnormal,notpresent,notpresent,,34.0,1.2,,,10.8,33,,,no,no,no,good,no,no,ckd 115 | 113,61.0,90.0,1.015,0.0,2.0,,normal,notpresent,notpresent,,,,,,,,9800,,no,yes,no,poor,no,yes,ckd 116 | 114,12.0,60.0,1.015,3.0,0.0,abnormal,abnormal,present,notpresent,,51.0,1.8,,,12.1,,10300,,no,no,no,good,no,no,ckd 117 | 115,47.0,80.0,1.01,0.0,0.0,,abnormal,notpresent,notpresent,,28.0,0.9,,,12.4,44,5600,4.3,no,no,no,good,no,yes,ckd 118 | 116,,70.0,1.015,4.0,0.0,abnormal,normal,notpresent,notpresent,104.0,16.0,0.5,,,,,,,no,no,no,good,yes,no,ckd 119 | 117,,70.0,1.02,0.0,0.0,,,notpresent,notpresent,219.0,36.0,1.3,139.0,3.7,12.5,37,9800,4.4,no,no,no,good,no,no,ckd 120 | 118,55.0,70.0,1.01,3.0,0.0,,normal,notpresent,notpresent,99.0,25.0,1.2,,,11.4,,,,no,no,no,poor,yes,no,ckd 121 | 119,60.0,70.0,1.01,0.0,0.0,,normal,notpresent,notpresent,140.0,27.0,1.2,,,,,,,no,no,no,good,no,no,ckd 122 | 120,72.0,90.0,1.025,1.0,3.0,,normal,notpresent,notpresent,323.0,40.0,2.2,137.0,5.3,12.6,,,,no,yes,yes,poor,no,no,ckd 123 | 121,54.0,60.0,,3.0,,,,notpresent,notpresent,125.0,21.0,1.3,137.0,3.4,15.0,46,,,yes,yes,no,good,yes,no,ckd 124 | 122,34.0,70.0,,,,,,notpresent,notpresent,,219.0,12.2,130.0,3.8,6.0,,,,yes,no,no,good,no,yes,ckd 125 | 123,43.0,80.0,1.015,2.0,3.0,,abnormal,present,present,,30.0,1.1,,,14.0,42,14900,,no,no,no,good,no,no,ckd 126 | 124,65.0,100.0,1.015,0.0,0.0,,normal,notpresent,notpresent,90.0,98.0,2.5,,,9.1,28,5500,3.6,yes,no,no,good,no,no,ckd 127 | 125,72.0,90.0,,,,,,notpresent,notpresent,308.0,36.0,2.5,131.0,4.3,,,,,yes,yes,no,poor,no,no,ckd 128 | 126,70.0,90.0,1.015,0.0,0.0,,normal,notpresent,notpresent,144.0,125.0,4.0,136.0,4.6,12.0,37,8200,4.5,yes,yes,no,poor,yes,no,ckd 129 | 127,71.0,60.0,1.015,4.0,0.0,normal,normal,notpresent,notpresent,118.0,125.0,5.3,136.0,4.9,11.4,35,15200,4.3,yes,yes,no,poor,yes,no,ckd 130 | 128,52.0,90.0,1.015,4.0,3.0,normal,abnormal,notpresent,notpresent,224.0,166.0,5.6,133.0,47.0,8.1,23,5000,2.9,yes,yes,no,good,no,yes,ckd 131 | 129,75.0,70.0,1.025,1.0,0.0,,normal,notpresent,notpresent,158.0,49.0,1.4,135.0,4.7,11.1,,,,yes,no,no,poor,yes,no,ckd 132 | 130,50.0,90.0,1.01,2.0,0.0,normal,abnormal,present,present,128.0,208.0,9.2,134.0,4.8,8.2,22,16300,2.7,no,no,no,poor,yes,yes,ckd 133 | 131,5.0,50.0,1.01,0.0,0.0,,normal,notpresent,notpresent,,25.0,0.6,,,11.8,36,12400,,no,no,no,good,no,no,ckd 134 | 132,50.0,,,,,normal,,notpresent,notpresent,219.0,176.0,13.8,136.0,4.5,8.6,24,13200,2.7,yes,no,no,good,yes,yes,ckd 135 | 133,70.0,100.0,1.015,4.0,0.0,normal,normal,notpresent,notpresent,118.0,125.0,5.3,136.0,4.9,12.0,37, 8400,8.0,yes,no,no,good,no,no,ckd 136 | 134,47.0,100.0,1.01,,,normal,,notpresent,notpresent,122.0,,16.9,138.0,5.2,10.8,33,10200,3.8,no,yes,no,good,no,no,ckd 137 | 135,48.0,80.0,1.015,0.0,2.0,,normal,notpresent,notpresent,214.0,24.0,1.3,140.0,4.0,13.2,39,,,no,yes,no,poor,no,no,ckd 138 | 136,46.0,90.0,1.02,,,,normal,notpresent,notpresent,213.0,68.0,2.8,146.0,6.3,9.3,,,,yes,yes,no,good,no,no,ckd 139 | 137,45.0,60.0,1.01,2.0,0.0,normal,abnormal,present,notpresent,268.0,86.0,4.0,134.0,5.1,10.0,29,9200,,yes,yes,no,good,no,no,ckd 140 | 138,73.0,,1.01,1.0,0.0,,,notpresent,notpresent,95.0,51.0,1.6,142.0,3.5,,,,,no, no,no,good,no,no,ckd 141 | 139,41.0,70.0,1.015,2.0,0.0,,abnormal,notpresent,present,,68.0,2.8,132.0,4.1,11.1,33,,,yes,no,no,good,yes,yes,ckd 142 | 140,69.0,70.0,1.01,0.0,4.0,,normal,notpresent,notpresent,256.0,40.0,1.2,142.0,5.6,,,,,no,no,no,good,no,no,ckd 143 | 141,67.0,70.0,1.01,1.0,0.0,normal,normal,notpresent,notpresent,,106.0,6.0,137.0,4.9,6.1,19,6500,,yes,no,no,good,no,yes,ckd 144 | 142,72.0,90.0,,,,,,notpresent,notpresent,84.0,145.0,7.1,135.0,5.3,,,,,no,yes,no,good,no,no,ckd 145 | 143,41.0,80.0,1.015,1.0,4.0,abnormal,normal,notpresent,notpresent,210.0,165.0,18.0,135.0,4.7,,,,,no,yes,no,good,no,no,ckd 146 | 144,60.0,90.0,1.01,2.0,0.0,abnormal,normal,notpresent,notpresent,105.0,53.0,2.3,136.0,5.2,11.1,33,10500,4.1,no,no,no,good,no,no,ckd 147 | 145,57.0,90.0,1.015,5.0,0.0,abnormal,abnormal,notpresent,present,,322.0,13.0,126.0,4.8,8.0,24,4200,3.3,yes,yes,yes,poor,yes,yes,ckd 148 | 146,53.0,100.0,1.01,1.0,3.0,abnormal,normal,notpresent,notpresent,213.0,23.0,1.0,139.0,4.0,,,,,no,yes,no,good,no,no,ckd 149 | 147,60.0,60.0,1.01,3.0,1.0,normal,abnormal,present,notpresent,288.0,36.0,1.7,130.0,3.0,7.9,25,15200,3.0,yes,no,no,poor,no,yes,ckd 150 | 148,69.0,60.0,,,,,,notpresent,notpresent,171.0,26.0,48.1,,,,,,,yes,no,no,poor,no,no,ckd 151 | 149,65.0,70.0,1.02,1.0,0.0,abnormal,abnormal,notpresent,notpresent,139.0,29.0,1.0,,,10.5,32,,,yes,no,no,good,yes,no,ckd 152 | 150,8.0,60.0,1.025,3.0,0.0,normal,normal,notpresent,notpresent,78.0,27.0,0.9,,,12.3,41,6700,,no,no,no,poor,yes,no,ckd 153 | 151,76.0,90.0,,,,,,notpresent,notpresent,172.0,46.0,1.7,141.0,5.5,9.6,30,,,yes,yes,no,good,no,yes,ckd 154 | 152,39.0,70.0,1.01,0.0,0.0,,normal,notpresent,notpresent,121.0,20.0,0.8,133.0,3.5,10.9,32,,,no,yes,no,good,no,no,ckd 155 | 153,55.0,90.0,1.01,2.0,1.0,abnormal,abnormal,notpresent,notpresent,273.0,235.0,14.2,132.0,3.4,8.3,22,14600,2.9,yes,yes,no,poor,yes,yes,ckd 156 | 154,56.0,90.0,1.005,4.0,3.0,abnormal,abnormal,notpresent,notpresent,242.0,132.0,16.4,140.0,4.2,8.4,26,,3,yes,yes,no,poor,yes,yes,ckd 157 | 155,50.0,70.0,1.02,3.0,0.0,abnormal,normal,present,present,123.0,40.0,1.8,,,11.1,36,4700,,no,no,no,good,no,no,ckd 158 | 156,66.0,90.0,1.015,2.0,0.0,,normal,notpresent,present,153.0,76.0,3.3,,,,,,,no,no,no,poor,no,no,ckd 159 | 157,62.0,70.0,1.025,3.0,0.0,normal,abnormal,notpresent,notpresent,122.0,42.0,1.7,136.0,4.7,12.6,39,7900,3.9,yes,yes,no,good,no,no,ckd 160 | 158,71.0,60.0,1.02,3.0,2.0,normal,normal,present,notpresent,424.0,48.0,1.5,132.0,4.0,10.9,31,,,yes,yes,yes,good,no,no,ckd 161 | 159,59.0,80.0,1.01,1.0,0.0,abnormal,normal,notpresent,notpresent,303.0,35.0,1.3,122.0,3.5,10.4,35,10900,4.3,no,yes,no,poor,no,no,ckd 162 | 160,81.0,60.0,,,,,,notpresent,notpresent,148.0,39.0,2.1,147.0,4.2,10.9,35,9400,2.4,yes,yes,yes,poor,yes,no,ckd 163 | 161,62.0,,1.015,3.0,0.0,abnormal,,notpresent,notpresent,,,,,,14.3,42,10200,4.8,yes,yes,no,good,no,no,ckd 164 | 162,59.0,70.0,,,,,,notpresent,notpresent,204.0,34.0,1.5,124.0,4.1,9.8,37,6000, ?,no,yes,no,good,no,no,ckd 165 | 163,46.0,80.0,1.01,0.0,0.0,,normal,notpresent,notpresent,160.0,40.0,2.0,140.0,4.1,9.0,27,8100,3.2,yes,no,no,poor,no,yes,ckd 166 | 164,14.0,,1.015,0.0,0.0,,,notpresent,notpresent,192.0,15.0,0.8,137.0,4.2,14.3,40,9500,5.4,no,yes,no,poor,yes,no,ckd 167 | 165,60.0,80.0,1.02,0.0,2.0,,,notpresent,notpresent,,,,,,,,,,no,yes,no,good,no,no,ckd 168 | 166,27.0,60.0,,,,,,notpresent,notpresent,76.0,44.0,3.9,127.0,4.3,,,,,no,no,no,poor,yes,yes,ckd 169 | 167,34.0,70.0,1.02,0.0,0.0,abnormal,normal,notpresent,notpresent,139.0,19.0,0.9,,,12.7,42,2200,,no,no,no,poor,no,no,ckd 170 | 168,65.0,70.0,1.015,4.0,4.0,,normal,present,notpresent,307.0,28.0,1.5,,,11.0,39,6700,,yes,yes,no,good,no,no,ckd 171 | 169,,70.0,1.01,0.0,2.0,,normal,notpresent,notpresent,220.0,68.0,2.8,,,8.7,27,,,yes,yes,no,good,no,yes,ckd 172 | 170,66.0,70.0,1.015,2.0,5.0,,normal,notpresent,notpresent,447.0,41.0,1.7,131.0,3.9,12.5,33,9600,4.4,yes,yes,no,good,no,no,ckd 173 | 171,83.0,70.0,1.02,3.0,0.0,normal,normal,notpresent,notpresent,102.0,60.0,2.6,115.0,5.7,8.7,26,12800,3.1,yes,no,no,poor,no,yes,ckd 174 | 172,62.0,80.0,1.01,1.0,2.0,,,notpresent,notpresent,309.0,113.0,2.9,130.0,2.5,10.6,34,12800,4.9,no,no,no,good,no,no,ckd 175 | 173,17.0,70.0,1.015,1.0,0.0,abnormal,normal,notpresent,notpresent,22.0,1.5,7.3,145.0,2.8,13.1,41,11200,,no,no,no,good,no,no,ckd 176 | 174,54.0,70.0,,,,,,notpresent,notpresent,111.0,146.0,7.5,141.0,4.7,11.0,35,8600,4.6,no,no,no,good,no,no,ckd 177 | 175,60.0,50.0,1.01,0.0,0.0,,normal,notpresent,notpresent,261.0,58.0,2.2,113.0,3.0,,,4200,3.4,yes,no,no,good,no,no,ckd 178 | 176,21.0,90.0,1.01,4.0,0.0,normal,abnormal,present,present,107.0,40.0,1.7,125.0,3.5,8.3,23,12400,3.9,no,no,no,good,no,yes,ckd 179 | 177,65.0,80.0,1.015,2.0,1.0,normal,normal,present,notpresent,215.0,133.0,2.5,,,13.2,41,,,no,yes,no,good,no,no,ckd 180 | 178,42.0,90.0,1.02,2.0,0.0,abnormal,abnormal,present,notpresent,93.0,153.0,2.7,139.0,4.3,9.8,34,9800,,no,no,no,poor,yes,yes,ckd 181 | 179,72.0,90.0,1.01,2.0,0.0,,abnormal,present,notpresent,124.0,53.0,2.3,,,11.9,39,,,no,no,no,good,no,no,ckd 182 | 180,73.0,90.0,1.01,1.0,4.0,abnormal,abnormal,present,notpresent,234.0,56.0,1.9,,,10.3,28,,,no,yes,no,good,no,no,ckd 183 | 181,45.0,70.0,1.025,2.0,0.0,normal,abnormal,present,notpresent,117.0,52.0,2.2,136.0,3.8,10.0,30,19100,3.7,no,no,no,good,no,no,ckd 184 | 182,61.0,80.0,1.02,0.0,0.0,,normal,notpresent,notpresent,131.0,23.0,0.8,140.0,4.1,11.3,35,,,no,no,no,good,no,no,ckd 185 | 183,30.0,70.0,1.015,0.0,0.0,,normal,notpresent,notpresent,101.0,106.0,6.5,135.0,4.3,,,,,no,no,no,poor,no,no,ckd 186 | 184,54.0,60.0,1.015,3.0,2.0,,abnormal,notpresent,notpresent,352.0,137.0,3.3,133.0,4.5,11.3,31,5800,3.6,yes,yes,yes,poor,yes,no,ckd 187 | 185,4.0,,1.02,1.0,0.0,,normal,notpresent,notpresent,99.0,23.0,0.6,138.0,4.4,12.0,34, ?,,no,no,no,good,no,no,ckd 188 | 186,8.0,50.0,1.02,4.0,0.0,normal,normal,notpresent,notpresent,,46.0,1.0,135.0,3.8,,,,,no,no,no,good,yes,no,ckd 189 | 187,3.0,,1.01,2.0,0.0,normal,normal,notpresent,notpresent,,22.0,0.7,,,10.7,34,12300,,no,no,no,good,no,no,ckd 190 | 188,8.0,,,,,,,notpresent,notpresent,80.0,66.0,2.5,142.0,3.6,12.2,38,,,no, no,no,good,no,no,ckd 191 | 189,64.0,60.0,1.01,4.0,1.0,abnormal,abnormal,notpresent,present,239.0,58.0,4.3,137.0,5.4,9.5,29,7500,3.4,yes,yes,no,poor,yes,no,ckd 192 | 