├── Decision Tree ├── ALGO.PNG ├── diabetes.png ├── decision_tree.png ├── README.md └── data │ └── diabetes.csv ├── Simple_Linear_Regression ├── House Prediction │ ├── homeprices.csv │ ├── Task │ │ ├── years.xlsx │ │ ├── years.csv │ │ └── canada_per_capita_income.csv │ ├── scatterplot.JPG │ ├── areas.csv │ ├── different_lines.JPG │ ├── error_equation.jpg │ ├── homepricetable.JPG │ ├── linear_equation.png │ └── prediction.csv ├── Error_Regression │ ├── mse,mae,rmse.pdf │ ├── r2adjustedr2.pdf │ └── placement.csv ├── Multi_linear_Regression │ └── data │ │ ├── equation.jpg │ │ ├── homeprices.jpg │ │ ├── home_equation.jpg │ │ ├── general_equation.jpg │ │ └── homeprices.csv ├── Regression_Model _Train_Test_Split │ └── data │ │ └── carprices.csv ├── data │ ├── Salary_Data.csv │ └── placement.csv └── Basic_Steps_ML.ipynb ├── Logistic_Regression ├── data │ ├── insurance_data.csv │ └── placement.csv └── logistic_regression_insurance.ipynb ├── Support Vector Machine └── data │ └── Position_Salaries.csv ├── Encoding └── data │ └── customer.csv ├── K-mean Clustering └── data │ └── student_clustering.csv ├── column_transformer └── data │ └── covid_toy.csv ├── Pycaret for classification └── data │ └── heart_disease.csv ├── Random Forest ├── Random forest │ └── data │ │ └── heart_disease.csv └── Bagging │ ├── data │ └── diabetes.csv │ └── bagging_diabetes_prediction.ipynb └── KNN └── data └── Social_Network_Ads.csv /Decision Tree/ALGO.PNG: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/SajidMajeed92/UGD-Machine-Learning/main/Decision Tree/ALGO.PNG -------------------------------------------------------------------------------- /Decision Tree/diabetes.png: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/SajidMajeed92/UGD-Machine-Learning/main/Decision Tree/diabetes.png -------------------------------------------------------------------------------- /Decision Tree/decision_tree.png: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/SajidMajeed92/UGD-Machine-Learning/main/Decision Tree/decision_tree.png -------------------------------------------------------------------------------- /Simple_Linear_Regression/House Prediction/homeprices.csv: -------------------------------------------------------------------------------- 1 | area,price 2 | 2600,550000 3 | 3000,565000 4 | 3200,610000 5 | 3600,680000 6 | 4000,725000 7 | -------------------------------------------------------------------------------- /Simple_Linear_Regression/Error_Regression/mse,mae,rmse.pdf: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/SajidMajeed92/UGD-Machine-Learning/main/Simple_Linear_Regression/Error_Regression/mse,mae,rmse.pdf -------------------------------------------------------------------------------- /Simple_Linear_Regression/Error_Regression/r2adjustedr2.pdf: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/SajidMajeed92/UGD-Machine-Learning/main/Simple_Linear_Regression/Error_Regression/r2adjustedr2.pdf -------------------------------------------------------------------------------- /Simple_Linear_Regression/House Prediction/Task/years.xlsx: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/SajidMajeed92/UGD-Machine-Learning/main/Simple_Linear_Regression/House Prediction/Task/years.xlsx -------------------------------------------------------------------------------- /Simple_Linear_Regression/House Prediction/scatterplot.JPG: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/SajidMajeed92/UGD-Machine-Learning/main/Simple_Linear_Regression/House Prediction/scatterplot.JPG -------------------------------------------------------------------------------- /Simple_Linear_Regression/House Prediction/areas.csv: -------------------------------------------------------------------------------- 1 | area 2 | 1000 3 | 1500 4 | 2300 5 | 3540 6 | 4120 7 | 4560 8 | 5490 9 | 3460 10 | 4750 11 | 2300 12 | 9000 13 | 8600 14 | 7100 15 | -------------------------------------------------------------------------------- /Simple_Linear_Regression/House Prediction/different_lines.JPG: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/SajidMajeed92/UGD-Machine-Learning/main/Simple_Linear_Regression/House Prediction/different_lines.JPG -------------------------------------------------------------------------------- /Simple_Linear_Regression/House Prediction/error_equation.jpg: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/SajidMajeed92/UGD-Machine-Learning/main/Simple_Linear_Regression/House Prediction/error_equation.jpg -------------------------------------------------------------------------------- /Simple_Linear_Regression/House Prediction/homepricetable.JPG: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/SajidMajeed92/UGD-Machine-Learning/main/Simple_Linear_Regression/House Prediction/homepricetable.JPG -------------------------------------------------------------------------------- /Simple_Linear_Regression/House Prediction/linear_equation.png: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/SajidMajeed92/UGD-Machine-Learning/main/Simple_Linear_Regression/House Prediction/linear_equation.png -------------------------------------------------------------------------------- /Simple_Linear_Regression/Multi_linear_Regression/data/equation.jpg: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/SajidMajeed92/UGD-Machine-Learning/main/Simple_Linear_Regression/Multi_linear_Regression/data/equation.jpg -------------------------------------------------------------------------------- /Simple_Linear_Regression/Multi_linear_Regression/data/homeprices.jpg: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/SajidMajeed92/UGD-Machine-Learning/main/Simple_Linear_Regression/Multi_linear_Regression/data/homeprices.jpg -------------------------------------------------------------------------------- /Simple_Linear_Regression/Multi_linear_Regression/data/home_equation.jpg: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/SajidMajeed92/UGD-Machine-Learning/main/Simple_Linear_Regression/Multi_linear_Regression/data/home_equation.jpg -------------------------------------------------------------------------------- /Simple_Linear_Regression/Multi_linear_Regression/data/general_equation.jpg: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/SajidMajeed92/UGD-Machine-Learning/main/Simple_Linear_Regression/Multi_linear_Regression/data/general_equation.jpg -------------------------------------------------------------------------------- /Simple_Linear_Regression/Multi_linear_Regression/data/homeprices.csv: -------------------------------------------------------------------------------- 1 | area,bedrooms,age,price 2 | 2600,3,20,550000 3 | 3000,4,15,565000 4 | 3200,,18,610000 5 | 3600,3,30,595000 6 | 4000,5,8,760000 7 | 4100,6,8,810000 8 | -------------------------------------------------------------------------------- /Decision Tree/README.md: -------------------------------------------------------------------------------- 1 | 1- For Downloading Graphviz Library 2 | https://www.npackd.org/p/org.graphviz.Graphviz/2.38 3 | 4 | 2- After Installation Add path to environment variable 5 | "C:\Program Files (x86)\Graphviz2.38\bin" -------------------------------------------------------------------------------- /Logistic_Regression/data/insurance_data.csv: -------------------------------------------------------------------------------- 1 | age,bought_insurance 2 | 22,0 3 | 25,0 4 | 47,1 5 | 52,0 6 | 46,1 7 | 56,1 8 | 55,0 9 | 60,1 10 | 62,1 11 | 61,1 12 | 18,0 13 | 28,0 14 | 27,0 15 | 29,0 16 | 49,1 17 | 55,1 18 | 25,1 19 | 58,1 20 | 19,0 21 | 18,0 22 | 21,0 23 | 26,0 24 | 40,1 25 | 45,1 26 | 50,1 27 | 54,1 28 | 23,0 -------------------------------------------------------------------------------- /Simple_Linear_Regression/House Prediction/Task/years.csv: -------------------------------------------------------------------------------- 1 | year 2 | 1978 3 | 1979 4 | 1980 5 | 1981 6 | 1982 7 | 1992 8 | 1993 9 | 1994 10 | 1995 11 | 1996 12 | 1997 13 | 1998 14 | 1999 15 | 2000 16 | 2001 17 | 2002 18 | 2011 19 | 2012 20 | 2013 21 | 2014 22 | 2015 23 | 2016 24 | 2017 25 | 2018 26 | 2019 27 | 2020 28 | 2021 29 | -------------------------------------------------------------------------------- /Support Vector Machine/data/Position_Salaries.csv: -------------------------------------------------------------------------------- 1 | Position,Level,Salary 2 | Business Analyst,1,45000 3 | Junior Consultant,2,50000 4 | Senior Consultant,3,60000 5 | Manager,4,80000 6 | Country Manager,5,110000 7 | Region Manager,6,150000 8 | Partner,7,200000 9 | Senior Partner,8,300000 10 | C-level,9,500000 11 | CEO,10,1000000 -------------------------------------------------------------------------------- /Simple_Linear_Regression/House Prediction/prediction.csv: -------------------------------------------------------------------------------- 1 | ,area,prices 2 | 0,1000,316404.1095890411 3 | 1,1500,384297.9452054794 4 | 2,2300,492928.0821917808 5 | 3,3540,661304.794520548 6 | 4,4120,740061.6438356165 7 | 5,4560,799808.2191780822 8 | 6,5490,926090.7534246575 9 | 7,3460,650441.7808219178 10 | 8,4750,825607.8767123288 11 | 9,2300,492928.0821917808 12 | 10,9000,1402705.479452055 13 | 11,8600,1348390.4109589043 14 | 12,7100,1144708.904109589 15 | -------------------------------------------------------------------------------- /Simple_Linear_Regression/Regression_Model _Train_Test_Split/data/carprices.csv: -------------------------------------------------------------------------------- 1 | Mileage,Age(yrs),Sell Price($) 2 | 69000,6,18000 3 | 35000,3,34000 4 | 57000,5,26100 5 | 22500,2,40000 6 | 46000,4,31500 7 | 59000,5,26750 8 | 52000,5,32000 9 | 72000,6,19300 10 | 91000,8,12000 11 | 67000,6,22000 12 | 83000,7,18700 13 | 79000,7,19500 14 | 59000,5,26000 15 | 58780,4,27500 16 | 82450,7,19400 17 | 25400,3,35000 18 | 28000,2,35500 19 | 69000,5,19700 20 | 87600,8,12800 21 | 52000,5,28200 22 | -------------------------------------------------------------------------------- /Simple_Linear_Regression/data/Salary_Data.csv: -------------------------------------------------------------------------------- 1 | YearsExperience,Salary 2 | 1.1,39343.00 3 | 1.3,46205.00 4 | 1.5,37731.00 5 | 2.0,43525.00 6 | 2.2,39891.00 7 | 2.9,56642.00 8 | 3.0,60150.00 9 | 3.2,54445.00 10 | 3.2,64445.00 11 | 3.7,57189.00 12 | 3.9,63218.00 13 | 4.0,55794.00 14 | 4.0,56957.00 15 | 4.1,57081.00 16 | 4.5,61111.00 17 | 4.9,67938.00 18 | 5.1,66029.00 19 | 5.3,83088.00 20 | 5.9,81363.00 21 | 6.0,93940.00 22 | 6.8,91738.00 23 | 7.1,98273.00 24 | 7.9,101302.00 25 | 8.2,113812.00 26 | 8.7,109431.00 27 | 9.0,105582.00 28 | 9.5,116969.00 29 | 9.6,112635.00 30 | 10.3,122391.00 31 | 10.5,121872.00 32 | -------------------------------------------------------------------------------- /Simple_Linear_Regression/House Prediction/Task/canada_per_capita_income.csv: -------------------------------------------------------------------------------- 1 | year,per capita income (US$) 2 | 1970,3399.299037 3 | 1971,3768.297935 4 | 1972,4251.175484 5 | 1973,4804.463248 6 | 1974,5576.514583 7 | 1975,5998.144346 8 | 1976,7062.131392 9 | 1977,7100.12617 10 | 1978,7247.967035 11 | 1979,7602.912681 12 | 1980,8355.96812 13 | 1981,9434.390652 14 | 1982,9619.438377 15 | 1983,10416.53659 16 | 1984,10790.32872 17 | 1985,11018.95585 18 | 1986,11482.89153 19 | 1987,12974.80662 20 | 1988,15080.28345 21 | 1989,16426.72548 22 | 1990,16838.6732 23 | 1991,17266.09769 24 | 1992,16412.08309 25 | 1993,15875.58673 26 | 1994,15755.82027 27 | 1995,16369.31725 28 | 1996,16699.82668 29 | 1997,17310.75775 30 | 1998,16622.67187 31 | 1999,17581.02414 32 | 2000,18987.38241 33 | 2001,18601.39724 34 | 2002,19232.17556 35 | 2003,22739.42628 36 | 2004,25719.14715 37 | 2005,29198.05569 38 | 2006,32738.2629 39 | 2007,36144.48122 40 | 2008,37446.48609 41 | 2009,32755.17682 42 | 2010,38420.52289 43 | 2011,42334.71121 44 | 2012,42665.25597 45 | 2013,42676.46837 46 | 2014,41039.8936 47 | 2015,35175.18898 48 | 2016,34229.19363 49 | -------------------------------------------------------------------------------- /Simple_Linear_Regression/Basic_Steps_ML.ipynb: -------------------------------------------------------------------------------- 1 | { 2 | "cells": [ 3 | { 4 | "cell_type": "markdown", 5 | "metadata": {}, 6 | "source": [ 7 | "## Steps For Solving Machine Learning Task\n", 8 | "\n", 9 | "#### 0. Preprocess + EDA + Feature Selection\n", 10 | "#### 1. Extract input and output cols\n", 11 | "#### 2. Scale the values\n", 12 | "#### 3. Train test split\n", 13 | "#### 4. Train the model\n", 14 | "#### 5. Evaluate the model/model selection\n", 15 | "#### 6. Deploy the model" 16 | ] 17 | }, 18 | { 19 | "cell_type": "markdown", 