190,6.0,60.0,1.01,4.0,0.0,abnormal,abnormal,notpresent,present,94.0,67.0,1.0,135.0,4.9,9.9,30,16700,4.8,no,no,no,poor,no,no,ckd 193 | 191,,70.0,1.01,3.0,0.0,normal,normal,notpresent,notpresent,110.0,115.0,6.0,134.0,2.7,9.1,26,9200,3.4,yes,yes,no,poor,no,no,ckd 194 | 192,46.0,110.0,1.015,0.0,0.0,,normal,notpresent,notpresent,130.0,16.0,0.9,,,,,,,no,no,no,good,no,no,ckd 195 | 193,32.0,90.0,1.025,1.0,0.0,abnormal,abnormal,notpresent,notpresent,,223.0,18.1,113.0,6.5,5.5,15,2600,2.8,yes,yes,no,poor,yes,yes,ckd 196 | 194,80.0,70.0,1.01,2.0,,,abnormal,notpresent,notpresent,,49.0,1.2,,,,,,,yes, yes,no,good,no,no,ckd 197 | 195,70.0,90.0,1.02,2.0,1.0,abnormal,abnormal,notpresent,present,184.0,98.6,3.3,138.0,3.9,5.8,,,,yes,yes,yes,poor,no,no,ckd 198 | 196,49.0,100.0,1.01,3.0,0.0,abnormal,abnormal,notpresent,notpresent,129.0,158.0,11.8,122.0,3.2,8.1,24,9600,3.5,yes,yes,no,poor,yes,yes,ckd 199 | 197,57.0,80.0,,,,,,notpresent,notpresent,,111.0,9.3,124.0,5.3,6.8,,4300,3.0,yes,yes,no,good,no,yes,ckd 200 | 198,59.0,100.0,1.02,4.0,2.0,normal,normal,notpresent,notpresent,252.0,40.0,3.2,137.0,4.7,11.2,30,26400,3.9,yes,yes,no,poor,yes,no,ckd 201 | 199,65.0,80.0,1.015,0.0,0.0,,normal,notpresent,notpresent,92.0,37.0,1.5,140.0,5.2,8.8,25,10700,3.2,yes,no,yes,good,yes,no,ckd 202 | 200,90.0,90.0,1.025,1.0,0.0,,normal,notpresent,notpresent,139.0,89.0,3.0,140.0,4.1,12.0,37,7900,3.9,yes,yes,no,good,no,no,ckd 203 | 201,64.0,70.0,,,,,,notpresent,notpresent,113.0,94.0,7.3,137.0,4.3,7.9,21,,,yes,yes,yes,good,yes,yes,ckd 204 | 202,78.0,60.0,,,,,,notpresent,notpresent,114.0,74.0,2.9,135.0,5.9,8.0,24,,,no,yes,no,good,no,yes,ckd 205 | 203,,90.0,,,,,,notpresent,notpresent,207.0,80.0,6.8,142.0,5.5,8.5,,,,yes,yes,no,good,no,yes,ckd 206 | 204,65.0,90.0,1.01,4.0,2.0,normal,normal,notpresent,notpresent,172.0,82.0,13.5,145.0,6.3,8.8,31,,,yes,yes,no,good,yes,yes,ckd 207 | 205,61.0,70.0,,,,,,notpresent,notpresent,100.0,28.0,2.1,,,12.6,43,,,yes,yes,no,good,no,no,ckd 208 | 206,60.0,70.0,1.01,1.0,0.0,,normal,notpresent,notpresent,109.0,96.0,3.9,135.0,4.0,13.8,41,,,yes,no,no,good,no,no,ckd 209 | 207,50.0,70.0,1.01,0.0,0.0,,normal,notpresent,notpresent,230.0,50.0,2.2,,,12.0,41,10400,4.6,yes,yes,no,good,no,no,ckd 210 | 208,67.0,80.0,,,,,,notpresent,notpresent,341.0,37.0,1.5,,,12.3,41,6900,4.9,yes,yes,no,good,no,yes,ckd 211 | 209,19.0,70.0,1.02,0.0,0.0,,normal,notpresent,notpresent,,,,,,11.5,,6900,,no,no,no,good,no,no,ckd 212 | 210,59.0,100.0,1.015,4.0,2.0,normal,normal,notpresent,notpresent,255.0,132.0,12.8,135.0,5.7,7.3,20,9800,3.9,yes,yes,yes,good,no,yes,ckd 213 | 211,54.0,120.0,1.015,0.0,0.0,,normal,notpresent,notpresent,103.0,18.0,1.2,,,,,,,no,no,no,good,no,no,ckd 214 | 212,40.0,70.0,1.015,3.0,4.0,normal,normal,notpresent,notpresent,253.0,150.0,11.9,132.0,5.6,10.9,31,8800,3.4,yes,yes,no,poor,yes,no,ckd 215 | 213,55.0,80.0,1.01,3.0,1.0,normal,abnormal,present,present,214.0,73.0,3.9,137.0,4.9,10.9,34,7400,3.7,yes,yes,no,good,yes,no,ckd 216 | 214,68.0,80.0,1.015,0.0,0.0,,abnormal,notpresent,notpresent,171.0,30.0,1.0,,,13.7, 43,4900,5.2,no,yes,no,good,no,no,ckd 217 | 215,2.0,,1.01,3.0,0.0,normal,abnormal,notpresent,notpresent,,,,,,,,,,no,no,no,good,yes,no,ckd 218 | 216,64.0,70.0,1.01,0.0,0.0,,normal,notpresent,notpresent,107.0,15.0,,,,12.8,38,,,no,no,no,good,no,no,ckd 219 | 217,63.0,100.0,1.01,1.0,0.0,,normal,notpresent,notpresent,78.0,61.0,1.8,141.0,4.4,12.2,36,10500,4.3,no,yes,no,good,no,no,ckd 220 | 218,33.0,90.0,1.015,0.0,0.0,,normal,notpresent,notpresent,92.0,19.0,0.8,,,11.8,34,7000,,no,no,no,good,no,no,ckd 221 | 219,68.0,90.0,1.01,0.0,0.0,,normal,notpresent,notpresent,238.0,57.0,2.5,,,9.8,28,8000,3.3,yes,yes,no,poor,no,no,ckd 222 | 220,36.0,80.0,1.01,0.0,0.0,,normal,notpresent,notpresent,103.0,,,,,11.9,36,8800,,no,no,no,good,no,no,ckd 223 | 221,66.0,70.0,1.02,1.0,0.0,normal,,notpresent,notpresent,248.0,30.0,1.7,138.0,5.3,,,,,yes,yes,no,good,no,no,ckd 224 | 222,74.0,60.0,,,,,,notpresent,notpresent,108.0,68.0,1.8,,,,,,,yes,yes,no,good,no,no,ckd 225 | 223,71.0,90.0,1.01,0.0,3.0,,normal,notpresent,notpresent,303.0,30.0,1.3,136.0,4.1,13.0,38,9200,4.6,yes,yes,no,good,no,no,ckd 226 | 224,34.0,60.0,1.02,0.0,0.0,,normal,notpresent,notpresent,117.0,28.0,2.2,138.0,3.8,,,,,no,no,no,good,yes,no,ckd 227 | 225,60.0,90.0,1.01,3.0,5.0,abnormal,normal,notpresent,present,490.0,95.0,2.7,131.0,3.8,11.5,35,12000,4.5,yes,yes,no,good,no,no,ckd 228 | 226,64.0,100.0,1.015,4.0,2.0,abnormal,abnormal,notpresent,present,163.0,54.0,7.2,140.0,4.6,7.9,26,7500,3.4,yes,yes,no,good,yes,no,ckd 229 | 227,57.0,80.0,1.015,0.0,0.0,,normal,notpresent,notpresent,120.0,48.0,1.6,,,11.3,36,7200,3.8,yes,yes,no,good,no,no,ckd 230 | 228,60.0,70.0,,,,,,notpresent,notpresent,124.0,52.0,2.5,,,,,,,yes,no,no,good,no,no,ckd 231 | 229,59.0,50.0,1.01,3.0,0.0,normal,abnormal,notpresent,notpresent,241.0,191.0,12.0,114.0,2.9,9.6,31,15700,3.8,no,yes,no,good,yes,no,ckd 232 | 230,65.0,60.0,1.01,2.0,0.0,normal,abnormal,present,notpresent,192.0,17.0,1.7,130.0,4.3,,,9500,,yes,yes,no,poor,no,no,ckd 233 | 231,60.0,90.0,,,,,,notpresent,notpresent,269.0,51.0,2.8,138.0,3.7,11.5,35,,,yes,yes,yes,good,yes,no,ckd 234 | 232,50.0,90.0,1.015,1.0,0.0,abnormal,abnormal,notpresent,notpresent,,,,,,,,,,no,no,no,good,yes,no,ckd 235 | 233,51.0,100.0,1.015,2.0,0.0,normal,normal,notpresent,present,93.0,20.0,1.6,146.0,4.5,,,,,no,no,no,poor,no,no,ckd 236 | 234,37.0,100.0,1.01,0.0,0.0,abnormal,normal,notpresent,notpresent,,19.0,1.3,,,15.0,44,4100,5.2,yes,no,no,good,no,no,ckd 237 | 235,45.0,70.0,1.01,2.0,0.0,,normal,notpresent,notpresent,113.0,93.0,2.3,,,7.9,26,5700,,no,no,yes,good,no,yes,ckd 238 | 236,65.0,80.0,,,,,,notpresent,notpresent,74.0,66.0,2.0,136.0,5.4,9.1,25,,,yes,yes,yes,good,yes,no,ckd 239 | 237,80.0,70.0,1.015,2.0,2.0,,normal,notpresent,notpresent,141.0,53.0,2.2,,,12.7,40,9600,,yes,yes,no,poor,yes,no,ckd 240 | 238,72.0,100.0,,,,,,notpresent,notpresent,201.0,241.0,13.4,127.0,4.8,9.4,28,,,yes,yes,no,good,no,yes,ckd 241 | 239,34.0,90.0,1.015,2.0,0.0,normal,normal,notpresent,notpresent,104.0,50.0,1.6,137.0,4.1,11.9,39,,,no,no,no,good,no,no,ckd 242 | 240,65.0,70.0,1.015,1.0,0.0,,normal,notpresent,notpresent,203.0,46.0,1.4,,,11.4,36,5000,4.1,yes,yes,no,poor,yes,no,ckd 243 | 241,57.0,70.0,1.015,1.0,0.0,,abnormal,notpresent,notpresent,165.0,45.0,1.5,140.0,3.3,10.4,31,4200,3.9,no,no,no,good,no,no,ckd 244 | 242,69.0,70.0,1.01,4.0,3.0,normal,abnormal,present,present,214.0,96.0,6.3,120.0,3.9,9.4,28,11500,3.3,yes,yes,yes,good,yes,yes,ckd 245 | 243,62.0,90.0,1.02,2.0,1.0,,normal,notpresent,notpresent,169.0,48.0,2.4,138.0,2.9,13.4,47,11000,6.1,yes,no,no,good,no,no,ckd 246 | 244,64.0,90.0,1.015,3.0,2.0,,abnormal,present,notpresent,463.0,64.0,2.8,135.0,4.1,12.2,40,9800,4.6,yes,yes,no,good,no,yes,ckd 247 | 245,48.0,100.0,,,,,,notpresent,notpresent,103.0,79.0,5.3,135.0,6.3,6.3,19,7200,2.6,yes,no,yes,poor,no,no,ckd 248 | 246,48.0,110.0,1.015,3.0,0.0,abnormal,normal,present,notpresent,106.0,215.0,15.2,120.0,5.7,8.6,26,5000,2.5,yes,no,yes,good,no,yes,ckd 249 | 247,54.0,90.0,1.025,1.0,0.0,normal,abnormal,notpresent,notpresent,150.0,18.0,1.2,140.0,4.2,,,,,no,no,no,poor,yes,yes,ckd 250 | 248,59.0,70.0,1.01,1.0,3.0,abnormal,abnormal,notpresent,notpresent,424.0,55.0,1.7,138.0,4.5,12.6,37,10200,4.1,yes,yes,yes,good,no,no,ckd 251 | 249,56.0,90.0,1.01,4.0,1.0,normal,abnormal,present,notpresent,176.0,309.0,13.3,124.0,6.5,3.1,9,5400,2.1,yes,yes,no,poor,yes,yes,ckd 252 | 250,40.0,80.0,1.025,0.0,0.0,normal,normal,notpresent,notpresent,140.0,10.0,1.2,135.0,5.0,15.0,48,10400,4.5,no,no,no,good,no,no,notckd 253 | 251,23.0,80.0,1.025,0.0,0.0,normal,normal,notpresent,notpresent,70.0,36.0,1.0,150.0,4.6,17.0,52,9800,5.0,no,no,no,good,no,no,notckd 254 | 252,45.0,80.0,1.025,0.0,0.0,normal,normal,notpresent,notpresent,82.0,49.0,0.6,147.0,4.4,15.9,46,9100,4.7,no,no,no,good,no,no,notckd 255 | 253,57.0,80.0,1.025,0.0,0.0,normal,normal,notpresent,notpresent,119.0,17.0,1.2,135.0,4.7,15.4,42,6200,6.2,no,no,no,good,no,no,notckd 256 | 254,51.0,60.0,1.025,0.0,0.0,normal,normal,notpresent,notpresent,99.0,38.0,0.8,135.0,3.7,13.0,49,8300,5.2,no,no,no,good,no,no,notckd 257 | 255,34.0,80.0,1.025,0.0,0.0,normal,normal,notpresent,notpresent,121.0,27.0,1.2,144.0,3.9,13.6,52,9200,6.3,no,no,no,good,no,no,notckd 258 | 256,60.0,80.0,1.025,0.0,0.0,normal,normal,notpresent,notpresent,131.0,10.0,0.5,146.0,5.0,14.5,41,10700,5.1,no,no,no,good,no,no,notckd 259 | 257,38.0,60.0,1.02,0.0,0.0,normal,normal,notpresent,notpresent,91.0,36.0,0.7,135.0,3.7,14.0,46,9100,5.8,no,no,no,good,no,no,notckd 260 | 258,42.0,80.0,1.02,0.0,0.0,normal,normal,notpresent,notpresent,98.0,20.0,0.5,140.0,3.5,13.9,44,8400,5.5,no,no,no,good,no,no,notckd 261 | 259,35.0,80.0,1.02,0.0,0.0,normal,normal,notpresent,notpresent,104.0,31.0,1.2,135.0,5.0,16.1,45,4300,5.2,no,no,no,good,no,no,notckd 262 | 260,30.0,80.0,1.02,0.0,0.0,normal,normal,notpresent,notpresent,131.0,38.0,1.0,147.0,3.8,14.1,45,9400,5.3,no,no,no,good,no,no,notckd 263 | 261,49.0,80.0,1.02,0.0,0.0,normal,normal,notpresent,notpresent,122.0,32.0,1.2,139.0,3.9,17.0,41,5600,4.9,no,no,no,good,no,no,notckd 264 | 262,55.0,80.0,1.02,0.0,0.0,normal,normal,notpresent,notpresent,118.0,18.0,0.9,135.0,3.6,15.5,43,7200,5.4,no,no,no,good,no,no,notckd 265 | 263,45.0,80.0,1.02,0.0,0.0,normal,normal,notpresent,notpresent,117.0,46.0,1.2,137.0,5.0,16.2,45,8600,5.2,no,no,no,good,no,no,notckd 266 | 264,42.0,80.0,1.02,0.0,0.0,normal,normal,notpresent,notpresent,132.0,24.0,0.7,140.0,4.1,14.4,50,5000,4.5,no,no,no,good,no,no,notckd 267 | 