20 | "metadata": {}, 21 | "source": [] 22 | } 23 | ], 24 | "metadata": { 25 | "kernelspec": { 26 | "display_name": "Python 3", 27 | "language": "python", 28 | "name": "python3" 29 | }, 30 | "language_info": { 31 | "codemirror_mode": { 32 | "name": "ipython", 33 | "version": 3 34 | }, 35 | "file_extension": ".py", 36 | "mimetype": "text/x-python", 37 | "name": "python", 38 | "nbconvert_exporter": "python", 39 | "pygments_lexer": "ipython3", 40 | "version": "3.12.0" 41 | } 42 | }, 43 | "nbformat": 4, 44 | "nbformat_minor": 2 45 | } 46 | -------------------------------------------------------------------------------- /Encoding/data/customer.csv: -------------------------------------------------------------------------------- 1 | age,gender,review,education,purchased 2 | 30,Female,Average,School,No 3 | 68,Female,Poor,UG,No 4 | 70,Female,Good,PG,No 5 | 72,Female,Good,PG,No 6 | 16,Female,Average,UG,No 7 | 31,Female,Average,School,Yes 8 | 18,Male,Good,School,No 9 | 60,Female,Poor,School,Yes 10 | 65,Female,Average,UG,No 11 | 74,Male,Good,UG,Yes 12 | 98,Female,Good,UG,Yes 13 | 74,Male,Good,UG,Yes 14 | 51,Male,Poor,School,No 15 | 57,Female,Average,School,No 16 | 15,Male,Poor,PG,Yes 17 | 75,Male,Poor,UG,No 18 | 59,Male,Poor,UG,Yes 19 | 22,Female,Poor,UG,Yes 20 | 19,Male,Good,School,No 21 | 97,Male,Poor,PG,Yes 22 | 57,Female,Average,School,Yes 23 | 32,Male,Average,PG,No 24 | 18,Female,Poor,PG,Yes 25 | 96,Female,Good,School,No 26 | 16,Female,Average,PG,Yes 27 | 57,Female,Good,School,No 28 | 53,Female,Poor,PG,No 29 | 69,Female,Poor,PG,No 30 | 48,Male,Poor,School,No 31 | 83,Female,Average,UG,Yes 32 | 73,Male,Average,UG,No 33 | 22,Female,Poor,School,Yes 34 | 92,Male,Average,UG,Yes 35 | 89,Female,Good,PG,Yes 36 | 86,Male,Average,School,No 37 | 74,Male,Poor,School,Yes 38 | 34,Female,Good,UG,Yes 39 | 94,Male,Average,PG,Yes 40 | 45,Female,Good,School,No 41 | 76,Male,Poor,PG,No 42 | 39,Male,Good,School,No 43 | 23,Male,Good,PG,Yes 44 | 30,Female,Good,PG,Yes 45 | 27,Male,Poor,PG,No 46 | 77,Female,Average,UG,No 47 | 61,Male,Poor,PG,Yes 48 | 64,Female,Poor,PG,No 49 | 38,Female,Good,PG,Yes 50 | 39,Female,Good,UG,Yes 51 | 25,Female,Good,UG,No 52 | -------------------------------------------------------------------------------- /Logistic_Regression/data/placement.csv: -------------------------------------------------------------------------------- 1 | ,cgpa,iq,placement 2 | 0,6.8,123.0,1 3 | 1,5.9,106.0,0 4 | 2,5.3,121.0,0 5 | 3,7.4,132.0,1 6 | 4,5.8,142.0,0 7 | 5,7.1,48.0,1 8 | 6,5.7,143.0,0 9 | 7,5.0,63.0,0 10 | 8,6.1,156.0,0 11 | 9,5.1,66.0,0 12 | 10,6.0,45.0,1 13 | 11,6.9,138.0,1 14 | 12,5.4,139.0,0 15 | 13,6.4,116.0,1 16 | 14,6.1,103.0,0 17 | 15,5.1,176.0,0 18 | 16,5.2,224.0,0 19 | 17,3.3,183.0,0 20 | 18,4.0,100.0,0 21 | 19,5.2,132.0,0 22 | 20,6.6,120.0,1 23 | 21,7.1,151.0,1 24 | 22,4.9,120.0,0 25 | 23,4.7,87.0,0 26 | 24,4.7,121.0,0 27 | 25,5.0,91.0,0 28 | 26,7.0,199.0,1 29 | 27,6.0,124.0,1 30 | 28,5.2,90.0,0 31 | 29,7.0,112.0,1 32 | 30,7.6,128.0,1 33 | 31,3.9,109.0,0 34 | 32,7.0,139.0,1 35 | 33,6.0,149.0,0 36 | 34,4.8,163.0,0 37 | 35,6.8,90.0,1 38 | 36,5.7,140.0,0 39 | 37,8.1,149.0,1 40 | 38,6.5,160.0,1 41 | 39,4.6,146.0,0 42 | 40,4.9,134.0,0 43 | 41,5.4,114.0,0 44 | 42,7.6,89.0,1 45 | 43,6.8,141.0,1 46 | 44,7.5,61.0,1 47 | 45,6.0,66.0,1 48 | 46,5.3,114.0,0 49 | 47,5.2,161.0,0 50 | 48,6.6,138.0,1 51 | 49,5.4,135.0,0 52 | 50,3.5,233.0,0 53 | 51,4.8,141.0,0 54 | 52,7.0,175.0,1 55 | 53,8.3,168.0,1 56 | 54,6.4,141.0,1 57 | 55,7.8,114.0,1 58 | 56,6.1,65.0,0 59 | 57,6.5,130.0,1 60 | 58,8.0,79.0,1 61 | 59,4.8,112.0,0 62 | 60,6.9,139.0,1 63 | 61,7.3,137.0,1 64 | 62,6.0,102.0,0 65 | 63,6.3,128.0,1 66 | 64,7.0,64.0,1 67 | 65,8.1,166.0,1 68 | 66,6.9,96.0,1 69 | 67,5.0,118.0,0 70 | 68,4.0,75.0,0 71 | 69,8.5,120.0,1 72 | 70,6.3,127.0,1 73 | 71,6.1,132.0,1 74 | 72,7.3,116.0,1 75 | 73,4.9,61.0,0 76 | 74,6.7,154.0,1 77 | 75,4.8,169.0,0 78 | 76,4.9,155.0,0 79 | 77,7.3,50.0,1 80 | 78,6.1,81.0,0 81 | 79,6.5,90.0,1 82 | 80,4.9,196.0,0 83 | 81,5.4,107.0,0 84 | 82,6.5,37.0,1 85 | 83,7.5,130.0,1 86 | 84,5.7,169.0,0 87 | 85,5.8,166.0,1 88 | 86,5.1,128.0,0 89 | 87,5.7,132.0,1 90 | 88,4.4,149.0,0 91 | 89,4.9,151.0,0 92 | 90,7.3,86.0,1 93 | 91,7.5,158.0,1 94 | 92,5.2,110.0,0 95 | 93,6.8,112.0,1 96 | 94,4.7,52.0,0 97 | 95,4.3,200.0,0 98 | 96,4.4,42.0,0 99 | 97,6.7,182.0,1 100 | 98,6.3,103.0,1 101 | 99,6.2,113.0,1 102 | -------------------------------------------------------------------------------- /K-mean Clustering/data/student_clustering.csv: -------------------------------------------------------------------------------- 1 | cgpa,iq 2 | 5.13,88 3 | 5.9,113 4 | 8.36,93 5 | 8.27,97 6 | 5.45,110 7 | 5.88,109 8 | 8.41,98 9 | 8.8,115 10 | 5.79,110 11 | 8.09,94 12 | 4.6,86 13 | 6.1,110 14 | 8.16,97 15 | 5.0,88 16 | 5.71,108 17 | 8.31,95 18 | 5.5,111 19 | 7.87,91 20 | 6.05,111 21 | 5.84,113 22 | 7.47,98 23 | 4.86,86 24 | 7.78,92 25 | 4.78,87 26 | 4.96,88 27 | 7.93,98 28 | 4.86,87 29 | 9.18,119 30 | 8.04,94 31 | 5.43,106 32 | 8.86,117 33 | 6.01,112 34 | 8.83,118 35 | 5.32,106 36 | 7.77,96 37 | 8.0,96 38 | 8.56,118 39 | 5.91,108 40 | 5.44,84 41 | 5.57,113 42 | 5.34,85 43 | 8.43,96 44 | 8.02,93 45 | 5.31,86 46 | 8.96,116 47 | 8.78,116 48 | 8.14,94 49 | 6.4,108 50 | 8.45,119 51 | 5.67,109 52 | 5.14,83 53 | 4.95,86 54 | 8.79,116 55 | 8.12,96 56 | 8.81,115 57 | 6.05,108 58 | 5.85,111 59 | 8.88,115 60 | 5.87,109 61 | 9.07,117 62 | 6.02,104 63 | 8.34,96 64 | 8.65,95 65 | 8.92,118 66 | 5.21,87 67 | 8.75,113 68 | 8.53,93 69 | 4.91,85 70 | 5.77,111 71 | 8.29,95 72 | 6.06,109 73 | 8.71,116 74 | 7.93,94 75 | 5.28,83 76 | 5.55,109 77 | 8.86,118 78 | 5.81,112 79 | 9.3,117 80 | 5.15,88 81 | 8.72,92 82 | 8.14,91 83 | 9.01,121 84 | 5.47,111 85 | 4.9,85 86 | 8.97,116 87 | 4.89,88 88 | 9.0,117 89 | 5.74,109 90 | 8.76,117 91 | 5.8,108 92 | 8.78,117 93 | 9.23,114 94 | 8.2,92 95 | 5.05,86 96 | 8.67,95 97 | 8.18,94 98 | 9.03,118 99 | 8.61,95 100 | 4.98,91 101 | 9.13,118 102 | 5.88,110 103 | 5.01,86 104 | 4.95,88 105 | 8.91,119 106 | 4.96,89 107 | 4.85,86 108 | 7.99,92 109 | 4.76,90 110 | 8.98,118 111 | 9.03,118 112 | 8.08,94 113 | 8.86,117 114 | 5.91,109 115 | 5.67,111 116 | 8.26,91 117 | 8.89,118 118 | 8.25,95 119 | 5.74,108 120 | 8.97,117 121 | 4.98,87 122 | 4.78,87 123 | 5.69,109 124 | 8.4,93 125 | 8.72,119 126 | 7.84,97 127 | 5.2,85 128 | 8.08,98 129 | 6.05,109 130 | 5.05,87 131 | 8.25,96 132 | 8.3,93 133 | 6.14,111 134 | 5.01,83 135 | 4.77,86 136 | 5.74,112 137 | 8.93,118 138 | 5.94,109 139 | 4.68,87 140 | 7.9,100 141 | 7.97,96 142 | 8.21,94 143 | 4.81,85 144 | 5.86,111 145 | 5.03,87 146 | 4.98,87 147 | 8.58,118 148 | 5.32,88 149 | 8.94,117 150 | 6.38,107 151 | 4.86,88 152 | 8.6,117 153 | 4.89,85 154 | 8.77,117 155 | 8.81,116 156 | 4.88,86 157 | 8.23,95 158 | 6.61,111 159 | 8.54,118 160 | 6.04,110 161 | 8.35,93 162 | 5.01,86 163 | 8.97,119 164 | 6.24,108 165 | 8.33,92 166 | 8.91,117 167 | 4.67,86 168 | 6.1,109 169 | 5.15,85 170 | 4.97,88 171 | 8.68,119 172 | 9.06,120 173 | 5.8,110 174 | 8.9,117 175 | 4.87,88 176 | 5.2,89 177 | 8.46,98 178 | 8.94,115 179 | 5.87,108 180 | 4.99,88 181 | 8.91,115 182 | 8.91,117 183 | 5.97,108 184 | 6.17,110 185 | 6.01,107 186 | 7.89,96 187 | 4.79,88 188 | 7.91,93 189 | 8.23,91 190 | 8.95,116 191 | 6.33,111 192 | 8.4,93 193 | 8.44,94 194 | 4.76,89 195 | 4.78,85 196 | 8.79,96 197 | 4.68,89 198 | 8.57,118 199 | 5.85,112 200 | 6.23,108 201 | 8.82,117 202 | -------------------------------------------------------------------------------- /Simple_Linear_Regression/data/placement.csv: -------------------------------------------------------------------------------- 1 | cgpa,package 2 | 6.89,3.26 3 | 5.12,1.98 4 | 7.82,3.25 5 | 7.42,3.67 6 | 6.94,3.57 7 | 7.89,2.99 8 | 6.73,2.6 9 | 6.75,2.48 10 | 6.09,2.31 11 | 8.31,3.51 12 | 5.32,1.86 13 | 6.61,2.6 14 | 8.94,3.65 15 | 6.93,2.89 16 | 7.73,3.42 17 | 7.25,3.23 18 | 6.84,2.35 19 | 5.38,2.09 20 | 6.94,2.98 21 | 7.48,2.83 22 | 7.28,3.16 23 | 6.85,2.93 24 | 6.14,2.3 25 | 6.19,2.48 26 | 6.53,2.71 27 | 7.28,3.65 28 | 8.31,3.42 29 | 5.42,2.16 30 | 5.94,2.24 31 | 7.15,3.49 32 | 7.36,3.26 33 | 8.1,3.89 34 | 6.96,3.08 35 | 6.35,2.73 36 | 7.34,3.42 37 | 6.87,2.87 38 | 5.99,2.84 39 | 5.9,2.43 40 | 8.62,4.36 41 | 7.43,3.33 42 | 9.38,4.02 43 | 6.89,2.7 44 | 5.95,2.54 45 | 7.66,2.76 46 | 5.09,1.86 47 | 7.87,3.58 48 | 6.07,2.26 49 | 5.84,3.26 50 | 8.63,4.09 51 | 8.87,4.62 52 | 9.58,4.43 53 | 9.26,3.79 54 | 8.37,4.11 55 | 6.47,2.61 56 | 6.86,3.09 57 | 8.2,3.39 58 | 5.84,2.74 59 | 6.6,1.94 60 | 6.92,3.09 61 | 7.56,3.31 62 | 5.61,2.19 63 | 5.48,1.61 64 | 6.34,2.09 65 | 9.16,4.25 66 | 7.36,2.92 67 | 7.6,3.81 68 | 5.11,1.63 69 | 6.51,2.89 70 | 7.56,2.99 71 | 7.3,2.94 72 | 5.79,2.35 73 | 7.47,3.34 74 | 7.78,3.62 75 | 8.44,4.03 76 | 6.85,3.44 77 | 6.97,3.28 78 | 6.94,3.15 79 | 8.99,4.6 80 | 6.59,2.21 81 | 7.18,3.0 82 | 7.63,3.44 83 | 6.1,2.2 84 | 5.58,2.17 85 | 8.44,3.49 86 | 4.26,1.53 87 | 4.79,1.48 88 | 7.61,2.77 89 | 8.09,3.55 90 | 4.73,1.48 91 | 6.42,2.72 92 | 7.11,2.66 93 | 6.22,2.14 94 | 7.9,4.0 95 | 6.79,3.08 96 | 5.83,2.42 97 | 6.63,2.79 98 | 7.11,2.61 99 | 5.98,2.84 100 | 7.69,3.83 101 | 6.61,3.24 102 | 7.95,4.14 103 | 6.71,3.52 104 | 5.13,1.37 105 | 7.05,3.0 106 | 7.62,3.74 107 | 6.66,2.82 108 | 6.13,2.19 109 | 6.33,2.59 110 | 7.76,3.54 111 | 7.77,4.06 112 | 8.18,3.76 113 | 5.42,2.25 114 | 8.58,4.1 115 | 6.94,2.37 116 | 5.84,1.87 117 | 8.35,4.21 118 | 9.04,3.33 119 | 7.12,2.99 120 | 7.4,2.88 121 | 7.39,2.65 122 | 5.23,1.73 123 | 6.5,3.02 124 | 5.12,2.01 125 | 5.1,2.3 126 | 6.06,2.31 127 | 7.33,3.16 128 | 5.91,2.6 129 | 6.78,3.11 130 | 7.93,3.34 131 | 7.29,3.12 132 | 6.68,2.49 133 | 6.37,2.01 134 | 5.84,2.48 135 | 6.05,2.58 136 | 7.2,2.83 137 | 6.1,2.6 138 | 5.64,2.1 139 | 7.14,3.13 140 | 7.91,3.89 141 | 7.19,2.4 142 | 7.91,3.15 143 | 6.76,3.18 144 | 6.93,3.04 145 | 4.85,1.54 146 | 6.17,2.42 147 | 5.84,2.18 148 | 6.07,2.46 149 | 5.66,2.21 150 | 7.57,3.4 151 | 8.28,3.67 152 | 6.3,2.73 153 | 6.12,2.76 154 | 7.37,3.08 155 | 7.94,3.99 156 | 7.08,2.85 157 | 6.98,3.09 158 | 7.38,3.13 159 | 6.47,2.7 160 | 5.95,3.04 161 | 8.71,4.08 162 | 7.13,2.93 163 | 7.3,3.33 164 | 5.53,2.55 165 | 8.93,3.91 166 | 9.06,3.82 167 | 8.21,4.08 168 | 8.6,3.98 169 | 8.13,3.6 170 | 8.65,3.52 171 | 9.31,4.37 172 | 6.22,2.87 173 | 8.01,3.76 174 | 6.93,2.51 175 | 6.75,2.56 176 | 7.32,2.99 177 | 7.04,3.5 178 | 6.29,3.23 179 | 7.09,3.64 180 | 8.15,3.63 181 | 7.14,3.03 182 | 6.19,2.72 183 | 8.22,3.89 184 | 5.88,2.08 185 | 7.28,2.72 186 | 7.88,3.14 187 | 6.31,3.18 188 | 7.84,3.47 189 | 6.26,2.44 190 | 7.35,3.08 191 | 8.11,4.06 192 | 6.19,2.69 193 | 7.28,3.48 194 | 8.25,3.75 195 | 4.57,1.94 196 | 7.89,3.67 197 | 6.93,2.46 198 | 5.89,2.57 199 | 7.21,3.24 200 | 7.63,3.96 201 | 6.22,2.33 202 | -------------------------------------------------------------------------------- /Simple_Linear_Regression/Error_Regression/placement.csv: -------------------------------------------------------------------------------- 1 | cgpa,package 2 | 6.89,3.26 3 | 5.12,1.98 4 | 7.82,3.25 5 | 7.42,3.67 6 | 6.94,3.57 7 | 7.89,2.99 8 | 6.73,2.6 9 | 6.75,2.48 10 | 6.09,2.31 11 | 8.31,3.51 12 | 5.32,1.86 13 | 6.61,2.6 14 | 8.94,3.65 15 | 6.93,2.89 16 | 7.73,3.42 17 | 7.25,3.23 18 | 6.84,2.35 19 | 5.38,2.09 20 | 6.94,2.98 21 | 7.48,2.83 22 | 7.28,3.16 23 | 6.85,2.93 24 | 6.14,2.3 25 | 6.19,2.48 26 | 6.53,2.71 27 | 7.28,3.65 28 | 8.31,3.42 29 | 5.42,2.16 30 | 5.94,2.24 31 | 7.15,3.49 32 | 7.36,3.26 33 | 8.1,3.89 34 | 6.96,3.08 35 | 6.35,2.73 36 | 7.34,3.42 37 | 6.87,2.87 38 | 5.99,2.84 39 | 5.9,2.43 40 | 8.62,4.36 41 | 7.43,3.33 42 | 9.38,4.02 43 | 6.89,2.7 44 | 5.95,2.54 45 | 7.66,2.76 46 | 5.09,1.86 47 | 7.87,3.58 48 | 6.07,2.26 49 | 5.84,3.26 50 | 8.63,4.09 51 | 8.87,4.62 52 | 9.58,4.43 53 | 9.26,3.79 54 | 8.37,4.11 55 | 6.47,2.61 56 | 6.86,3.09 57 | 8.2,3.39 58 | 5.84,2.74 59 | 6.6,1.94 60 | 6.92,3.09 61 | 7.56,3.31 62 | 5.61,2.19 63 | 5.48,1.61 64 | 6.34,2.09 65 | 9.16,4.25 66 | 7.36,2.92 67 | 7.6,3.81 68 | 5.11,1.63 69 | 6.51,2.89 70 | 7.56,2.99 71 | 7.3,2.94 72 | 5.79,2.35 73 | 7.47,3.34 74 | 7.78,3.62 75 | 8.44,4.03 76 | 6.85,3.44 77 | 6.97,3.28 78 | 6.94,3.15 79 | 8.99,4.6 80 | 6.59,2.21 81 | 7.18,3.0 82 | 7.63,3.44 83 | 6.1,2.2 84 | 5.58,2.17 85 | 8.44,3.49 86 | 4.26,1.53 87 | 4.79,1.48 88 | 7.61,2.77 89 | 8.09,3.55 90 | 4.73,1.48 91 | 6.42,2.72 92 | 7.11,2.66 93 | 6.22,2.14 94 | 7.9,4.0 95 | 6.79,3.08 96 | 5.83,2.42 97 | 6.63,2.79 98 | 7.11,2.61 99 | 5.98,2.84 100 | 7.69,3.83 101 | 6.61,3.24 102 | 7.95,4.14 103 | 6.71,3.52 104 | 5.13,1.37 105 | 7.05,3.0 106 | 7.62,3.74 107 | 6.66,2.82 108 | 6.13,2.19 109 | 6.33,2.59 110 | 7.76,3.54 111 | 7.77,4.06 112 | 8.18,3.76 113 | 5.42,2.25 114 | 8.58,4.1 115 | 6.94,2.37 116 | 5.84,1.87 117 | 8.35,4.21 118 | 9.04,3.33 119 | 7.12,2.99 120 | 7.4,2.88 121 | 7.39,2.65 122 | 5.23,1.73 123 | 6.5,3.02 124 | 5.12,2.01 125 | 5.1,2.3 126 | 6.06,2.31 127 | 7.33,3.16 128 | 5.91,2.6 129 | 6.78,3.11 130 | 7.93,3.34 131 | 7.29,3.12 132 | 6.68,2.49 133 | 6.37,2.01 134 | 5.84,2.48 135 | 6.05,2.58 136 | 7.2,2.83 137 | 6.1,2.6 138 | 5.64,2.1 139 | 7.14,3.13 140 | 7.91,3.89 141 | 7.19,2.4 142 | 7.91,3.15 143 | 6.76,3.18 144 | 6.93,3.04 145 | 4.85,1.54 146 | 6.17,2.42 147 | 5.84,2.18 148 | 6.07,2.46 149 | 5.66,2.21 150 | 7.57,3.4 151 | 8.28,3.67 152 | 6.3,2.73 153 | 6.12,2.76 154 | 7.37,3.08 155 | 7.94,3.99 156 | 7.08,2.85 157 | 6.98,3.09 158 | 7.38,3.13 159 | 6.47,2.7 160 | 5.95,3.04 161 | 8.71,4.08 162 | 7.13,2.93 163 | 7.3,3.33 164 | 5.53,2.55 165 | 8.93,3.91 166 | 9.06,3.82 167 | 8.21,4.08 168 | 8.6,3.98 169 | 8.13,3.6 170 | 8.65,3.52 171 | 9.31,4.37 172 | 6.22,2.87 173 | 8.01,3.76 174 | 6.93,2.51 175 | 6.75,2.56 176 | 7.32,2.99 177 | 7.04,3.5 178 | 6.29,3.23 179 | 7.09,3.64 180 | 8.15,3.63 181 | 7.14,3.03 182 | 6.19,2.72 183 | 8.22,3.89 184 | 5.88,2.08 185 | 7.28,2.72 186 | 7.88,3.14 187 | 6.31,3.18 188 | 7.84,3.47 189 | 6.26,2.44 190 | 7.35,3.08 191 | 8.11,4.06 192 | 6.19,2.69 193 | 7.28,3.48 194 | 8.25,3.75 195 | 4.57,1.94 196 | 7.89,3.67 197 | 6.93,2.46 198 | 5.89,2.57 199 | 7.21,3.24 200 | 7.63,3.96 201 | 6.22,2.33 202 | -------------------------------------------------------------------------------- /column_transformer/data/covid_toy.csv: -------------------------------------------------------------------------------- 1 | age,gender,fever,cough,city,has_covid 2 | 60,Male,103.0,Mild,Kolkata,No 3 | 27,Male,100.0,Mild,Delhi,Yes 4 | 42,Male,101.0,Mild,Delhi,No 5 | 31,Female,98.0,Mild,Kolkata,No 6 | 65,Female,101.0,Mild,Mumbai,No 7 | 84,Female,,Mild,Bangalore,Yes 8 | 14,Male,101.0,Strong,Bangalore,No 9 | 20,Female,,Strong,Mumbai,Yes 10 | 19,Female,100.0,Strong,Bangalore,No 11 | 64,Female,101.0,Mild,Delhi,No 12 | 75,Female,,Mild,Delhi,No 13 | 65,Female,98.0,Mild,Mumbai,Yes 14 | 25,Female,99.0,Strong,Kolkata,No 15 | 64,Male,102.0,Mild,Bangalore,Yes 16 | 51,Male,104.0,Mild,Bangalore,No 17 | 70,Male,103.0,Strong,Kolkata,Yes 18 | 69,Female,103.0,Mild,Kolkata,Yes 19 | 40,Female,98.0,Strong,Delhi,No 20 | 64,Female,98.0,Mild,Bangalore,Yes 21 | 42,Female,,Strong,Bangalore,Yes 22 | 12,Male,98.0,Strong,Bangalore,No 23 | 73,Male,98.0,Mild,Bangalore,Yes 24 | 71,Female,98.0,Strong,Kolkata,Yes 25 | 80,Female,98.0,Mild,Delhi,Yes 26 | 13,Female,100.0,Strong,Kolkata,No 27 | 23,Male,,Mild,Mumbai,No 28 | 19,Female,100.0,Mild,Kolkata,Yes 29 | 33,Female,102.0,Strong,Delhi,No 30 | 16,Male,104.0,Mild,Kolkata,No 31 | 34,Female,,Strong,Mumbai,Yes 32 | 15,Male,101.0,Mild,Delhi,Yes 33 | 83,Male,103.0,Mild,Kolkata,No 34 | 34,Female,101.0,Strong,Delhi,Yes 35 | 26,Female,98.0,Mild,Kolkata,No 36 | 74,Male,102.0,Mild,Mumbai,Yes 37 | 82,Female,102.0,Strong,Bangalore,No 38 | 38,Female,101.0,Mild,Bangalore,No 39 | 55,Male,100.0,Mild,Kolkata,No 40 | 49,Female,101.0,Mild,Delhi,Yes 41 | 50,Female,103.0,Mild,Kolkata,No 42 | 49,Female,102.0,Mild,Delhi,No 43 | 82,Male,,Mild,Kolkata,Yes 44 | 27,Male,100.0,Mild,Delhi,Yes 45 | 22,Female,99.0,Mild,Bangalore,Yes 46 | 20,Male,102.0,Strong,Delhi,No 47 | 72,Male,99.0,Mild,Bangalore,No 48 | 19,Female,101.0,Mild,Mumbai,No 49 | 18,Female,104.0,Mild,Bangalore,No 50 | 66,Male,99.0,Strong,Bangalore,No 51 | 44,Male,104.0,Mild,Mumbai,No 52 | 19,Male,101.0,Mild,Delhi,Yes 53 | 11,Female,100.0,Strong,Kolkata,Yes 54 | 47,Female,100.0,Strong,Bangalore,Yes 55 | 83,Male,98.0,Mild,Delhi,Yes 56 | 60,Female,99.0,Mild,Mumbai,Yes 57 | 81,Female,101.0,Mild,Mumbai,Yes 58 | 71,Male,,Strong,Kolkata,No 59 | 49,Female,99.0,Strong,Bangalore,No 60 | 23,Male,98.0,Strong,Mumbai,Yes 61 | 6,Female,104.0,Mild,Kolkata,Yes 62 | 24,Female,102.0,Strong,Bangalore,Yes 63 | 81,Female,98.0,Strong,Mumbai,No 64 | 56,Female,104.0,Strong,Bangalore,Yes 65 | 10,Male,100.0,Mild,Bangalore,No 66 | 42,Male,104.0,Mild,Mumbai,No 67 | 69,Female,102.0,Mild,Bangalore,No 68 | 51,Male,104.0,Mild,Kolkata,No 69 | 65,Male,99.0,Mild,Bangalore,No 70 | 54,Female,104.0,Strong,Kolkata,No 71 | 73,Female,103.0,Mild,Delhi,No 72 | 68,Female,101.0,Strong,Delhi,No 73 | 75,Female,104.0,Strong,Delhi,No 74 | 83,Female,101.0,Mild,Kolkata,No 75 | 34,Male,98.0,Strong,Kolkata,Yes 76 | 34,Female,104.0,Strong,Delhi,No 77 | 5,Male,102.0,Mild,Kolkata,Yes 78 | 80,Male,100.0,Mild,Bangalore,Yes 79 | 8,Female,101.0,Mild,Kolkata,No 80 | 11,Male,100.0,Mild,Bangalore,Yes 81 | 48,Female,103.0,Mild,Kolkata,Yes 82 | 14,Female,99.0,Mild,Mumbai,Yes 83 | 65,Male,99.0,Mild,Delhi,No 84 | 24,Male,98.0,Mild,Kolkata,Yes 85 | 17,Female,104.0,Mild,Kolkata,No 86 | 69,Female,98.0,Strong,Mumbai,No 87 | 16,Female,103.0,Mild,Bangalore,Yes 88 | 25,Male,104.0,Mild,Bangalore,Yes 89 | 47,Male,101.0,Strong,Bangalore,No 90 | 5,Female,100.0,Mild,Kolkata,No 91 | 46,Male,103.0,Strong,Bangalore,No 92 | 59,Female,99.0,Strong,Delhi,No 93 | 38,Male,,Mild,Delhi,Yes 94 | 82,Female,102.0,Strong,Kolkata,No 95 | 27,Male,100.0,Mild,Kolkata,Yes 96 | 79,Male,,Strong,Kolkata,Yes 97 | 12,Female,104.0,Mild,Bangalore,No 98 | 51,Female,101.0,Strong,Kolkata,Yes 99 | 20,Female,101.0,Mild,Bangalore,No 100 | 5,Female,98.0,Strong,Mumbai,No 101 | 10,Female,98.0,Strong,Kolkata,Yes 102 | -------------------------------------------------------------------------------- /Pycaret for classification/data/heart_disease.csv: -------------------------------------------------------------------------------- 1 | age,sex,cp,trestbps,chol,fbs,restecg,thalach,exang,oldpeak,slope,ca,thal,target 2 | 63,1,3,145,233,1,0,150,0,2.3,0,0,1,1 3 | 37,1,2,130,250,0,1,187,0,3.5,0,0,2,1 4 | 41,0,1,130,204,0,0,172,0,1.4,2,0,2,1 5 | 56,1,1,120,236,0,1,178,0,0.8,2,0,2,1 6 | 57,0,0,120,354,0,1,163,1,0.6,2,0,2,1 7 | 57,1,0,140,192,0,1,148,0,0.4,1,0,1,1 8 | 56,0,1,140,294,0,0,153,0,1.3,1,0,2,1 9 | 44,1,1,120,263,0,1,173,0,0,2,0,3,1 10 | 52,1,2,172,199,1,1,162,0,0.5,2,0,3,1 11 | 57,1,2,150,168,0,1,174,0,1.6,2,0,2,1 12 | 54,1,0,140,239,0,1,160,0,1.2,2,0,2,1 13 | 48,0,2,130,275,0,1,139,0,0.2,2,0,2,1 14 | 49,1,1,130,266,0,1,171,0,0.6,2,0,2,1 15 | 64,1,3,110,211,0,0,144,1,1.8,1,0,2,1 16 | 58,0,3,150,283,1,0,162,0,1,2,0,2,1 17 | 50,0,2,120,219,0,1,158,0,1.6,1,0,2,1 18 | 58,0,2,120,340,0,1,172,0,0,2,0,2,1 19 | 66,0,3,150,226,0,1,114,0,2.6,0,0,2,1 20 | 43,1,0,150,247,0,1,171,0,1.5,2,0,2,1 21 | 69,0,3,140,239,0,1,151,0,1.8,2,2,2,1 22 | 59,1,0,135,234,0,1,161,0,0.5,1,0,3,1 23 | 