265,50.0,80.0,1.02,0.0,0.0,normal,normal,notpresent,notpresent,97.0,40.0,0.6,150.0,4.5,14.2,48,10500,5.0,no,no,no,good,no,no,notckd 268 | 266,55.0,80.0,1.02,0.0,0.0,normal,normal,notpresent,notpresent,133.0,17.0,1.2,135.0,4.8,13.2,41,6800,5.3,no,no,no,good,no,no,notckd 269 | 267,48.0,80.0,1.025,0.0,0.0,normal,normal,notpresent,notpresent,122.0,33.0,0.9,146.0,3.9,13.9,48,9500,4.8,no,no,no,good,no,no,notckd 270 | 268,,80.0,,,,,,notpresent,notpresent,100.0,49.0,1.0,140.0,5.0,16.3,53,8500,4.9,no,no,no,good,no,no,notckd 271 | 269,25.0,80.0,1.025,0.0,0.0,normal,normal,notpresent,notpresent,121.0,19.0,1.2,142.0,4.9,15.0,48,6900,5.3,no,no,no,good,no,no,notckd 272 | 270,23.0,80.0,1.025,0.0,0.0,normal,normal,notpresent,notpresent,111.0,34.0,1.1,145.0,4.0,14.3,41,7200,5.0,no,no,no,good,no,no,notckd 273 | 271,30.0,80.0,1.025,0.0,0.0,normal,normal,notpresent,notpresent,96.0,25.0,0.5,144.0,4.8,13.8,42,9000,4.5,no,no,no,good,no,no,notckd 274 | 272,56.0,80.0,1.025,0.0,0.0,normal,normal,notpresent,notpresent,139.0,15.0,1.2,135.0,5.0,14.8,42,5600,5.5,no,no,no,good,no,no,notckd 275 | 273,47.0,80.0,1.02,0.0,0.0,normal,normal,notpresent,notpresent,95.0,35.0,0.9,140.0,4.1,,,,,no,no,no,good,no,no,notckd 276 | 274,19.0,80.0,1.02,0.0,0.0,normal,normal,notpresent,notpresent,107.0,23.0,0.7,141.0,4.2,14.4,44,,,no,no,no,good,no,no,notckd 277 | 275,52.0,80.0,1.02,0.0,0.0,normal,normal,notpresent,notpresent,125.0,22.0,1.2,139.0,4.6,16.5,43,4700,4.6,no,no,no,good,no,no,notckd 278 | 276,20.0,60.0,1.025,0.0,0.0,normal,normal,notpresent,notpresent,,,,137.0,4.7,14.0,41,4500,5.5,no,no,no,good,no,no,notckd 279 | 277,46.0,60.0,1.025,0.0,0.0,normal,normal,notpresent,notpresent,123.0,46.0,1.0,135.0,5.0,15.7,50,6300,4.8,no,no,no,good,no,no,notckd 280 | 278,48.0,60.0,1.02,0.0,0.0,normal,normal,notpresent,notpresent,112.0,44.0,1.2,142.0,4.9,14.5,44,9400,6.4,no,no,no,good,no,no,notckd 281 | 279,24.0,70.0,1.025,0.0,0.0,normal,normal,notpresent,notpresent,140.0,23.0,0.6,140.0,4.7,16.3,48,5800,5.6,no,no,no,good,no,no,notckd 282 | 280,47.0,80.0,,,,,,notpresent,notpresent,93.0,33.0,0.9,144.0,4.5,13.3,52,8100,5.2,no,no,no,good,no,no,notckd 283 | 281,55.0,80.0,1.025,0.0,0.0,normal,normal,notpresent,notpresent,130.0,50.0,1.2,147.0,5.0,15.5,41,9100,6.0,no,no,no,good,no,no,notckd 284 | 282,20.0,70.0,1.02,0.0,0.0,normal,normal,notpresent,notpresent,123.0,44.0,1.0,135.0,3.8,14.6,44,5500,4.8,no,no,no,good,no,no,notckd 285 | 283,60.0,70.0,1.02,0.0,0.0,normal,normal,notpresent,notpresent,,,,,,16.4,43,10800,5.7,no,no,no,good,no,no,notckd 286 | 284,33.0,80.0,1.025,0.0,0.0,normal,normal,notpresent,notpresent,100.0,37.0,1.2,142.0,4.0,16.9,52,6700,6.0,no,no,no,good,no,no,notckd 287 | 285,66.0,70.0,1.02,0.0,0.0,normal,normal,notpresent,notpresent,94.0,19.0,0.7,135.0,3.9,16.0,41,5300,5.9,no,no,no,good,no,no,notckd 288 | 286,71.0,70.0,1.02,0.0,0.0,normal,normal,notpresent,notpresent,81.0,18.0,0.8,145.0,5.0,14.7,44,9800,6.0,no,no,no,good,no,no,notckd 289 | 287,39.0,70.0,1.025,0.0,0.0,normal,normal,notpresent,notpresent,124.0,22.0,0.6,137.0,3.8,13.4,43,,,no,no,no,good,no,no,notckd 290 | 288,56.0,70.0,1.025,0.0,0.0,normal,normal,notpresent,notpresent,70.0,46.0,1.2,135.0,4.9,15.9,50,11000,5.1,,,,good,no,no,notckd 291 | 289,42.0,70.0,1.02,0.0,0.0,normal,normal,notpresent,notpresent,93.0,32.0,0.9,143.0,4.7,16.6,43,7100,5.3,no,no,no,good,no,no,notckd 292 | 290,54.0,70.0,1.02,0.0,0.0,,,,,76.0,28.0,0.6,146.0,3.5,14.8,52,8400,5.9,no,no,no,good,no,no,notckd 293 | 291,47.0,80.0,1.025,0.0,0.0,normal,normal,notpresent,notpresent,124.0,44.0,1.0,140.0,4.9,14.9,41,7000,5.7,no,no,no,good,no,no,notckd 294 | 292,30.0,80.0,1.02,0.0,0.0,normal,normal,notpresent,notpresent,89.0,42.0,0.5,139.0,5.0,16.7,52,10200,5.0,no,no,no,good,no,no,notckd 295 | 293,50.0,,1.02,0.0,0.0,normal,normal,notpresent,notpresent,92.0,19.0,1.2,150.0,4.8,14.9,48,4700,5.4,no,no,no,good,no,no,notckd 296 | 294,75.0,60.0,1.02,0.0,0.0,normal,normal,notpresent,notpresent,110.0,50.0,0.7,135.0,5.0,14.3,40,8300,5.8,no,no,no,,,,notckd 297 | 295,44.0,70.0,,,,,,notpresent,notpresent,106.0,25.0,0.9,150.0,3.6,15.0,50,9600,6.5,no,no,no,good,no,no,notckd 298 | 296,41.0,70.0,1.02,0.0,0.0,normal,normal,notpresent,notpresent,125.0,38.0,0.6,140.0,5.0,16.8,41,6300,5.9,no,no,no,good,no,no,notckd 299 | 297,53.0,60.0,1.025,0.0,0.0,normal,normal,notpresent,notpresent,116.0,26.0,1.0,146.0,4.9,15.8,45,7700,5.2,,,,good,no,no,notckd 300 | 298,34.0,60.0,1.02,0.0,0.0,normal,normal,notpresent,notpresent,91.0,49.0,1.2,135.0,4.5,13.5,48,8600,4.9,no,no,no,good,no,no,notckd 301 | 299,73.0,60.0,1.02,0.0,0.0,normal,normal,notpresent,notpresent,127.0,48.0,0.5,150.0,3.5,15.1,52,11000,4.7,no,no,no,good,no,no,notckd 302 | 300,45.0,60.0,1.02,0.0,0.0,normal,normal,,,114.0,26.0,0.7,141.0,4.2,15.0,43,9200,5.8,no,no,no,good,no,no,notckd 303 | 301,44.0,60.0,1.025,0.0,0.0,normal,normal,notpresent,notpresent,96.0,33.0,0.9,147.0,4.5,16.9,41,7200,5.0,no,no,no,good,no,no,notckd 304 | 302,29.0,70.0,1.02,0.0,0.0,normal,normal,notpresent,notpresent,127.0,44.0,1.2,145.0,5.0,14.8,48,,,no,no,no,good,no,no,notckd 305 | 303,55.0,70.0,1.02,0.0,0.0,normal,normal,notpresent,notpresent,107.0,26.0,1.1,,,17.0,50,6700,6.1,no,no,no,good,no,no,notckd 306 | 304,33.0,80.0,1.025,0.0,0.0,normal,normal,notpresent,notpresent,128.0,38.0,0.6,135.0,3.9,13.1,45,6200,4.5,no,no,no,good,no,no,notckd 307 | 305,41.0,80.0,1.02,0.0,0.0,normal,normal,notpresent,notpresent,122.0,25.0,0.8,138.0,5.0,17.1,41,9100,5.2,no,no,no,good,no,no,notckd 308 | 306,52.0,80.0,1.02,0.0,0.0,normal,normal,notpresent,notpresent,128.0,30.0,1.2,140.0,4.5,15.2,52,4300,5.7,no,no,no,good,no,no,notckd 309 | 307,47.0,60.0,1.02,0.0,0.0,normal,normal,notpresent,notpresent,137.0,17.0,0.5,150.0,3.5,13.6,44,7900,4.5,no,no,no,good,no,no,notckd 310 | 308,43.0,80.0,1.025,0.0,0.0,normal,normal,notpresent,notpresent,81.0,46.0,0.6,135.0,4.9,13.9,48,6900,4.9,no,no,no,good,no,no,notckd 311 | 309,51.0,60.0,1.02,0.0,0.0,,,notpresent,notpresent,129.0,25.0,1.2,139.0,5.0,17.2,40,8100,5.9,no,no,no,good,no,no,notckd 312 | 310,46.0,60.0,1.02,0.0,0.0,normal,normal,notpresent,notpresent,102.0,27.0,0.7,142.0,4.9,13.2,44,11000,5.4,no,no,no,good,no,no,notckd 313 | 311,56.0,60.0,1.025,0.0,0.0,normal,normal,notpresent,notpresent,132.0,18.0,1.1,147.0,4.7,13.7,45,7500,5.6,no,no,no,good,no,no,notckd 314 | 312,80.0,70.0,1.02,0.0,0.0,normal,normal,notpresent,notpresent,,,,135.0,4.1,15.3,48,6300,6.1,no,no,no,good,no,no,notckd 315 | 313,55.0,80.0,1.02,0.0,0.0,normal,normal,notpresent,notpresent,104.0,28.0,0.9,142.0,4.8,17.3,52,8200,4.8,no,no,no,good,no,no,notckd 316 | 314,39.0,70.0,1.025,0.0,0.0,normal,normal,notpresent,notpresent,131.0,46.0,0.6,145.0,5.0,15.6,41,9400,4.7,no,no,no,good,no,no,notckd 317 | 315,44.0,70.0,1.025,0.0,0.0,normal,normal,notpresent,notpresent,,,,,,13.8,48,7800,4.4,no,no,no,good,no,no,notckd 318 | 316,35.0,,1.02,0.0,0.0,normal,normal,,,99.0,30.0,0.5,135.0,4.9,15.4,48,5000,5.2,no,no,no,good,no,no,notckd 319 | 317,58.0,70.0,1.02,0.0,0.0,normal,normal,notpresent,notpresent,102.0,48.0,1.2,139.0,4.3,15.0,40,8100,4.9,no,no,no,good,no,no,notckd 320 | 318,61.0,70.0,1.025,0.0,0.0,normal,normal,notpresent,notpresent,120.0,29.0,0.7,137.0,3.5,17.4,52,7000,5.3,no,no,no,good,no,no,notckd 321 | 319,30.0,60.0,1.02,0.0,0.0,normal,normal,notpresent,notpresent,138.0,15.0,1.1,135.0,4.4,,,,,no,no,no,good,no,no,notckd 322 | 320,57.0,60.0,1.02,0.0,0.0,normal,normal,notpresent,notpresent,105.0,49.0,1.2,150.0,4.7,15.7,44,10400,6.2,no,no,no,good,no,no,notckd 323 | 321,65.0,60.0,1.02,0.0,0.0,normal,normal,notpresent,notpresent,109.0,39.0,1.0,144.0,3.5,13.9,48,9600,4.8,no,no,no,good,no,no,notckd 324 | 322,70.0,60.0,,,,,,notpresent,notpresent,120.0,40.0,0.5,140.0,4.6,16.0,43,4500,4.9,no,no,no,good,no,no,notckd 325 | 323,43.0,80.0,1.025,0.0,0.0,normal,normal,notpresent,notpresent,130.0,30.0,1.1,143.0,5.0,15.9,45,7800,4.5,no,no,no,good,no,no,notckd 326 | 324,40.0,80.0,1.02,0.0,0.0,normal,normal,notpresent,notpresent,119.0,15.0,0.7,150.0,4.9,,,,,no,no,no,good,no,no,notckd 327 | 325,58.0,80.0,1.02,0.0,0.0,normal,normal,notpresent,notpresent,100.0,50.0,1.2,140.0,3.5,14.0,50,6700,6.5,no,no,no,good,no,no,notckd 328 | 326,47.0,60.0,1.02,0.0,0.0,normal,normal,notpresent,notpresent,109.0,25.0,1.1,141.0,4.7,15.8,41,8300,5.2,no,no,no,good,no,no,notckd 329 | 327,30.0,60.0,1.025,0.0,0.0,normal,normal,notpresent,notpresent,120.0,31.0,0.8,150.0,4.6,13.4,44,10700,5.8,no,no,no,good,no,no,notckd 330 | 328,28.0,70.0,1.02,0.0,0.0,normal,normal,,,131.0,29.0,0.6,145.0,4.9,,45,8600,6.5,no,no,no,good,no,no,notckd 331 | 329,33.0,60.0,1.025,0.0,0.0,normal,normal,notpresent,notpresent,80.0,25.0,0.9,146.0,3.5,14.1,48,7800,5.1,no,no,no,good,no,no,notckd 332 | 330,43.0,80.0,1.02,0.0,0.0,normal,normal,notpresent,notpresent,114.0,32.0,1.1,135.0,3.9,,42,,,no,no,no,good,no,no,notckd 333 | 331,59.0,70.0,1.025,0.0,0.0,normal,normal,notpresent,notpresent,130.0,39.0,0.7,147.0,4.7,13.5,46,6700,4.5,no,no,no,good,no,no,notckd 334 | 332,34.0,70.0,1.025,0.0,0.0,normal,normal,notpresent,notpresent,,33.0,1.0,150.0,5.0,15.3,44,10500,6.1,no,no,no,good,no,no,notckd 335 | 333,23.0,80.0,1.02,0.0,0.0,normal,normal,notpresent,notpresent,99.0,46.0,1.2,142.0,4.0,17.7,46,4300,5.5,no,no,no,good,no,no,notckd 336 | 334,24.0,80.0,1.025,0.0,0.0,normal,normal,notpresent,notpresent,125.0,,,136.0,3.5,15.4,43,5600,4.5,no,no,no,good,no,no,notckd 337 | 335,60.0,60.0,1.02,0.0,0.0,normal,normal,notpresent,notpresent,134.0,45.0,0.5,139.0,4.8,14.2,48,10700,5.6,no,no,no,good,no,no,notckd 338 | 