44,1,2,130,233,0,1,179,1,0.4,2,0,2,1 24 | 42,1,0,140,226,0,1,178,0,0,2,0,2,1 25 | 61,1,2,150,243,1,1,137,1,1,1,0,2,1 26 | 40,1,3,140,199,0,1,178,1,1.4,2,0,3,1 27 | 71,0,1,160,302,0,1,162,0,0.4,2,2,2,1 28 | 59,1,2,150,212,1,1,157,0,1.6,2,0,2,1 29 | 51,1,2,110,175,0,1,123,0,0.6,2,0,2,1 30 | 65,0,2,140,417,1,0,157,0,0.8,2,1,2,1 31 | 53,1,2,130,197,1,0,152,0,1.2,0,0,2,1 32 | 41,0,1,105,198,0,1,168,0,0,2,1,2,1 33 | 65,1,0,120,177,0,1,140,0,0.4,2,0,3,1 34 | 44,1,1,130,219,0,0,188,0,0,2,0,2,1 35 | 54,1,2,125,273,0,0,152,0,0.5,0,1,2,1 36 | 51,1,3,125,213,0,0,125,1,1.4,2,1,2,1 37 | 46,0,2,142,177,0,0,160,1,1.4,0,0,2,1 38 | 54,0,2,135,304,1,1,170,0,0,2,0,2,1 39 | 54,1,2,150,232,0,0,165,0,1.6,2,0,3,1 40 | 65,0,2,155,269,0,1,148,0,0.8,2,0,2,1 41 | 65,0,2,160,360,0,0,151,0,0.8,2,0,2,1 42 | 51,0,2,140,308,0,0,142,0,1.5,2,1,2,1 43 | 48,1,1,130,245,0,0,180,0,0.2,1,0,2,1 44 | 45,1,0,104,208,0,0,148,1,3,1,0,2,1 45 | 53,0,0,130,264,0,0,143,0,0.4,1,0,2,1 46 | 39,1,2,140,321,0,0,182,0,0,2,0,2,1 47 | 52,1,1,120,325,0,1,172,0,0.2,2,0,2,1 48 | 44,1,2,140,235,0,0,180,0,0,2,0,2,1 49 | 47,1,2,138,257,0,0,156,0,0,2,0,2,1 50 | 53,0,2,128,216,0,0,115,0,0,2,0,0,1 51 | 53,0,0,138,234,0,0,160,0,0,2,0,2,1 52 | 51,0,2,130,256,0,0,149,0,0.5,2,0,2,1 53 | 66,1,0,120,302,0,0,151,0,0.4,1,0,2,1 54 | 62,1,2,130,231,0,1,146,0,1.8,1,3,3,1 55 | 44,0,2,108,141,0,1,175,0,0.6,1,0,2,1 56 | 63,0,2,135,252,0,0,172,0,0,2,0,2,1 57 | 52,1,1,134,201,0,1,158,0,0.8,2,1,2,1 58 | 48,1,0,122,222,0,0,186,0,0,2,0,2,1 59 | 45,1,0,115,260,0,0,185,0,0,2,0,2,1 60 | 34,1,3,118,182,0,0,174,0,0,2,0,2,1 61 | 57,0,0,128,303,0,0,159,0,0,2,1,2,1 62 | 71,0,2,110,265,1,0,130,0,0,2,1,2,1 63 | 54,1,1,108,309,0,1,156,0,0,2,0,3,1 64 | 52,1,3,118,186,0,0,190,0,0,1,0,1,1 65 | 41,1,1,135,203,0,1,132,0,0,1,0,1,1 66 | 58,1,2,140,211,1,0,165,0,0,2,0,2,1 67 | 35,0,0,138,183,0,1,182,0,1.4,2,0,2,1 68 | 51,1,2,100,222,0,1,143,1,1.2,1,0,2,1 69 | 45,0,1,130,234,0,0,175,0,0.6,1,0,2,1 70 | 44,1,1,120,220,0,1,170,0,0,2,0,2,1 71 | 62,0,0,124,209,0,1,163,0,0,2,0,2,1 72 | 54,1,2,120,258,0,0,147,0,0.4,1,0,3,1 73 | 51,1,2,94,227,0,1,154,1,0,2,1,3,1 74 | 29,1,1,130,204,0,0,202,0,0,2,0,2,1 75 | 51,1,0,140,261,0,0,186,1,0,2,0,2,1 76 | 43,0,2,122,213,0,1,165,0,0.2,1,0,2,1 77 | 55,0,1,135,250,0,0,161,0,1.4,1,0,2,1 78 | 51,1,2,125,245,1,0,166,0,2.4,1,0,2,1 79 | 59,1,1,140,221,0,1,164,1,0,2,0,2,1 80 | 52,1,1,128,205,1,1,184,0,0,2,0,2,1 81 | 58,1,2,105,240,0,0,154,1,0.6,1,0,3,1 82 | 41,1,2,112,250,0,1,179,0,0,2,0,2,1 83 | 45,1,1,128,308,0,0,170,0,0,2,0,2,1 84 | 60,0,2,102,318,0,1,160,0,0,2,1,2,1 85 | 52,1,3,152,298,1,1,178,0,1.2,1,0,3,1 86 | 42,0,0,102,265,0,0,122,0,0.6,1,0,2,1 87 | 67,0,2,115,564,0,0,160,0,1.6,1,0,3,1 88 | 68,1,2,118,277,0,1,151,0,1,2,1,3,1 89 | 46,1,1,101,197,1,1,156,0,0,2,0,3,1 90 | 54,0,2,110,214,0,1,158,0,1.6,1,0,2,1 91 | 58,0,0,100,248,0,0,122,0,1,1,0,2,1 92 | 48,1,2,124,255,1,1,175,0,0,2,2,2,1 93 | 57,1,0,132,207,0,1,168,1,0,2,0,3,1 94 | 52,1,2,138,223,0,1,169,0,0,2,4,2,1 95 | 54,0,1,132,288,1,0,159,1,0,2,1,2,1 96 | 45,0,1,112,160,0,1,138,0,0,1,0,2,1 97 | 53,1,0,142,226,0,0,111,1,0,2,0,3,1 98 | 62,0,0,140,394,0,0,157,0,1.2,1,0,2,1 99 | 52,1,0,108,233,1,1,147,0,0.1,2,3,3,1 100 | 43,1,2,130,315,0,1,162,0,1.9,2,1,2,1 101 | 53,1,2,130,246,1,0,173,0,0,2,3,2,1 102 | 42,1,3,148,244,0,0,178,0,0.8,2,2,2,1 103 | 59,1,3,178,270,0,0,145,0,4.2,0,0,3,1 104 | 63,0,1,140,195,0,1,179,0,0,2,2,2,1 105 | 42,1,2,120,240,1,1,194,0,0.8,0,0,3,1 106 | 50,1,2,129,196,0,1,163,0,0,2,0,2,1 107 | 68,0,2,120,211,0,0,115,0,1.5,1,0,2,1 108 | 69,1,3,160,234,1,0,131,0,0.1,1,1,2,1 109 | 45,0,0,138,236,0,0,152,1,0.2,1,0,2,1 110 | 50,0,1,120,244,0,1,162,0,1.1,2,0,2,1 111 | 50,0,0,110,254,0,0,159,0,0,2,0,2,1 112 | 64,0,0,180,325,0,1,154,1,0,2,0,2,1 113 | 57,1,2,150,126,1,1,173,0,0.2,2,1,3,1 114 | 64,0,2,140,313,0,1,133,0,0.2,2,0,3,1 115 | 43,1,0,110,211,0,1,161,0,0,2,0,3,1 116 | 55,1,1,130,262,0,1,155,0,0,2,0,2,1 117 | 37,0,2,120,215,0,1,170,0,0,2,0,2,1 118 | 41,1,2,130,214,0,0,168,0,2,1,0,2,1 119 | 56,1,3,120,193,0,0,162,0,1.9,1,0,3,1 120 | 46,0,1,105,204,0,1,172,0,0,2,0,2,1 121 | 46,0,0,138,243,0,0,152,1,0,1,0,2,1 122 | 64,0,0,130,303,0,1,122,0,2,1,2,2,1 123 | 59,1,0,138,271,0,0,182,0,0,2,0,2,1 124 | 41,0,2,112,268,0,0,172,1,0,2,0,2,1 125 | 54,0,2,108,267,0,0,167,0,0,2,0,2,1 126 | 39,0,2,94,199,0,1,179,0,0,2,0,2,1 127 | 34,0,1,118,210,0,1,192,0,0.7,2,0,2,1 128 | 47,1,0,112,204,0,1,143,0,0.1,2,0,2,1 129 | 67,0,2,152,277,0,1,172,0,0,2,1,2,1 130 | 52,0,2,136,196,0,0,169,0,0.1,1,0,2,1 131 | 74,0,1,120,269,0,0,121,1,0.2,2,1,2,1 132 | 54,0,2,160,201,0,1,163,0,0,2,1,2,1 133 | 49,0,1,134,271,0,1,162,0,0,1,0,2,1 134 | 42,1,1,120,295,0,1,162,0,0,2,0,2,1 135 | 41,1,1,110,235,0,1,153,0,0,2,0,2,1 136 | 41,0,1,126,306,0,1,163,0,0,2,0,2,1 137 | 49,0,0,130,269,0,1,163,0,0,2,0,2,1 138 | 60,0,2,120,178,1,1,96,0,0,2,0,2,1 139 | 62,1,1,128,208,1,0,140,0,0,2,0,2,1 140 | 57,1,0,110,201,0,1,126,1,1.5,1,0,1,1 141 | 64,1,0,128,263,0,1,105,1,0.2,1,1,3,1 142 | 51,0,2,120,295,0,0,157,0,0.6,2,0,2,1 143 | 43,1,0,115,303,0,1,181,0,1.2,1,0,2,1 144 | 42,0,2,120,209,0,1,173,0,0,1,0,2,1 145 | 67,0,0,106,223,0,1,142,0,0.3,2,2,2,1 146 | 76,0,2,140,197,0,2,116,0,1.1,1,0,2,1 147 | 70,1,1,156,245,0,0,143,0,0,2,0,2,1 148 | 44,0,2,118,242,0,1,149,0,0.3,1,1,2,1 149 | 60,0,3,150,240,0,1,171,0,0.9,2,0,2,1 150 | 44,1,2,120,226,0,1,169,0,0,2,0,2,1 151 | 42,1,2,130,180,0,1,150,0,0,2,0,2,1 152 | 66,1,0,160,228,0,0,138,0,2.3,2,0,1,1 153 | 71,0,0,112,149,0,1,125,0,1.6,1,0,2,1 154 | 64,1,3,170,227,0,0,155,0,0.6,1,0,3,1 155 | 66,0,2,146,278,0,0,152,0,0,1,1,2,1 156 | 39,0,2,138,220,0,1,152,0,0,1,0,2,1 157 | 58,0,0,130,197,0,1,131,0,0.6,1,0,2,1 158 | 47,1,2,130,253,0,1,179,0,0,2,0,2,1 159 | 35,1,1,122,192,0,1,174,0,0,2,0,2,1 160 | 58,1,1,125,220,0,1,144,0,0.4,1,4,3,1 161 | 56,1,1,130,221,0,0,163,0,0,2,0,3,1 162 | 56,1,1,120,240,0,1,169,0,0,0,0,2,1 163 | 55,0,1,132,342,0,1,166,0,1.2,2,0,2,1 164 | 41,1,1,120,157,0,1,182,0,0,2,0,2,1 165 | 38,1,2,138,175,0,1,173,0,0,2,4,2,1 166 | 38,1,2,138,175,0,1,173,0,0,2,4,2,1 167 | 67,1,0,160,286,0,0,108,1,1.5,1,3,2,0 168 | 67,1,0,120,229,0,0,129,1,2.6,1,2,3,0 169 | 62,0,0,140,268,0,0,160,0,3.6,0,2,2,0 170 | 63,1,0,130,254,0,0,147,0,1.4,1,1,3,0 171 | 53,1,0,140,203,1,0,155,1,3.1,0,0,3,0 172 | 56,1,2,130,256,1,0,142,1,0.6,1,1,1,0 173 | 48,1,1,110,229,0,1,168,0,1,0,0,3,0 174 | 58,1,1,120,284,0,0,160,0,1.8,1,0,2,0 175 | 58,1,2,132,224,0,0,173,0,3.2,2,2,3,0 176 | 