336,25.0,60.0,1.02,0.0,0.0,normal,normal,notpresent,notpresent,119.0,27.0,0.5,,,15.2,40,9200,5.2,no,no,no,good,no,no,notckd 339 | 337,44.0,70.0,1.025,0.0,0.0,normal,normal,notpresent,notpresent,92.0,40.0,0.9,141.0,4.9,14.0,52,7500,6.2,no,no,no,good,no,no,notckd 340 | 338,62.0,80.0,1.02,0.0,0.0,normal,normal,notpresent,notpresent,132.0,34.0,0.8,147.0,3.5,17.8,44,4700,4.5,no,no,no,good,no,no,notckd 341 | 339,25.0,70.0,1.02,0.0,0.0,normal,normal,notpresent,notpresent,88.0,42.0,0.5,136.0,3.5,13.3,48,7000,4.9,no,no,no,good,no,no,notckd 342 | 340,32.0,70.0,1.025,0.0,0.0,normal,normal,notpresent,notpresent,100.0,29.0,1.1,142.0,4.5,14.3,43,6700,5.9,no,no,no,good,no,no,notckd 343 | 341,63.0,70.0,1.025,0.0,0.0,normal,normal,notpresent,notpresent,130.0,37.0,0.9,150.0,5.0,13.4,41,7300,4.7,no,no,no,good,no,no,notckd 344 | 342,44.0,60.0,1.02,0.0,0.0,normal,normal,notpresent,notpresent,95.0,46.0,0.5,138.0,4.2,15.0,50,7700,6.3,no,no,no,good,no,no,notckd 345 | 343,37.0,60.0,1.025,0.0,0.0,normal,normal,notpresent,notpresent,111.0,35.0,0.8,135.0,4.1,16.2,50,5500,5.7,no,no,no,good,no,no,notckd 346 | 344,64.0,60.0,1.02,0.0,0.0,normal,normal,notpresent,notpresent,106.0,27.0,0.7,150.0,3.3,14.4,42,8100,4.7,no,no,no,good,no,no,notckd 347 | 345,22.0,60.0,1.025,0.0,0.0,normal,normal,notpresent,notpresent,97.0,18.0,1.2,138.0,4.3,13.5,42,7900,6.4,no,no,no,good,no,no,notckd 348 | 346,33.0,60.0,,,,normal,normal,notpresent,notpresent,130.0,41.0,0.9,141.0,4.4,15.5,52,4300,5.8,no,no,no,good,no,no,notckd 349 | 347,43.0,60.0,1.025,0.0,0.0,normal,normal,notpresent,notpresent,108.0,25.0,1.0,144.0,5.0,17.8,43,7200,5.5,no,no,no,good,no,no,notckd 350 | 348,38.0,80.0,1.02,0.0,0.0,normal,normal,notpresent,notpresent,99.0,19.0,0.5,147.0,3.5,13.6,44,7300,6.4,no,no,no,good,no,no,notckd 351 | 349,35.0,70.0,1.025,0.0,0.0,,,notpresent,notpresent,82.0,36.0,1.1,150.0,3.5,14.5,52,9400,6.1,no,no,no,good,no,no,notckd 352 | 350,65.0,70.0,1.025,0.0,0.0,,,notpresent,notpresent,85.0,20.0,1.0,142.0,4.8,16.1,43,9600,4.5,no,no,no,good,no,no,notckd 353 | 351,29.0,80.0,1.02,0.0,0.0,normal,normal,notpresent,notpresent,83.0,49.0,0.9,139.0,3.3,17.5,40,9900,4.7,no,no,no,good,no,no,notckd 354 | 352,37.0,60.0,1.02,0.0,0.0,normal,normal,notpresent,notpresent,109.0,47.0,1.1,141.0,4.9,15.0,48,7000,5.2,no,no,no,good,no,no,notckd 355 | 353,39.0,60.0,1.02,0.0,0.0,normal,normal,notpresent,notpresent,86.0,37.0,0.6,150.0,5.0,13.6,51,5800,4.5,no,no,no,good,no,no,notckd 356 | 354,32.0,60.0,1.025,0.0,0.0,normal,normal,notpresent,notpresent,102.0,17.0,0.4,147.0,4.7,14.6,41,6800,5.1,no,no,no,good,no,no,notckd 357 | 355,23.0,60.0,1.02,0.0,0.0,normal,normal,notpresent,notpresent,95.0,24.0,0.8,145.0,5.0,15.0,52,6300,4.6,no,no,no,good,no,no,notckd 358 | 356,34.0,70.0,1.025,0.0,0.0,normal,normal,notpresent,notpresent,87.0,38.0,0.5,144.0,4.8,17.1,47,7400,6.1,no,no,no,good,no,no,notckd 359 | 357,66.0,70.0,1.025,0.0,0.0,normal,normal,notpresent,notpresent,107.0,16.0,1.1,140.0,3.6,13.6,42,11000,4.9,no,no,no,good,no,no,notckd 360 | 358,47.0,60.0,1.02,0.0,0.0,normal,normal,notpresent,notpresent,117.0,22.0,1.2,138.0,3.5,13.0,45,5200,5.6,no,no,no,good,no,no,notckd 361 | 359,74.0,60.0,1.02,0.0,0.0,normal,normal,notpresent,notpresent,88.0,50.0,0.6,147.0,3.7,17.2,53,6000,4.5,no,no,no,good,no,no,notckd 362 | 360,35.0,60.0,1.025,0.0,0.0,normal,normal,notpresent,notpresent,105.0,39.0,0.5,135.0,3.9,14.7,43,5800,6.2,no,no,no,good,no,no,notckd 363 | 361,29.0,80.0,1.02,0.0,0.0,normal,normal,notpresent,notpresent,70.0,16.0,0.7,138.0,3.5,13.7,54,5400,5.8,no,no,no,good,no,no,notckd 364 | 362,33.0,80.0,1.025,0.0,0.0,normal,normal,notpresent,notpresent,89.0,19.0,1.1,144.0,5.0,15.0,40,10300,4.8,no,no,no,good,no,no,notckd 365 | 363,67.0,80.0,1.025,0.0,0.0,normal,normal,notpresent,notpresent,99.0,40.0,0.5,,,17.8,44,5900,5.2,no,no,no,good,no,no,notckd 366 | 364,73.0,80.0,1.025,0.0,0.0,normal,normal,notpresent,notpresent,118.0,44.0,0.7,137.0,3.5,14.8,45,9300,4.7,no,no,no,good,no,no,notckd 367 | 365,24.0,80.0,1.02,0.0,0.0,normal,normal,notpresent,notpresent,93.0,46.0,1.0,145.0,3.5,,,10700,6.3,no,no,no,good,no,no,notckd 368 | 366,60.0,80.0,1.025,0.0,0.0,normal,normal,notpresent,notpresent,81.0,15.0,0.5,141.0,3.6,15.0,46,10500,5.3,no,no,no,good,no,no,notckd 369 | 367,68.0,60.0,1.025,0.0,0.0,normal,normal,notpresent,notpresent,125.0,41.0,1.1,139.0,3.8,17.4,50,6700,6.1,no,no,no,good,no,no,notckd 370 | 368,30.0,80.0,1.025,0.0,0.0,normal,normal,notpresent,notpresent,82.0,42.0,0.7,146.0,5.0,14.9,45,9400,5.9,no,no,no,good,no,no,notckd 371 | 369,75.0,70.0,1.02,0.0,0.0,normal,normal,notpresent,notpresent,107.0,48.0,0.8,144.0,3.5,13.6,46,10300,4.8,no,no,no,good,no,no,notckd 372 | 370,69.0,70.0,1.02,0.0,0.0,normal,normal,notpresent,notpresent,83.0,42.0,1.2,139.0,3.7,16.2,50,9300,5.4,no,no,no,good,no,no,notckd 373 | 371,28.0,60.0,1.025,0.0,0.0,normal,normal,notpresent,notpresent,79.0,50.0,0.5,145.0,5.0,17.6,51,6500,5.0,no,no,no,good,no,no,notckd 374 | 372,72.0,60.0,1.02,0.0,0.0,normal,normal,notpresent,notpresent,109.0,26.0,0.9,150.0,4.9,15.0,52,10500,5.5,no,no,no,good,no,no,notckd 375 | 373,61.0,70.0,1.025,0.0,0.0,normal,normal,notpresent,notpresent,133.0,38.0,1.0,142.0,3.6,13.7,47,9200,4.9,no,no,no,good,no,no,notckd 376 | 374,79.0,80.0,1.025,0.0,0.0,normal,normal,notpresent,notpresent,111.0,44.0,1.2,146.0,3.6,16.3,40,8000,6.4,no,no,no,good,no,no,notckd 377 | 375,70.0,80.0,1.02,0.0,0.0,normal,normal,notpresent,notpresent,74.0,41.0,0.5,143.0,4.5,15.1,48,9700,5.6,no,no,no,good,no,no,notckd 378 | 376,58.0,70.0,1.025,0.0,0.0,normal,normal,notpresent,notpresent,88.0,16.0,1.1,147.0,3.5,16.4,53,9100,5.2,no,no,no,good,no,no,notckd 379 | 377,64.0,70.0,1.02,0.0,0.0,normal,normal,notpresent,notpresent,97.0,27.0,0.7,145.0,4.8,13.8,49,6400,4.8,no,no,no,good,no,no,notckd 380 | 378,71.0,60.0,1.025,0.0,0.0,normal,normal,notpresent,notpresent,,,0.9,140.0,4.8,15.2,42,7700,5.5,no,no,no,good,no,no,notckd 381 | 379,62.0,80.0,1.025,0.0,0.0,normal,normal,notpresent,notpresent,78.0,45.0,0.6,138.0,3.5,16.1,50,5400,5.7,no,no,no,good,no,no,notckd 382 | 380,59.0,60.0,1.02,0.0,0.0,normal,normal,notpresent,notpresent,113.0,23.0,1.1,139.0,3.5,15.3,54,6500,4.9,no,no,no,good,no,no,notckd 383 | 381,71.0,70.0,1.025,0.0,0.0,,,notpresent,notpresent,79.0,47.0,0.5,142.0,4.8,16.6,40,5800,5.9,no,no,no,good,no,no,notckd 384 | 382,48.0,80.0,1.025,0.0,0.0,normal,normal,notpresent,notpresent,75.0,22.0,0.8,137.0,5.0,16.8,51,6000,6.5,no,no,no,good,no,no,notckd 385 | 383,80.0,80.0,1.025,0.0,0.0,normal,normal,notpresent,notpresent,119.0,46.0,0.7,141.0,4.9,13.9,49,5100,5.0,no,no,no,good,no,no,notckd 386 | 384,57.0,60.0,1.02,0.0,0.0,normal,normal,notpresent,notpresent,132.0,18.0,1.1,150.0,4.7,15.4,42,11000,4.5,no,no,no,good,no,no,notckd 387 | 385,63.0,70.0,1.02,0.0,0.0,normal,normal,notpresent,notpresent,113.0,25.0,0.6,146.0,4.9,16.5,52,8000,5.1,no,no,no,good,no,no,notckd 388 | 386,46.0,70.0,1.025,0.0,0.0,normal,normal,notpresent,notpresent,100.0,47.0,0.5,142.0,3.5,16.4,43,5700,6.5,no,no,no,good,no,no,notckd 389 | 387,15.0,80.0,1.025,0.0,0.0,normal,normal,notpresent,notpresent,93.0,17.0,0.9,136.0,3.9,16.7,50,6200,5.2,no,no,no,good,no,no,notckd 390 | 388,51.0,80.0,1.02,0.0,0.0,normal,normal,notpresent,notpresent,94.0,15.0,1.2,144.0,3.7,15.5,46,9500,6.4,no,no,no,good,no,no,notckd 391 | 389,41.0,80.0,1.025,0.0,0.0,normal,normal,notpresent,notpresent,112.0,48.0,0.7,140.0,5.0,17.0,52,7200,5.8,no,no,no,good,no,no,notckd 392 | 390,52.0,80.0,1.025,0.0,0.0,normal,normal,notpresent,notpresent,99.0,25.0,0.8,135.0,3.7,15.0,52,6300,5.3,no,no,no,good,no,no,notckd 393 | 391,36.0,80.0,1.025,0.0,0.0,normal,normal,notpresent,notpresent,85.0,16.0,1.1,142.0,4.1,15.6,44,5800,6.3,no,no,no,good,no,no,notckd 394 | 392,57.0,80.0,1.02,0.0,0.0,normal,normal,notpresent,notpresent,133.0,48.0,1.2,147.0,4.3,14.8,46,6600,5.5,no,no,no,good,no,no,notckd 395 | 393,43.0,60.0,1.025,0.0,0.0,normal,normal,notpresent,notpresent,117.0,45.0,0.7,141.0,4.4,13.0,54,7400,5.4,no,no,no,good,no,no,notckd 396 | 394,50.0,80.0,1.02,0.0,0.0,normal,normal,notpresent,notpresent,137.0,46.0,0.8,139.0,5.0,14.1,45,9500,4.6,no,no,no,good,no,no,notckd 397 | 395,55.0,80.0,1.02,0.0,0.0,normal,normal,notpresent,notpresent,140.0,49.0,0.5,150.0,4.9,15.7,47,6700,4.9,no,no,no,good,no,no,notckd 398 | 396,42.0,70.0,1.025,0.0,0.0,normal,normal,notpresent,notpresent,75.0,31.0,1.2,141.0,3.5,16.5,54,7800,6.2,no,no,no,good,no,no,notckd 399 | 397,12.0,80.0,1.02,0.0,0.0,normal,normal,notpresent,notpresent,100.0,26.0,0.6,137.0,4.4,15.8,49,6600,5.4,no,no,no,good,no,no,notckd 400 | 398,17.0,60.0,1.025,0.0,0.0,normal,normal,notpresent,notpresent,114.0,50.0,1.0,135.0,4.9,14.2,51,7200,5.9,no,no,no,good,no,no,notckd 401 | 399,58.0,80.0,1.025,0.0,0.0,normal,normal,notpresent,notpresent,131.0,18.0,1.1,141.0,3.5,15.8,53,6800,6.1,no,no,no,good,no,no,notckd 402 | -------------------------------------------------------------------------------- /6. Chronic KIdney Disease/Explanation.docx: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/GauravRajwada/100-Data-Science-Projects/ff69c5ea24308eaaa8356e066bfacdf42d11cfcc/6. Chronic KIdney Disease/Explanation.docx -------------------------------------------------------------------------------- /6. Chronic KIdney Disease/Kidney-Disease.sav: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/GauravRajwada/100-Data-Science-Projects/ff69c5ea24308eaaa8356e066bfacdf42d11cfcc/6. Chronic KIdney Disease/Kidney-Disease.sav -------------------------------------------------------------------------------- /7. Maleria Cell Images/malaria-cell-cnn.h5: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/GauravRajwada/100-Data-Science-Projects/ff69c5ea24308eaaa8356e066bfacdf42d11cfcc/7. Maleria Cell Images/malaria-cell-cnn.h5 -------------------------------------------------------------------------------- /8. Cardiovascular_Disease/Cardiovascular_Disease_model.sav: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/GauravRajwada/100-Data-Science-Projects/ff69c5ea24308eaaa8356e066bfacdf42d11cfcc/8. Cardiovascular_Disease/Cardiovascular_Disease_model.sav -------------------------------------------------------------------------------- /8. Cardiovascular_Disease/Explanation.docx: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/GauravRajwada/100-Data-Science-Projects/ff69c5ea24308eaaa8356e066bfacdf42d11cfcc/8. Cardiovascular_Disease/Explanation.docx -------------------------------------------------------------------------------- /8. Cardiovascular_Disease/cardio.csv: -------------------------------------------------------------------------------- 1 | ,id,age,gender,height,weight,ap_hi,ap_lo,cholesterol,gluc,smoke,alco,active,cardio 2 | count,70000.0,70000.0,70000.0,70000.0,70000.0,70000.0,70000.0,70000.0,70000.0,70000.0,70000.0,70000.0,70000.0 3 | mean,49972.4199,19468.865814285713,1.3495714285714286,164.35922857142856,74.20569,128.8172857142857,96.63041428571428,1.3668714285714285,1.226457142857143,0.08812857142857143,0.053771428571428574,0.8037285714285715,0.4997 4 | std,28851.30232317303,2467.2516672413913,0.47683801558294814,8.210126364538139,14.39575667851056,154.01141945605565,188.47253029643605,0.6802503486997775,0.5722702766136001,0.28348381677011014,0.22556770360401027,0.3971790635048892,0.5000034814661523 5 | min,0.0,10798.0,1.0,55.0,10.0,-150.0,-70.0,1.0,1.0,0.0,0.0,0.0,0.0 6 | 25%,25006.75,17664.0,1.0,159.0,65.0,120.0,80.0,1.0,1.0,0.0,0.0,1.0,0.0 7 | 50%,50001.5,19703.0,1.0,165.0,72.0,120.0,80.0,1.0,1.0,0.0,0.0,1.0,0.0 8 | 75%,74889.25,21327.0,2.0,170.0,82.0,140.0,90.0,2.0,1.0,0.0,0.0,1.0,1.0 9 | max,99999.0,23713.0,2.0,250.0,200.0,16020.0,11000.0,3.0,3.0,1.0,1.0,1.0,1.0 10 | -------------------------------------------------------------------------------- /9. Heart_Disease_UCI/Explanation.docx: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/GauravRajwada/100-Data-Science-Projects/ff69c5ea24308eaaa8356e066bfacdf42d11cfcc/9. Heart_Disease_UCI/Explanation.docx -------------------------------------------------------------------------------- /9. Heart_Disease_UCI/Heart_Disease_UCI.sav: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/GauravRajwada/100-Data-Science-Projects/ff69c5ea24308eaaa8356e066bfacdf42d11cfcc/9. Heart_Disease_UCI/Heart_Disease_UCI.sav -------------------------------------------------------------------------------- /9. Heart_Disease_UCI/Report.PNG: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/GauravRajwada/100-Data-Science-Projects/ff69c5ea24308eaaa8356e066bfacdf42d11cfcc/9. Heart_Disease_UCI/Report.PNG -------------------------------------------------------------------------------- /9. Heart_Disease_UCI/heart_disease_dataset.csv: -------------------------------------------------------------------------------- 1 | age,sex,cp,trestbps,chol,fbs,restecg,thalach,exang,oldpeak,slope,ca,thal,target 2 | 63,1,1,145,233,1,2,150,0,2.3,3,0,6,0 3 | 67,1,4,160,286,0,2,108,1,1.5,2,3,3,1 4 | 67,1,4,120,229,0,2,129,1,2.6,2,2,7,1 5 | 37,1,3,130,250,0,0,187,0,3.5,3,0,3,0 6 | 41,0,2,130,204,0,2,172,0,1.4,1,0,3,0 7 | 56,1,2,120,236,0,0,178,0,0.8,1,0,3,0 8 | 62,0,4,140,268,0,2,160,0,3.6,3,2,3,1 9 | 57,0,4,120,354,0,0,163,1,0.6,1,0,3,0 10 | 63,1,4,130,254,0,2,147,0,1.4,2,1,7,1 11 | 53,1,4,140,203,1,2,155,1,3.1,3,0,7,1 12 | 57,1,4,140,192,0,0,148,0,0.4,2,0,6,0 13 | 56,0,2,140,294,0,2,153,0,1.3,2,0,3,0 14 | 56,1,3,130,256,1,2,142,1,0.6,2,1,6,1 15 | 44,1,2,120,263,0,0,173,0,0,1,0,7,0 16 | 52,1,3,172,199,1,0,162,0,0.5,1,0,7,0 17 | 57,1,3,150,168,0,0,174,0,1.6,1,0,3,0 18 | 48,1,2,110,229,0,0,168,0,1,3,0,7,1 19 | 54,1,4,140,239,0,0,160,0,1.2,1,0,3,0 20 | 48,0,3,130,275,0,0,139,0,0.2,1,0,3,0 21 | 49,1,2,130,266,0,0,171,0,0.6,1,0,3,0 22 | 64,1,1,110,211,0,2,144,1,1.8,2,0,3,0 23 | 58,0,1,150,283,1,2,162,0,1,1,0,3,0 24 | 58,1,2,120,284,0,2,160,0,1.8,2,0,3,1 25 | 58,1,3,132,224,0,2,173,0,3.2,1,2,7,1 26 | 60,1,4,130,206,0,2,132,1,2.4,2,2,7,1 27 | 50,0,3,120,219,0,0,158,0,1.6,2,0,3,0 28 | 58,0,3,120,340,0,0,172,0,0,1,0,3,0 29 | 66,0,1,150,226,0,0,114,0,2.6,3,0,3,0 30 | 43,1,4,150,247,0,0,171,0,1.5,1,0,3,0 31 | 40,1,4,110,167,0,2,114,1,2,2,0,7,1 32 | 69,0,1,140,239,0,0,151,0,1.8,1,2,3,0 33 | 60,1,4,117,230,1,0,160,1,1.4,1,2,7,1 34 | 64,1,3,140,335,0,0,158,0,0,1,0,3,1 35 | 59,1,4,135,234,0,0,161,0,0.5,2,0,7,0 36 | 44,1,3,130,233,0,0,179,1,0.4,1,0,3,0 37 | 42,1,4,140,226,0,0,178,0,0,1,0,3,0 38 | 43,1,4,120,177,0,2,120,1,2.5,2,0,7,1 39 | 57,1,4,150,276,0,2,112,1,0.6,2,1,6,1 40 | 55,1,4,132,353,0,0,132,1,1.2,2,1,7,1 41 | 61,1,3,150,243,1,0,137,1,1,2,0,3,0 42 | 65,0,4,150,225,0,2,114,0,1,2,3,7,1 43 | 40,1,1,140,199,0,0,178,1,1.4,1,0,7,0 44 | 71,0,2,160,302,0,0,162,0,0.4,1,2,3,0 45 | 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257 | 42,0,3,120,209,0,0,173,0,0,2,0,3,0 258 | 67,0,4,106,223,0,0,142,0,0.3,1,2,3,0 259 | 76,0,3,140,197,0,1,116,0,1.1,2,0,3,0 260 | 70,1,2,156,245,0,2,143,0,0,1,0,3,0 261 | 57,1,2,124,261,0,0,141,0,0.3,1,0,7,1 262 | 44,0,3,118,242,0,0,149,0,0.3,2,1,3,0 263 | 58,0,2,136,319,1,2,152,0,0,1,2,3,1 264 | 60,0,1,150,240,0,0,171,0,0.9,1,0,3,0 265 | 44,1,3,120,226,0,0,169,0,0,1,0,3,0 266 | 61,1,4,138,166,0,2,125,1,3.6,2,1,3,1 267 | 42,1,4,136,315,0,0,125,1,1.8,2,0,6,1 268 | 52,1,4,128,204,1,0,156,1,1,2,0,-100000,1 269 | 59,1,3,126,218,1,0,134,0,2.2,2,1,6,1 270 | 40,1,4,152,223,0,0,181,0,0,1,0,7,1 271 | 42,1,3,130,180,0,0,150,0,0,1,0,3,0 272 | 61,1,4,140,207,0,2,138,1,1.9,1,1,7,1 273 | 66,1,4,160,228,0,2,138,0,2.3,1,0,6,0 274 | 46,1,4,140,311,0,0,120,1,1.8,2,2,7,1 275 | 71,0,4,112,149,0,0,125,0,1.6,2,0,3,0 276 | 59,1,1,134,204,0,0,162,0,0.8,1,2,3,1 277 | 64,1,1,170,227,0,2,155,0,0.6,2,0,7,0 278 | 66,0,3,146,278,0,2,152,0,0,2,1,3,0 279 | 39,0,3,138,220,0,0,152,0,0,2,0,3,0 280 | 57,1,2,154,232,0,2,164,0,0,1,1,3,1 281 | 58,0,4,130,197,0,0,131,0,0.6,2,0,3,0 282 | 57,1,4,110,335,0,0,143,1,3,2,1,7,1 283 | 47,1,3,130,253,0,0,179,0,0,1,0,3,0 284 | 55,0,4,128,205,0,1,130,1,2,2,1,7,1 285 | 35,1,2,122,192,0,0,174,0,0,1,0,3,0 286 | 61,1,4,148,203,0,0,161,0,0,1,1,7,1 287 | 58,1,4,114,318,0,1,140,0,4.4,3,3,6,1 288 | 58,0,4,170,225,1,2,146,1,2.8,2,2,6,1 289 | 58,1,2,125,220,0,0,144,0,0.4,2,-100000,7,0 290 | 56,1,2,130,221,0,2,163,0,0,1,0,7,0 291 | 56,1,2,120,240,0,0,169,0,0,3,0,3,0 292 | 67,1,3,152,212,0,2,150,0,0.8,2,0,7,1 293 | 55,0,2,132,342,0,0,166,0,1.2,1,0,3,0 294 | 44,1,4,120,169,0,0,144,1,2.8,3,0,6,1 295 | 63,1,4,140,187,0,2,144,1,4,1,2,7,1 296 | 63,0,4,124,197,0,0,136,1,0,2,0,3,1 297 | 41,1,2,120,157,0,0,182,0,0,1,0,3,0 298 | 59,1,4,164,176,1,2,90,0,1,2,2,6,1 299 | 57,0,4,140,241,0,0,123,1,0.2,2,0,7,1 300 | 45,1,1,110,264,0,0,132,0,1.2,2,0,7,1 301 | 68,1,4,144,193,1,0,141,0,3.4,2,2,7,1 302 | 57,1,4,130,131,0,0,115,1,1.2,2,1,7,1 303 | 57,0,2,130,236,0,2,174,0,0,2,1,3,1 304 | 38,1,3,138,175,0,0,173,0,0,1,-100000,3,0 305 | -------------------------------------------------------------------------------- /DNA_classification/.ds_Store: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/GauravRajwada/100-Data-Science-Projects/ff69c5ea24308eaaa8356e066bfacdf42d11cfcc/DNA_classification/.ds_Store -------------------------------------------------------------------------------- /DNA_classification/Clasification_Of_dna.ipynb: -------------------------------------------------------------------------------- 1 | { 2 | "cells": [ 3 | { 4 | "cell_type": "markdown", 5 | "metadata": {}, 6 | "source": [ 7 | "# Classifying DNA Sequences\n", 8 | "\n", 9 | "## Step 1: Importing the Dataset\n", 10 | "\n", 11 | "The following code cells will import necessary libraries and import the dataset from the UCI repository as a Pandas DataFrame." 12 | ] 13 | }, 14 | { 15 | "cell_type": "code", 16 | "execution_count": 1, 17 | "metadata": {}, 18 | "outputs": [], 19 | "source": [ 20 | "# To make sure all of the correct libraries are installed, import each module and print the version number\n", 21 | "\n", 22 | "import sys\n", 23 | "import numpy\n", 24 | "import sklearn\n", 25 | "import pandas" 26 | ] 27 | }, 28 | { 29 | "cell_type": "code", 30 | "execution_count": 2, 31 | "metadata": {}, 32 | "outputs": [], 33 | "source": [ 34 | "# Import, change module names\n", 35 | "import numpy as np\n", 36 | "import pandas as pd\n", 37 | "\n", 38 | "# import the uci Molecular Biology (Promoter Gene Sequences) Data Set\n", 39 | "url = 'https://archive.ics.uci.edu/ml/machine-learning-databases/molecular-biology/promoter-gene-sequences/promoters.data'\n", 40 | "names = ['Class', 'id', 'Sequence']\n", 41 | "data = pd.read_csv(url, names = names)" 42 | ] 43 | }, 44 | { 45 | "cell_type": "code", 46 | "execution_count": 3, 47 | "metadata": {}, 48 | "outputs": [ 49 | { 50 | "name": "stdout", 51 | "output_type": "stream", 52 | "text": [ 53 | "Class +\n", 54 | "id S10\n", 55 | "Sequence \\t\\ttactagcaatacgcttgcgttcggtggttaagtatgtataat...\n", 56 | "Name: 0, dtype: object\n" 57 | ] 58 | } 59 | ], 60 | "source": [ 61 | "print(data.iloc[0])" 62 | ] 63 | }, 64 | { 65 | "cell_type": "markdown", 66 | "metadata": {}, 67 | "source": [ 68 | "## Step 2: Preprocessing the Dataset\n", 69 | "\n", 70 | "The data is not in a usable form; as a result, we will need to process it before using it to train our algorithms." 