60,1,0,130,206,0,0,132,1,2.4,1,2,3,0 177 | 40,1,0,110,167,0,0,114,1,2,1,0,3,0 178 | 60,1,0,117,230,1,1,160,1,1.4,2,2,3,0 179 | 64,1,2,140,335,0,1,158,0,0,2,0,2,0 180 | 43,1,0,120,177,0,0,120,1,2.5,1,0,3,0 181 | 57,1,0,150,276,0,0,112,1,0.6,1,1,1,0 182 | 55,1,0,132,353,0,1,132,1,1.2,1,1,3,0 183 | 65,0,0,150,225,0,0,114,0,1,1,3,3,0 184 | 61,0,0,130,330,0,0,169,0,0,2,0,2,0 185 | 58,1,2,112,230,0,0,165,0,2.5,1,1,3,0 186 | 50,1,0,150,243,0,0,128,0,2.6,1,0,3,0 187 | 44,1,0,112,290,0,0,153,0,0,2,1,2,0 188 | 60,1,0,130,253,0,1,144,1,1.4,2,1,3,0 189 | 54,1,0,124,266,0,0,109,1,2.2,1,1,3,0 190 | 50,1,2,140,233,0,1,163,0,0.6,1,1,3,0 191 | 41,1,0,110,172,0,0,158,0,0,2,0,3,0 192 | 51,0,0,130,305,0,1,142,1,1.2,1,0,3,0 193 | 58,1,0,128,216,0,0,131,1,2.2,1,3,3,0 194 | 54,1,0,120,188,0,1,113,0,1.4,1,1,3,0 195 | 60,1,0,145,282,0,0,142,1,2.8,1,2,3,0 196 | 60,1,2,140,185,0,0,155,0,3,1,0,2,0 197 | 59,1,0,170,326,0,0,140,1,3.4,0,0,3,0 198 | 46,1,2,150,231,0,1,147,0,3.6,1,0,2,0 199 | 67,1,0,125,254,1,1,163,0,0.2,1,2,3,0 200 | 62,1,0,120,267,0,1,99,1,1.8,1,2,3,0 201 | 65,1,0,110,248,0,0,158,0,0.6,2,2,1,0 202 | 44,1,0,110,197,0,0,177,0,0,2,1,2,0 203 | 60,1,0,125,258,0,0,141,1,2.8,1,1,3,0 204 | 58,1,0,150,270,0,0,111,1,0.8,2,0,3,0 205 | 68,1,2,180,274,1,0,150,1,1.6,1,0,3,0 206 | 62,0,0,160,164,0,0,145,0,6.2,0,3,3,0 207 | 52,1,0,128,255,0,1,161,1,0,2,1,3,0 208 | 59,1,0,110,239,0,0,142,1,1.2,1,1,3,0 209 | 60,0,0,150,258,0,0,157,0,2.6,1,2,3,0 210 | 49,1,2,120,188,0,1,139,0,2,1,3,3,0 211 | 59,1,0,140,177,0,1,162,1,0,2,1,3,0 212 | 57,1,2,128,229,0,0,150,0,0.4,1,1,3,0 213 | 61,1,0,120,260,0,1,140,1,3.6,1,1,3,0 214 | 39,1,0,118,219,0,1,140,0,1.2,1,0,3,0 215 | 61,0,0,145,307,0,0,146,1,1,1,0,3,0 216 | 56,1,0,125,249,1,0,144,1,1.2,1,1,2,0 217 | 43,0,0,132,341,1,0,136,1,3,1,0,3,0 218 | 62,0,2,130,263,0,1,97,0,1.2,1,1,3,0 219 | 63,1,0,130,330,1,0,132,1,1.8,2,3,3,0 220 | 65,1,0,135,254,0,0,127,0,2.8,1,1,3,0 221 | 48,1,0,130,256,1,0,150,1,0,2,2,3,0 222 | 63,0,0,150,407,0,0,154,0,4,1,3,3,0 223 | 55,1,0,140,217,0,1,111,1,5.6,0,0,3,0 224 | 65,1,3,138,282,1,0,174,0,1.4,1,1,2,0 225 | 56,0,0,200,288,1,0,133,1,4,0,2,3,0 226 | 54,1,0,110,239,0,1,126,1,2.8,1,1,3,0 227 | 70,1,0,145,174,0,1,125,1,2.6,0,0,3,0 228 | 62,1,1,120,281,0,0,103,0,1.4,1,1,3,0 229 | 35,1,0,120,198,0,1,130,1,1.6,1,0,3,0 230 | 59,1,3,170,288,0,0,159,0,0.2,1,0,3,0 231 | 64,1,2,125,309,0,1,131,1,1.8,1,0,3,0 232 | 47,1,2,108,243,0,1,152,0,0,2,0,2,0 233 | 57,1,0,165,289,1,0,124,0,1,1,3,3,0 234 | 55,1,0,160,289,0,0,145,1,0.8,1,1,3,0 235 | 64,1,0,120,246,0,0,96,1,2.2,0,1,2,0 236 | 70,1,0,130,322,0,0,109,0,2.4,1,3,2,0 237 | 51,1,0,140,299,0,1,173,1,1.6,2,0,3,0 238 | 58,1,0,125,300,0,0,171,0,0,2,2,3,0 239 | 60,1,0,140,293,0,0,170,0,1.2,1,2,3,0 240 | 77,1,0,125,304,0,0,162,1,0,2,3,2,0 241 | 35,1,0,126,282,0,0,156,1,0,2,0,3,0 242 | 70,1,2,160,269,0,1,112,1,2.9,1,1,3,0 243 | 59,0,0,174,249,0,1,143,1,0,1,0,2,0 244 | 64,1,0,145,212,0,0,132,0,2,1,2,1,0 245 | 57,1,0,152,274,0,1,88,1,1.2,1,1,3,0 246 | 56,1,0,132,184,0,0,105,1,2.1,1,1,1,0 247 | 48,1,0,124,274,0,0,166,0,0.5,1,0,3,0 248 | 56,0,0,134,409,0,0,150,1,1.9,1,2,3,0 249 | 66,1,1,160,246,0,1,120,1,0,1,3,1,0 250 | 54,1,1,192,283,0,0,195,0,0,2,1,3,0 251 | 69,1,2,140,254,0,0,146,0,2,1,3,3,0 252 | 51,1,0,140,298,0,1,122,1,4.2,1,3,3,0 253 | 43,1,0,132,247,1,0,143,1,0.1,1,4,3,0 254 | 62,0,0,138,294,1,1,106,0,1.9,1,3,2,0 255 | 67,1,0,100,299,0,0,125,1,0.9,1,2,2,0 256 | 59,1,3,160,273,0,0,125,0,0,2,0,2,0 257 | 45,1,0,142,309,0,0,147,1,0,1,3,3,0 258 | 58,1,0,128,259,0,0,130,1,3,1,2,3,0 259 | 50,1,0,144,200,0,0,126,1,0.9,1,0,3,0 260 | 62,0,0,150,244,0,1,154,1,1.4,1,0,2,0 261 | 38,1,3,120,231,0,1,182,1,3.8,1,0,3,0 262 | 66,0,0,178,228,1,1,165,1,1,1,2,3,0 263 | 52,1,0,112,230,0,1,160,0,0,2,1,2,0 264 | 53,1,0,123,282,0,1,95,1,2,1,2,3,0 265 | 63,0,0,108,269,0,1,169,1,1.8,1,2,2,0 266 | 54,1,0,110,206,0,0,108,1,0,1,1,2,0 267 | 66,1,0,112,212,0,0,132,1,0.1,2,1,2,0 268 | 55,0,0,180,327,0,2,117,1,3.4,1,0,2,0 269 | 49,1,2,118,149,0,0,126,0,0.8,2,3,2,0 270 | 54,1,0,122,286,0,0,116,1,3.2,1,2,2,0 271 | 56,1,0,130,283,1,0,103,1,1.6,0,0,3,0 272 | 46,1,0,120,249,0,0,144,0,0.8,2,0,3,0 273 | 61,1,3,134,234,0,1,145,0,2.6,1,2,2,0 274 | 67,1,0,120,237,0,1,71,0,1,1,0,2,0 275 | 58,1,0,100,234,0,1,156,0,0.1,2,1,3,0 276 | 47,1,0,110,275,0,0,118,1,1,1,1,2,0 277 | 52,1,0,125,212,0,1,168,0,1,2,2,3,0 278 | 58,1,0,146,218,0,1,105,0,2,1,1,3,0 279 | 57,1,1,124,261,0,1,141,0,0.3,2,0,3,0 280 | 58,0,1,136,319,1,0,152,0,0,2,2,2,0 281 | 61,1,0,138,166,0,0,125,1,3.6,1,1,2,0 282 | 42,1,0,136,315,0,1,125,1,1.8,1,0,1,0 283 | 52,1,0,128,204,1,1,156,1,1,1,0,0,0 284 | 59,1,2,126,218,1,1,134,0,2.2,1,1,1,0 285 | 40,1,0,152,223,0,1,181,0,0,2,0,3,0 286 | 61,1,0,140,207,0,0,138,1,1.9,2,1,3,0 287 | 46,1,0,140,311,0,1,120,1,1.8,1,2,3,0 288 | 59,1,3,134,204,0,1,162,0,0.8,2,2,2,0 289 | 57,1,1,154,232,0,0,164,0,0,2,1,2,0 290 | 57,1,0,110,335,0,1,143,1,3,1,1,3,0 291 | 55,0,0,128,205,0,2,130,1,2,1,1,3,0 292 | 61,1,0,148,203,0,1,161,0,0,2,1,3,0 293 | 58,1,0,114,318,0,2,140,0,4.4,0,3,1,0 294 | 58,0,0,170,225,1,0,146,1,2.8,1,2,1,0 295 | 67,1,2,152,212,0,0,150,0,0.8,1,0,3,0 296 | 44,1,0,120,169,0,1,144,1,2.8,0,0,1,0 297 | 63,1,0,140,187,0,0,144,1,4,2,2,3,0 298 | 63,0,0,124,197,0,1,136,1,0,1,0,2,0 299 | 59,1,0,164,176,1,0,90,0,1,1,2,1,0 300 | 57,0,0,140,241,0,1,123,1,0.2,1,0,3,0 301 | 45,1,3,110,264,0,1,132,0,1.2,1,0,3,0 302 | 68,1,0,144,193,1,1,141,0,3.4,1,2,3,0 303 | 57,1,0,130,131,0,1,115,1,1.2,1,1,3,0 304 | 57,0,1,130,236,0,0,174,0,0,1,1,2,0 305 | -------------------------------------------------------------------------------- /Random Forest/Random forest/data/heart_disease.csv: -------------------------------------------------------------------------------- 1 | age,sex,cp,trestbps,chol,fbs,restecg,thalach,exang,oldpeak,slope,ca,thal,target 2 | 63,1,3,145,233,1,0,150,0,2.3,0,0,1,1 3 | 37,1,2,130,250,0,1,187,0,3.5,0,0,2,1 4 | 41,0,1,130,204,0,0,172,0,1.4,2,0,2,1 5 | 56,1,1,120,236,0,1,178,0,0.8,2,0,2,1 6 | 57,0,0,120,354,0,1,163,1,0.6,2,0,2,1 7 | 57,1,0,140,192,0,1,148,0,0.4,1,0,1,1 8 | 56,0,1,140,294,0,0,153,0,1.3,1,0,2,1 9 | 44,1,1,120,263,0,1,173,0,0,2,0,3,1 10 | 52,1,2,172,199,1,1,162,0,0.5,2,0,3,1 11 | 57,1,2,150,168,0,1,174,0,1.6,2,0,2,1 12 | 54,1,0,140,239,0,1,160,0,1.2,2,0,2,1 13 | 48,0,2,130,275,0,1,139,0,0.2,2,0,2,1 14 | 49,1,1,130,266,0,1,171,0,0.6,2,0,2,1 15 | 64,1,3,110,211,0,0,144,1,1.8,1,0,2,1 16 | 