71 | ] 72 | }, 73 | { 74 | "cell_type": "code", 75 | "execution_count": 4, 76 | "metadata": {}, 77 | "outputs": [ 78 | { 79 | "name": "stdout", 80 | "output_type": "stream", 81 | "text": [ 82 | "0 +\n", 83 | "1 +\n", 84 | "2 +\n", 85 | "3 +\n", 86 | "4 +\n", 87 | "Name: Class, dtype: object\n" 88 | ] 89 | } 90 | ], 91 | "source": [ 92 | "# Building our Dataset by creating a custom Pandas DataFrame\n", 93 | "# Each column in a DataFrame is called a Series. Lets start by making a series for each column.\n", 94 | "\n", 95 | "classes = data.loc[:, 'Class']\n", 96 | "print(classes[:5])" 97 | ] 98 | }, 99 | { 100 | "cell_type": "code", 101 | "execution_count": 5, 102 | "metadata": {}, 103 | "outputs": [ 104 | { 105 | "name": "stdout", 106 | "output_type": "stream", 107 | "text": [ 108 | "['t', 'a', 'c', 't', 'a', 'g', 'c', 'a', 'a', 't', 'a', 'c', 'g', 'c', 't', 't', 'g', 'c', 'g', 't', 't', 'c', 'g', 'g', 't', 'g', 'g', 't', 't', 'a', 'a', 'g', 't', 'a', 't', 'g', 't', 'a', 't', 'a', 'a', 't', 'g', 'c', 'g', 'c', 'g', 'g', 'g', 'c', 't', 't', 'g', 't', 'c', 'g', 't', '+']\n" 109 | ] 110 | } 111 | ], 112 | "source": [ 113 | "# generate list of DNA sequences\n", 114 | "sequences = list(data.loc[:, 'Sequence'])\n", 115 | "dataset = {}\n", 116 | "\n", 117 | "# loop through sequences and split into individual nucleotides\n", 118 | "for i, seq in enumerate(sequences):\n", 119 | " \n", 120 | " # split into nucleotides, remove tab characters\n", 121 | " nucleotides = list(seq)\n", 122 | " nucleotides = [x for x in nucleotides if x != '\\t']\n", 123 | " \n", 124 | " # append class assignment\n", 125 | " nucleotides.append(classes[i])\n", 126 | " \n", 127 | " # add to dataset\n", 128 | " dataset[i] = nucleotides\n", 129 | " \n", 130 | "print(dataset[0])" 131 | ] 132 | }, 133 | { 134 | "cell_type": "code", 135 | "execution_count": 6, 136 | "metadata": {}, 137 | "outputs": [ 138 | { 139 | "name": "stdout", 140 | "output_type": "stream", 141 | "text": [ 142 | " 0 1 2 3 4 5 6 7 8 9 ... 96 97 98 99 100 101 102 \\\n", 143 | "0 t t g a t a c t c t ... c c t a g c g \n", 144 | "1 a g t a c g a t g t ... c g a g a c t \n", 145 | "2 c c a t g g g t a t ... g c t a g t a \n", 146 | "3 t t c t a g g c c t ... a t g g a c t \n", 147 | "4 a a t g t g g t t a ... g a a g g a t \n", 148 | "5 g t a t a c g a t a ... t g c g c a c \n", 149 | "6 c c g g a a g c a a ... a g c t a t t \n", 150 | "7 a c a a t a t a a t ... g a g g t g c \n", 151 | "8 a t g t t g g a t t ... a c a t g g a \n", 152 | "9 t g a g a g g a a t ... c t a a t c a \n", 153 | "10 a a a t a a a a t c ... c t c c c c c \n", 154 | "11 c c c g c g g c a c ... c t g t a t a \n", 155 | "12 g a t t t g g a c t ... t c a c g c a \n", 156 | "13 c g a a a a a c t c ... t t g c c t g \n", 157 | "14 t t g t t t t t g t ... a t t a c a a \n", 158 | "15 t t t c t g t t c t ... g g c a t a t \n", 159 | "16 g g g g g g t g g g ... a t a g c a t \n", 160 | "17 c t c a a a a a a t ... g t a a g c a \n", 161 | "18 g c a a c a a t c c ... a g t a a g a \n", 162 | "19 t a t g g a g a a a ... g a c g c g c \n", 163 | "20 t c t t a g c c g g ... c t a a a g c \n", 164 | "21 c g a g a a c t g g ... a t g g a t g \n", 165 | "22 g c g t a g a g a c ... t t a g c c a \n", 166 | "23 g t c g a g t t c c ... g t c a t t c \n", 167 | "24 t g t t g t c a g g ... t c c a t t a \n", 168 | "25 g a t t c t t t t g ... c c g g g g g \n", 169 | "26 g t a g t g c g c a ... a a c a c a a \n", 170 | "27 t t t c g c c a c a ... g t t t a g t \n", 171 | "28 t g t g a c t g g t ... c g t g t g t \n", 172 | "29 a g t g a g g c t a ... c c t a a g c \n", 173 | "30 a t t a a t a a t a ... t g g g a g a \n", 174 | "31 g g t g a a t t c c ... c g a g a t a \n", 175 | "32 t t t t c t g a t t ... g t c c t t t \n", 176 | "33 a c t a c a a c g c ... a g t t g t c \n", 177 | "34 t g g g a a c a t c ... c t c a c t t \n", 178 | "35 g t t a c a g g g c ... a t t g t t c \n", 179 | "36 t t t t t g c t t t ... a t g a t t g \n", 180 | "37 a a a g a a a a a a ... c t g c t g t \n", 181 | "38 t c t t g a t t a t ... t g t g c c g \n", 182 | "39 a a c t a a a a a a ... t c a t t t g \n", 183 | "40 a a a a a c g a t a ... g g t c t g a \n", 184 | "41 t t t g t t t t c t ... c c t t g a t \n", 185 | "42 g c g a g a c t g g ... a a a c t a g \n", 186 | "43 c t c a c g a g c c ... t a c t a a g \n", 187 | "44 g a t t g a g c a g ... a t t g g g a \n", 188 | "45 c a a a c g c t a c ... a g g c a g c \n", 189 | "46 g c a c c t c t t c ... a t t a c a g \n", 190 | "47 g g c t t c c c g a ... t t g t g g t \n", 191 | "48 g c c a c c a a a c ... g a a g t g t \n", 192 | "49 c a a a c g t a a c ... c a a g g a c \n", 193 | "50 t t c c g t c c a a ... t t c a c a a \n", 194 | "51 t c c a t t a a t c ... t c a g c c a \n", 195 | "52 g g c a g t t g g t ... t g t t c t c \n", 196 | "53 t c g a g a g a g g ... c c t a t a a \n", 197 | "54 c c g c t g a a t a ... t t a t a t t \n", 198 | "55 g a c t a g a c t c ... t t t g c a t \n", 199 | "56 t a g c g t t a t a ... g t t a g t g \n", 200 | "57 + + + + + + + + + + ... - - - - - - - \n", 201 | "\n", 202 | " 103 104 105 \n", 203 | "0 c c t \n", 204 | "1 g t a \n", 205 | "2 c c a \n", 206 | "3 g g c \n", 207 | "4 a t a \n", 208 | "5 c c t \n", 209 | "6 t c t \n", 210 | "7 a t a \n", 211 | "8 c c a \n", 212 | "9 g a t \n", 213 | "10 a a a \n", 214 | "11 t t a \n", 215 | "12 g g a \n", 216 | "13 a g t \n", 217 | "14 g c a \n", 218 | "15 a c a \n", 219 | "16 t t g \n", 220 | "17 g c g \n", 221 | "18 c t a \n", 222 | "19 c a g \n", 223 | "20 t a g \n", 224 | "21 g a c \n", 225 | "22 a c t \n", 226 | "23 g g c \n", 227 | "24 t g t \n", 228 | "25 g g a \n", 229 | "26 c t a \n", 230 | "27 t c t \n", 231 | "28 t t g \n", 232 | "29 c t g \n", 233 | "30 c g c \n", 234 | "31 g a a \n", 235 | "32 t g c \n", 236 | "33 t g t \n", 237 | "34 a g c \n", 238 | "35 c g a \n", 239 | "36 t t t \n", 240 | "37 g t t \n", 241 | "38 g t a \n", 242 | "39 a t g \n", 243 | "40 t t c \n", 244 | "41 t t c \n", 245 | "42 g g a \n", 246 | "43 t c a \n", 247 | "44 c t t \n", 248 | "45 a g c \n", 249 | "46 c a a \n", 250 | "47 c a a \n", 251 | "48 a a t \n", 252 | "49 a g c \n", 253 | "50 g g a \n", 254 | "51 g a a \n", 255 | "52 c g g \n", 256 | "53 t g a \n", 257 | "54 t a a \n", 258 | "55 c a c \n", 259 | "56 c c t \n", 260 | "57 - - - \n", 261 | "\n", 262 | "[58 rows x 106 columns]\n" 263 | ] 264 | } 265 | ], 266 | "source": [ 267 | "# turn dataset into pandas DataFrame\n", 268 | "dframe = pd.DataFrame(dataset)\n", 269 | "print(dframe)" 270 | ] 271 | }, 272 | { 273 | "cell_type": "code", 274 | "execution_count": 7, 275 | "metadata": {}, 276 | "outputs": [ 277 | { 278 | "name": "stdout", 279 | "output_type": "stream", 280 | "text": [ 281 | " 0 1 2 3 4 5 6 7 8 9 ... 48 49 50 51 52 53 54 55 56 57\n", 282 | "0 t a c t a g c a a t ... g c t t g t c g t +\n", 283 | "1 t g c t a t c c t g ... c a t c g c c a a +\n", 284 | "2 g t a c t a g a g a ... c a c c c g g c g +\n", 285 | "3 a a t t g t g a t g ... a a c a a a c t c +\n", 286 | "4 t c g a t a a t t a ... c c g t g g t a g +\n", 287 | "\n", 288 | "[5 rows x 58 columns]\n" 289 | ] 290 | } 291 | ], 292 | "source": [ 293 | "# transpose the DataFrame\n", 294 | "df = dframe.transpose()\n", 295 | "print(df.iloc[:5])" 296 | ] 297 | }, 298 | { 299 | "cell_type": "code", 300 | "execution_count": 8, 301 | "metadata": {}, 302 | "outputs": [ 303 | { 304 | "name": "stdout", 305 | "output_type": "stream", 306 | "text": [ 307 | " 0 1 2 3 4 5 6 7 8 9 ... 48 49 50 51 52 53 54 55 56 Class\n", 308 | "0 t a c t a g c a a t ... g c t t g t c g t +\n", 309 | "1 t g c t a t c c t g ... c a t c g c c a a +\n", 310 | "2 g t a c t a g a g a ... c a c c c g g c g +\n", 311 | "3 a a t t g t g a t g ... a a c a a a c t c +\n", 312 | "4 t c g a t a a t t a ... c c g t g g t a g +\n", 313 | "\n", 314 | "[5 rows x 58 columns]\n" 315 | ] 316 | } 317 | ], 318 | "source": [ 319 | "# for clarity, lets rename the last dataframe column to class\n", 320 | "df.rename(columns = {57: 'Class'}, inplace = True) \n", 321 | "print(df.iloc[:5])" 322 | ] 323 | }, 324 | { 325 | "cell_type": "code", 326 | "execution_count": 9, 327 | "metadata": {}, 328 | "outputs": [ 329 | { 330 | "data": { 331 | "text/html": [ 332 | "
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0123456789...484950515253545556Class
count106106106106106106106106106106...106106106106106106106106106106
unique4444444444...4444444442
toptaacaaaaaa...cccttcctt-
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4 rows × 58 columns

\n", 473 | "
" 474 | ], 475 | "text/plain": [ 476 | " 0 1 2 3 4 5 6 7 8 9 ... 48 49 50 \\\n", 477 | "count 106 106 106 106 106 106 106 106 106 106 ... 106 106 106 \n", 478 | "unique 4 4 4 4 4 4 4 4 4 4 ... 4 4 4 \n", 479 | "top t a a c a a a a a a ... c c c \n", 480 | "freq 38 34 30 30 36 42 38 34 33 36 ... 36 42 31 \n", 481 | "\n", 482 | " 51 52 53 54 55 56 Class \n", 483 | "count 106 106 106 106 106 106 106 \n", 484 | "unique 4 4 4 4 4 4 2 \n", 485 | "top t t c c t t - \n", 486 | "freq 33 35 32 29 29 34 53 \n", 487 | "\n", 488 | "[4 rows x 58 columns]" 489 | ] 490 | }, 491 | "execution_count": 9, 492 | "metadata": {}, 493 | "output_type": "execute_result" 494 | } 495 | ], 496 | "source": [ 497 | "# looks good! Let's start to familiarize ourselves with the dataset so we can pick the most suitable \n", 498 | "# algorithms for this data\n", 499 | "\n", 500 | "df.describe()" 501 | ] 502 | }, 503 | { 504 | "cell_type": "code", 505 | "execution_count": 10, 506 | "metadata": {}, 507 | "outputs": [ 508 | { 509 | "name": "stdout", 510 | "output_type": "stream", 511 | "text": [ 512 | " 0 1 2 3 4 5 6 7 8 9 ... 48 \\\n", 513 | "t 38.0 26.0 27.0 26.0 22.0 24.0 30.0 32.0 32.0 28.0 ... 21.0 \n", 514 | "c 27.0 22.0 21.0 30.0 19.0 18.0 21.0 20.0 22.0 22.0 ... 36.0 \n", 515 | "a 26.0 34.0 30.0 22.0 36.0 42.0 38.0 34.0 33.0 36.0 ... 23.0 \n", 516 | "g 15.0 24.0 28.0 28.0 29.0 22.0 17.0 20.0 19.0 20.0 ... 26.0 \n", 517 | "- NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN ... NaN \n", 518 | "+ NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN ... NaN \n", 519 | "\n", 520 | " 49 50 51 52 53 54 55 56 Class \n", 521 | "t 22.0 23.0 33.0 35.0 30.0 23.0 29.0 34.0 NaN \n", 522 | "c 42.0 31.0 32.0 21.0 32.0 29.0 29.0 17.0 NaN \n", 523 | "a 24.0 28.0 27.0 25.0 22.0 26.0 24.0 27.0 NaN \n", 524 | "g 18.0 24.0 14.0 25.0 22.0 28.0 24.0 28.0 NaN \n", 525 | "- NaN NaN NaN NaN NaN NaN NaN NaN 53.0 \n", 526 | "+ NaN NaN NaN NaN NaN NaN NaN NaN 53.0 \n", 527 | "\n", 528 | "[6 rows x 58 columns]\n" 529 | ] 530 | } 531 | ], 532 | "source": [ 533 | "# desribe does not tell us enough information since the attributes are text. Lets record value counts for each sequence\n", 534 | "series = []\n", 535 | "for name in df.columns:\n", 536 | " series.append(df[name].value_counts())\n", 537 | " \n", 538 | "info = pd.DataFrame(series)\n", 539 | "details = info.transpose()\n", 540 | "print(details)" 541 | ] 542 | }, 543 | { 544 | "cell_type": "code", 545 | "execution_count": 11, 546 | "metadata": {}, 547 | "outputs": [ 548 | { 549 | "data": { 550 | "text/html": [ 551 | "
\n", 552 | "\n", 565 | "\n", 566 | " \n", 567 | " \n", 568 | " \n", 569 | " \n", 570 | " \n", 571 | " \n", 572 | " \n", 573 | " \n", 574 | " \n", 575 | " \n", 576 | " \n", 577 | " \n", 578 | " \n", 579 | " \n", 580 | " \n", 581 | " \n", 582 | " \n", 583 | " \n", 584 | " \n", 585 | " \n", 586 | " \n", 587 | " \n", 588 | " \n", 589 | " \n", 590 | " \n", 591 | " \n", 592 | " \n", 593 | " \n", 594 | " \n", 595 | " \n", 596 | " \n", 597 | " \n", 598 | " \n", 599 | " \n", 600 | " \n", 601 | " \n", 602 | " \n", 603 | " \n", 604 | " \n", 605 | " \n", 606 | " \n", 607 | " \n", 608 | " \n", 609 | " \n", 610 | " \n", 611 | " \n", 612 | " \n", 613 | " \n", 614 | " \n", 615 | " \n", 616 | " \n", 617 | " \n", 618 | " \n", 619 | " \n", 620 | " \n", 621 | " \n", 622 | " \n", 623 | " \n", 624 | " \n", 625 | " \n", 626 | " \n", 627 | " \n", 628 | " \n", 629 | " \n", 630 | " \n", 631 | " \n", 632 | " \n", 633 | " \n", 634 | " \n", 635 | " \n", 636 | " \n", 637 | " \n", 638 | " \n", 639 | " \n", 640 | " \n", 641 | " \n", 642 | " \n", 643 | " \n", 644 | " \n", 645 | " \n", 646 | " \n", 647 | " \n", 648 | " \n", 649 | " \n", 650 | " \n", 651 | " \n", 652 | " \n", 653 | " \n", 654 | " \n", 655 | " \n", 656 | " \n", 657 | " \n", 658 | " \n", 659 | " \n", 660 | " \n", 661 | " \n", 662 | " \n", 663 | " \n", 664 | " \n", 665 | " \n", 666 | " \n", 667 | " \n", 668 | " \n", 669 | " \n", 670 | " \n", 671 | " \n", 672 | " \n", 673 | " \n", 674 | " \n", 675 | " \n", 676 | " \n", 677 | " \n", 678 | " \n", 679 | " \n", 680 | " \n", 681 | " \n", 682 | " \n", 683 | " \n", 684 | " \n", 685 | " \n", 686 | " \n", 687 | " \n", 688 | " \n", 689 | " \n", 690 | " \n", 691 | " \n", 692 | " \n", 693 | " \n", 694 | " \n", 695 | " \n", 696 | " \n", 697 | " \n", 698 | " \n", 699 | " \n", 700 | " \n", 701 | " \n", 702 | " \n", 703 | " \n", 704 | " \n", 705 | " \n", 706 | " \n", 707 | " \n", 708 | " \n", 709 | " \n", 710 | " \n", 711 | " \n", 712 | " \n", 713 | " \n", 714 | "
0_a0_c0_g0_t1_a1_c1_g1_t2_a2_c...55_a55_c55_g55_t56_a56_c56_g56_tClass_+Class_-
00001100001...0010000110
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5 rows × 230 columns

\n", 716 | "
" 717 | ], 718 | "text/plain": [ 719 | " 0_a 0_c 0_g 0_t 1_a 1_c 1_g 1_t 2_a 2_c ... 55_a 55_c \\\n", 720 | "0 0 0 0 1 1 0 0 0 0 1 ... 0 0 \n", 721 | "1 0 0 0 1 0 0 1 0 0 1 ... 1 0 \n", 722 | "2 0 0 1 0 0 0 0 1 1 0 ... 0 1 \n", 723 | "3 1 0 0 0 1 0 0 0 0 0 ... 0 0 \n", 724 | "4 0 0 0 1 0 1 0 0 0 0 ... 1 0 \n", 725 | "\n", 726 | " 55_g 55_t 56_a 56_c 56_g 56_t Class_+ Class_- \n", 727 | "0 1 0 0 0 0 1 1 0 \n", 728 | "1 0 0 1 0 0 0 1 0 \n", 729 | "2 0 0 0 0 1 0 1 0 \n", 730 | "3 0 1 0 1 0 0 1 0 \n", 731 | "4 0 0 0 0 1 0 1 0 \n", 732 | "\n", 733 | "[5 rows x 230 columns]" 734 | ] 735 | }, 736 | "execution_count": 11, 737 | "metadata": {}, 738 | "output_type": "execute_result" 739 | } 740 | ], 741 | "source": [ 742 | "# Unfortunately, we can't run machine learning algorithms on the data in 'String' formats. As a result, we need to switch\n", 743 | "# it to numerical data. This can easily be accomplished using the pd.get_dummies() function\n", 744 | "numerical_df = pd.get_dummies(df)\n", 745 | "numerical_df.iloc[:5]" 746 | ] 747 | }, 748 | { 749 | "cell_type": "code", 750 | "execution_count": 12, 751 | "metadata": {}, 752 | "outputs": [ 753 | { 754 | "name": "stdout", 755 | "output_type": "stream", 756 | "text": [ 757 | " 0_a 0_c 0_g 0_t 1_a 1_c 1_g 1_t 2_a 2_c ... 54_t 55_a 55_c \\\n", 758 | "0 0 0 0 1 1 0 0 0 0 1 ... 0 0 0 \n", 759 | "1 0 0 0 1 0 0 1 0 0 1 ... 0 1 0 \n", 760 | "2 0 0 1 0 0 0 0 1 1 0 ... 0 0 1 \n", 761 | "3 1 0 0 0 1 0 0 0 0 0 ... 0 0 0 \n", 762 | "4 0 0 0 1 0 1 0 0 0 0 ... 1 1 0 \n", 763 | "\n", 764 | " 55_g 55_t 56_a 56_c 56_g 56_t Class \n", 765 | "0 1 0 0 0 0 1 1 \n", 766 | "1 0 0 1 0 0 0 1 \n", 767 | "2 0 0 0 0 1 0 1 \n", 768 | "3 0 1 0 1 0 0 1 \n", 769 | "4 0 0 0 0 1 0 1 \n", 770 | "\n", 771 | "[5 rows x 229 columns]\n" 772 | ] 773 | } 774 | ], 775 | "source": [ 776 | "# We don't need both class columns. Lets drop one then rename the other to simply 'Class'.\n", 777 | "df = numerical_df.drop(columns=['Class_-'])\n", 778 | "\n", 779 | "df.rename(columns = {'Class_+': 'Class'}, inplace = True)\n", 780 | "print(df.iloc[:5])" 781 | ] 782 | }, 783 | { 784 | "cell_type": "code", 785 | "execution_count": 13, 786 | "metadata": {}, 787 | "outputs": [], 788 | "source": [ 789 | "# Use the model_selection module to separate training and testing datasets\n", 790 | "from sklearn import model_selection\n", 791 | "\n", 792 | "# Create X and Y datasets for training\n", 793 | "X = np.array(df.drop(['Class'], 1))\n", 794 | "y = np.array(df['Class'])\n", 795 | "\n", 796 | "# define seed for reproducibility\n", 797 | "seed = 1\n", 798 | "\n", 799 | "# split data into training and testing datasets\n", 800 | "X_train, X_test, y_train, y_test = model_selection.train_test_split(X, y, test_size=0.25, random_state=seed)\n" 801 | ] 802 | }, 803 | { 804 | "cell_type": "markdown", 805 | "metadata": {}, 806 | "source": [ 807 | "## Step 3: Training and Testing the Classification Algorithms\n", 808 | "\n", 809 | "Now that we have preprocessed the data and built our training and testing datasets, we can start to deploy different classification algorithms. It's relatively easy to test multiple models; as a result, we will compare and contrast the performance of ten different algorithms." 810 | ] 811 | }, 812 | { 813 | "cell_type": "code", 814 | "execution_count": 14, 815 | "metadata": {}, 816 | "outputs": [ 817 | { 818 | "name": "stdout", 819 | "output_type": "stream", 820 | "text": [ 821 | "Nearest Neighbors: 0.823214 (0.113908)\n", 822 | "Gaussian Process: 0.873214 (0.056158)\n", 823 | "Decision Tree: 0.750000 (0.185405)\n", 824 | "Random Forest: 0.635714 (0.127325)\n" 825 | ] 826 | }, 827 | { 828 | "name": "stderr", 829 | "output_type": "stream", 830 | "text": [ 831 | "/Users/junaid/anaconda3/lib/python3.6/site-packages/sklearn/neural_network/multilayer_perceptron.py:564: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (200) reached and the optimization hasn't converged yet.\n", 832 | " % self.max_iter, ConvergenceWarning)\n", 833 | "/Users/junaid/anaconda3/lib/python3.6/site-packages/sklearn/neural_network/multilayer_perceptron.py:564: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (200) reached and the optimization hasn't converged yet.\n", 834 | " % self.max_iter, ConvergenceWarning)\n", 835 | "/Users/junaid/anaconda3/lib/python3.6/site-packages/sklearn/neural_network/multilayer_perceptron.py:564: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (200) reached and the optimization hasn't converged yet.\n", 836 | " % self.max_iter, ConvergenceWarning)\n", 837 | "/Users/junaid/anaconda3/lib/python3.6/site-packages/sklearn/neural_network/multilayer_perceptron.py:564: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (200) reached and the optimization hasn't converged yet.\n", 838 | " % self.max_iter, ConvergenceWarning)\n", 839 | "/Users/junaid/anaconda3/lib/python3.6/site-packages/sklearn/neural_network/multilayer_perceptron.py:564: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (200) reached and the optimization hasn't converged yet.\n", 840 | " % self.max_iter, ConvergenceWarning)\n", 841 | "/Users/junaid/anaconda3/lib/python3.6/site-packages/sklearn/neural_network/multilayer_perceptron.py:564: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (200) reached and the optimization hasn't converged yet.\n", 842 | " % self.max_iter, ConvergenceWarning)\n", 843 | "/Users/junaid/anaconda3/lib/python3.6/site-packages/sklearn/neural_network/multilayer_perceptron.py:564: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (200) reached and the optimization hasn't converged yet.\n", 844 | " % self.max_iter, ConvergenceWarning)\n", 845 | "/Users/junaid/anaconda3/lib/python3.6/site-packages/sklearn/neural_network/multilayer_perceptron.py:564: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (200) reached and the optimization hasn't converged yet.\n", 846 | " % self.max_iter, ConvergenceWarning)\n", 847 | "/Users/junaid/anaconda3/lib/python3.6/site-packages/sklearn/neural_network/multilayer_perceptron.py:564: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (200) reached and the optimization hasn't converged yet.\n", 848 | " % self.max_iter, ConvergenceWarning)\n", 849 | "/Users/junaid/anaconda3/lib/python3.6/site-packages/sklearn/neural_network/multilayer_perceptron.py:564: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (200) reached and the optimization hasn't converged yet.\n", 850 | " % self.max_iter, ConvergenceWarning)\n" 851 | ] 852 | }, 853 | { 854 | "name": "stdout", 855 | "output_type": "stream", 856 | "text": [ 857 | "Neural Net: 0.887500 (0.087500)\n", 858 | "AdaBoost: 0.925000 (0.114564)\n", 859 | "Naive Bayes: 0.837500 (0.137500)\n", 860 | "SVM Linear: 0.850000 (0.108972)\n", 861 | "SVM RBF: 0.737500 (0.117925)\n", 862 | "SVM Sigmoid: 0.569643 (0.159209)\n" 863 | ] 864 | } 865 | ], 866 | "source": [ 867 | "# Now that we have our dataset, we can start building algorithms! We'll need to import each algorithm we plan on using\n", 868 | "# from sklearn. We also need to import some performance metrics, such as accuracy_score and classification_report.\n", 869 | "\n", 870 | "from sklearn.neighbors import KNeighborsClassifier\n", 871 | "from sklearn.neural_network import MLPClassifier\n", 872 | "from sklearn.gaussian_process import GaussianProcessClassifier\n", 873 | "from sklearn.gaussian_process.kernels import RBF\n", 874 | "from sklearn.tree import DecisionTreeClassifier\n", 875 | "from sklearn.ensemble import RandomForestClassifier, AdaBoostClassifier\n", 876 | "from sklearn.naive_bayes import GaussianNB\n", 877 | "from sklearn.svm import SVC\n", 878 | "from sklearn.metrics import classification_report, accuracy_score\n", 879 | "\n", 880 | "# define scoring method\n", 881 | "scoring = 'accuracy'\n", 882 | "\n", 883 | "# Define models to train\n", 884 | "names = [\"Nearest Neighbors\", \"Gaussian Process\",\n", 885 | " \"Decision Tree\", \"Random Forest\", \"Neural Net\", \"AdaBoost\",\n", 886 | " \"Naive Bayes\", \"SVM Linear\", \"SVM RBF\", \"SVM Sigmoid\"]\n", 887 | "\n", 888 | "classifiers = [\n", 889 | " KNeighborsClassifier(n_neighbors = 3),\n", 890 | " GaussianProcessClassifier(1.0 * RBF(1.0)),\n", 891 | " DecisionTreeClassifier(max_depth=5),\n", 892 | " RandomForestClassifier(max_depth=5, n_estimators=10, max_features=1),\n", 893 | " MLPClassifier(alpha=1),\n", 894 | " AdaBoostClassifier(),\n", 895 | " GaussianNB(),\n", 896 | " SVC(kernel = 'linear'), \n", 897 | " SVC(kernel = 'rbf'),\n", 898 | " SVC(kernel = 'sigmoid')\n", 899 | "]\n", 900 | "\n", 901 | "models = zip(names, classifiers)\n", 902 | "\n", 903 | "# evaluate each model in turn\n", 904 | "results = []\n", 905 | "names = []\n", 906 | "\n", 907 | "for name, model in models:\n", 908 | " kfold = model_selection.KFold(n_splits=10, random_state = seed)\n", 909 | " cv_results = model_selection.cross_val_score(model, X_train, y_train, cv=kfold, scoring=scoring)\n", 910 | " results.append(cv_results)\n", 911 | " names.append(name)\n", 912 | " msg = \"%s: %f (%f)\" % (name, cv_results.mean(), cv_results.std())\n", 913 | " print(msg)" 914 | ] 915 | }, 916 | { 917 | "cell_type": "code", 918 | "execution_count": 16, 919 | "metadata": {}, 920 | "outputs": [], 921 | "source": [ 922 | "# Remember, performance on the training data is not that important. We want to know how well our algorithms\n", 923 | "# can generalize to new data. To test this, let's make predictions on the validation dataset.\n", 924 | "\n", 925 | "for name, model in models:\n", 926 | " model.fit(X_train, y_train)\n", 927 | " predictions = model.predict(X_test)\n", 928 | " print(name)\n", 929 | " print(accuracy_score(y_test, predictions))\n", 930 | " print(classification_report(y_test, predictions))\n", 931 | " \n", 932 | "# Accuracy - ratio of correctly predicted observation to the total observations. \n", 933 | "# Precision - (false positives) ratio of correctly predicted positive observations to the total predicted positive observations\n", 934 | "# Recall (Sensitivity) - (false negatives) ratio of correctly predicted positive observations to the all observations in actual class - yes.\n", 935 | "# F1 score - F1 Score is the weighted average of Precision and Recall. Therefore, this score takes both false positives and false " 936 | ] 937 | }, 938 | { 939 | "cell_type": "code", 940 | "execution_count": null, 941 | "metadata": {}, 942 | "outputs": [], 943 | "source": [] 944 | } 945 | ], 946 | "metadata": { 947 | "kernelspec": { 948 | "display_name": "Python 3", 949 | "language": "python", 950 | "name": "python3" 951 | }, 952 | "language_info": { 953 | "codemirror_mode": { 954 | "name": "ipython", 955 | "version": 3 956 | }, 957 | "file_extension": ".py", 958 | "mimetype": "text/x-python", 959 | "name": "python", 960 | "nbconvert_exporter": "python", 961 | "pygments_lexer": "ipython3", 962 | "version": "3.6.5" 963 | } 964 | }, 965 | "nbformat": 4, 966 | "nbformat_minor": 2 967 | } 968 | -------------------------------------------------------------------------------- /House_price_pridiction/read.md: -------------------------------------------------------------------------------- 1 | The goal of this project is to predict and make research to predict sales prices for homes using Data Science and finding best algorithm. 2 | Tools: 3 | Used Jupyter Notebook 4 | Libraries: 5 | Pandas — For handling structured data 6 | Scikit Learn — For machine learning 7 | NumPy — For linear algebra and mathematics 8 | Seaborn — For data visualization 9 | -------------------------------------------------------------------------------- /LICENSE: -------------------------------------------------------------------------------- 1 | Apache License 2 | Version 2.0, January 2004 3 | http://www.apache.org/licenses/ 4 | 5 | TERMS AND CONDITIONS FOR USE, REPRODUCTION, AND DISTRIBUTION 6 | 7 | 1. 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The text should be enclosed in the appropriate 184 | comment syntax for the file format. We also recommend that a 185 | file or class name and description of purpose be included on the 186 | same "printed page" as the copyright notice for easier 187 | identification within third-party archives. 188 | 189 | Copyright [yyyy] [name of copyright owner] 190 | 191 | Licensed under the Apache License, Version 2.0 (the "License"); 192 | you may not use this file except in compliance with the License. 193 | You may obtain a copy of the License at 194 | 195 | http://www.apache.org/licenses/LICENSE-2.0 196 | 197 | Unless required by applicable law or agreed to in writing, software 198 | distributed under the License is distributed on an "AS IS" BASIS, 199 | WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. 200 | See the License for the specific language governing permissions and 201 | limitations under the License. 202 | -------------------------------------------------------------------------------- /Prediction-And-Analysis-of-crime/read.md: -------------------------------------------------------------------------------- 1 | Analysing and predicting a crime using a systematic approach for identifying and analyzing patterns and trends in crime. 2 | This system can predict regions which have high probability for crime occurrence and can visualize crime prone areas. 3 | Basically this project is the part of the project safest path maps. Which is being developed to produce safest paths considering 4 | factors like: 5 | potholes 6 | street lights 7 | crime rate in the area etc. 8 | The goal is to provide safest path map. 9 | 10 | Tools used: Jupyter Notebook 11 | -------------------------------------------------------------------------------- /README.md: -------------------------------------------------------------------------------- 1 | # 100-Data-Science-Projects 2 | 100 Data Science projects for anybody who is interested in doing research and want to learn about data science by building projects and coding them. 3 | 4 | The goal is to make a repository where anyone can contribute their research,projects and solution that is useful for everyone and can help in their journey of learning Data Science. 5 | 6 | Instructions to contribute are as follows: 7 | 8 | Propose your project,research create an issue if you want write what you think should be added or whta you wanna work on. 9 | 10 | Then you or anyone can make the project on it or you can contribute your own research on that project. 11 | 12 | After that anyone will fork the repo, write up the code and solution, 13 | Then submit a pull request. 14 | 15 | The code and project will be scrutinized, reviewed, andthe suggestions will be made 16 | then the project will be included (merge) into the main project repo. 17 | 18 | Anybody can suggest any changes or improvements if needed , for this one has to create the issue for that particular thing 19 | 20 | The steps will be repeated till 100 research work or project are reached. 21 | --------------------------------------------------------------------------------