58,0,3,150,283,1,0,162,0,1,2,0,2,1 17 | 50,0,2,120,219,0,1,158,0,1.6,1,0,2,1 18 | 58,0,2,120,340,0,1,172,0,0,2,0,2,1 19 | 66,0,3,150,226,0,1,114,0,2.6,0,0,2,1 20 | 43,1,0,150,247,0,1,171,0,1.5,2,0,2,1 21 | 69,0,3,140,239,0,1,151,0,1.8,2,2,2,1 22 | 59,1,0,135,234,0,1,161,0,0.5,1,0,3,1 23 | 44,1,2,130,233,0,1,179,1,0.4,2,0,2,1 24 | 42,1,0,140,226,0,1,178,0,0,2,0,2,1 25 | 61,1,2,150,243,1,1,137,1,1,1,0,2,1 26 | 40,1,3,140,199,0,1,178,1,1.4,2,0,3,1 27 | 71,0,1,160,302,0,1,162,0,0.4,2,2,2,1 28 | 59,1,2,150,212,1,1,157,0,1.6,2,0,2,1 29 | 51,1,2,110,175,0,1,123,0,0.6,2,0,2,1 30 | 65,0,2,140,417,1,0,157,0,0.8,2,1,2,1 31 | 53,1,2,130,197,1,0,152,0,1.2,0,0,2,1 32 | 41,0,1,105,198,0,1,168,0,0,2,1,2,1 33 | 65,1,0,120,177,0,1,140,0,0.4,2,0,3,1 34 | 44,1,1,130,219,0,0,188,0,0,2,0,2,1 35 | 54,1,2,125,273,0,0,152,0,0.5,0,1,2,1 36 | 51,1,3,125,213,0,0,125,1,1.4,2,1,2,1 37 | 46,0,2,142,177,0,0,160,1,1.4,0,0,2,1 38 | 54,0,2,135,304,1,1,170,0,0,2,0,2,1 39 | 54,1,2,150,232,0,0,165,0,1.6,2,0,3,1 40 | 65,0,2,155,269,0,1,148,0,0.8,2,0,2,1 41 | 65,0,2,160,360,0,0,151,0,0.8,2,0,2,1 42 | 51,0,2,140,308,0,0,142,0,1.5,2,1,2,1 43 | 48,1,1,130,245,0,0,180,0,0.2,1,0,2,1 44 | 45,1,0,104,208,0,0,148,1,3,1,0,2,1 45 | 53,0,0,130,264,0,0,143,0,0.4,1,0,2,1 46 | 39,1,2,140,321,0,0,182,0,0,2,0,2,1 47 | 52,1,1,120,325,0,1,172,0,0.2,2,0,2,1 48 | 44,1,2,140,235,0,0,180,0,0,2,0,2,1 49 | 47,1,2,138,257,0,0,156,0,0,2,0,2,1 50 | 53,0,2,128,216,0,0,115,0,0,2,0,0,1 51 | 53,0,0,138,234,0,0,160,0,0,2,0,2,1 52 | 51,0,2,130,256,0,0,149,0,0.5,2,0,2,1 53 | 66,1,0,120,302,0,0,151,0,0.4,1,0,2,1 54 | 62,1,2,130,231,0,1,146,0,1.8,1,3,3,1 55 | 44,0,2,108,141,0,1,175,0,0.6,1,0,2,1 56 | 63,0,2,135,252,0,0,172,0,0,2,0,2,1 57 | 52,1,1,134,201,0,1,158,0,0.8,2,1,2,1 58 | 48,1,0,122,222,0,0,186,0,0,2,0,2,1 59 | 45,1,0,115,260,0,0,185,0,0,2,0,2,1 60 | 34,1,3,118,182,0,0,174,0,0,2,0,2,1 61 | 57,0,0,128,303,0,0,159,0,0,2,1,2,1 62 | 71,0,2,110,265,1,0,130,0,0,2,1,2,1 63 | 54,1,1,108,309,0,1,156,0,0,2,0,3,1 64 | 52,1,3,118,186,0,0,190,0,0,1,0,1,1 65 | 41,1,1,135,203,0,1,132,0,0,1,0,1,1 66 | 58,1,2,140,211,1,0,165,0,0,2,0,2,1 67 | 35,0,0,138,183,0,1,182,0,1.4,2,0,2,1 68 | 51,1,2,100,222,0,1,143,1,1.2,1,0,2,1 69 | 45,0,1,130,234,0,0,175,0,0.6,1,0,2,1 70 | 44,1,1,120,220,0,1,170,0,0,2,0,2,1 71 | 62,0,0,124,209,0,1,163,0,0,2,0,2,1 72 | 54,1,2,120,258,0,0,147,0,0.4,1,0,3,1 73 | 51,1,2,94,227,0,1,154,1,0,2,1,3,1 74 | 29,1,1,130,204,0,0,202,0,0,2,0,2,1 75 | 51,1,0,140,261,0,0,186,1,0,2,0,2,1 76 | 43,0,2,122,213,0,1,165,0,0.2,1,0,2,1 77 | 55,0,1,135,250,0,0,161,0,1.4,1,0,2,1 78 | 51,1,2,125,245,1,0,166,0,2.4,1,0,2,1 79 | 59,1,1,140,221,0,1,164,1,0,2,0,2,1 80 | 52,1,1,128,205,1,1,184,0,0,2,0,2,1 81 | 58,1,2,105,240,0,0,154,1,0.6,1,0,3,1 82 | 41,1,2,112,250,0,1,179,0,0,2,0,2,1 83 | 45,1,1,128,308,0,0,170,0,0,2,0,2,1 84 | 60,0,2,102,318,0,1,160,0,0,2,1,2,1 85 | 52,1,3,152,298,1,1,178,0,1.2,1,0,3,1 86 | 42,0,0,102,265,0,0,122,0,0.6,1,0,2,1 87 | 67,0,2,115,564,0,0,160,0,1.6,1,0,3,1 88 | 68,1,2,118,277,0,1,151,0,1,2,1,3,1 89 | 46,1,1,101,197,1,1,156,0,0,2,0,3,1 90 | 54,0,2,110,214,0,1,158,0,1.6,1,0,2,1 91 | 58,0,0,100,248,0,0,122,0,1,1,0,2,1 92 | 48,1,2,124,255,1,1,175,0,0,2,2,2,1 93 | 57,1,0,132,207,0,1,168,1,0,2,0,3,1 94 | 52,1,2,138,223,0,1,169,0,0,2,4,2,1 95 | 54,0,1,132,288,1,0,159,1,0,2,1,2,1 96 | 45,0,1,112,160,0,1,138,0,0,1,0,2,1 97 | 53,1,0,142,226,0,0,111,1,0,2,0,3,1 98 | 62,0,0,140,394,0,0,157,0,1.2,1,0,2,1 99 | 52,1,0,108,233,1,1,147,0,0.1,2,3,3,1 100 | 43,1,2,130,315,0,1,162,0,1.9,2,1,2,1 101 | 53,1,2,130,246,1,0,173,0,0,2,3,2,1 102 | 42,1,3,148,244,0,0,178,0,0.8,2,2,2,1 103 | 59,1,3,178,270,0,0,145,0,4.2,0,0,3,1 104 | 63,0,1,140,195,0,1,179,0,0,2,2,2,1 105 | 42,1,2,120,240,1,1,194,0,0.8,0,0,3,1 106 | 50,1,2,129,196,0,1,163,0,0,2,0,2,1 107 | 68,0,2,120,211,0,0,115,0,1.5,1,0,2,1 108 | 69,1,3,160,234,1,0,131,0,0.1,1,1,2,1 109 | 45,0,0,138,236,0,0,152,1,0.2,1,0,2,1 110 | 50,0,1,120,244,0,1,162,0,1.1,2,0,2,1 111 | 50,0,0,110,254,0,0,159,0,0,2,0,2,1 112 | 64,0,0,180,325,0,1,154,1,0,2,0,2,1 113 | 57,1,2,150,126,1,1,173,0,0.2,2,1,3,1 114 | 64,0,2,140,313,0,1,133,0,0.2,2,0,3,1 115 | 43,1,0,110,211,0,1,161,0,0,2,0,3,1 116 | 55,1,1,130,262,0,1,155,0,0,2,0,2,1 117 | 37,0,2,120,215,0,1,170,0,0,2,0,2,1 118 | 41,1,2,130,214,0,0,168,0,2,1,0,2,1 119 | 56,1,3,120,193,0,0,162,0,1.9,1,0,3,1 120 | 46,0,1,105,204,0,1,172,0,0,2,0,2,1 121 | 46,0,0,138,243,0,0,152,1,0,1,0,2,1 122 | 64,0,0,130,303,0,1,122,0,2,1,2,2,1 123 | 59,1,0,138,271,0,0,182,0,0,2,0,2,1 124 | 41,0,2,112,268,0,0,172,1,0,2,0,2,1 125 | 54,0,2,108,267,0,0,167,0,0,2,0,2,1 126 | 39,0,2,94,199,0,1,179,0,0,2,0,2,1 127 | 34,0,1,118,210,0,1,192,0,0.7,2,0,2,1 128 | 47,1,0,112,204,0,1,143,0,0.1,2,0,2,1 129 | 67,0,2,152,277,0,1,172,0,0,2,1,2,1 130 | 52,0,2,136,196,0,0,169,0,0.1,1,0,2,1 131 | 74,0,1,120,269,0,0,121,1,0.2,2,1,2,1 132 | 54,0,2,160,201,0,1,163,0,0,2,1,2,1 133 | 49,0,1,134,271,0,1,162,0,0,1,0,2,1 134 | 42,1,1,120,295,0,1,162,0,0,2,0,2,1 135 | 41,1,1,110,235,0,1,153,0,0,2,0,2,1 136 | 41,0,1,126,306,0,1,163,0,0,2,0,2,1 137 | 49,0,0,130,269,0,1,163,0,0,2,0,2,1 138 | 60,0,2,120,178,1,1,96,0,0,2,0,2,1 139 | 62,1,1,128,208,1,0,140,0,0,2,0,2,1 140 | 57,1,0,110,201,0,1,126,1,1.5,1,0,1,1 141 | 64,1,0,128,263,0,1,105,1,0.2,1,1,3,1 142 | 51,0,2,120,295,0,0,157,0,0.6,2,0,2,1 143 | 43,1,0,115,303,0,1,181,0,1.2,1,0,2,1 144 | 42,0,2,120,209,0,1,173,0,0,1,0,2,1 145 | 67,0,0,106,223,0,1,142,0,0.3,2,2,2,1 146 | 76,0,2,140,197,0,2,116,0,1.1,1,0,2,1 147 | 70,1,1,156,245,0,0,143,0,0,2,0,2,1 148 | 44,0,2,118,242,0,1,149,0,0.3,1,1,2,1 149 | 60,0,3,150,240,0,1,171,0,0.9,2,0,2,1 150 | 44,1,2,120,226,0,1,169,0,0,2,0,2,1 151 | 42,1,2,130,180,0,1,150,0,0,2,0,2,1 152 | 66,1,0,160,228,0,0,138,0,2.3,2,0,1,1 153 | 71,0,0,112,149,0,1,125,0,1.6,1,0,2,1 154 | 64,1,3,170,227,0,0,155,0,0.6,1,0,3,1 155 | 66,0,2,146,278,0,0,152,0,0,1,1,2,1 156 | 39,0,2,138,220,0,1,152,0,0,1,0,2,1 157 | 58,0,0,130,197,0,1,131,0,0.6,1,0,2,1 158 | 47,1,2,130,253,0,1,179,0,0,2,0,2,1 159 | 35,1,1,122,192,0,1,174,0,0,2,0,2,1 160 | 58,1,1,125,220,0,1,144,0,0.4,1,4,3,1 161 | 56,1,1,130,221,0,0,163,0,0,2,0,3,1 162 | 56,1,1,120,240,0,1,169,0,0,0,0,2,1 163 | 55,0,1,132,342,0,1,166,0,1.2,2,0,2,1 164 | 41,1,1,120,157,0,1,182,0,0,2,0,2,1 165 | 38,1,2,138,175,0,1,173,0,0,2,4,2,1 166 | 38,1,2,138,175,0,1,173,0,0,2,4,2,1 167 | 67,1,0,160,286,0,0,108,1,1.5,1,3,2,0 168 | 67,1,0,120,229,0,0,129,1,2.6,1,2,3,0 169 | 62,0,0,140,268,0,0,160,0,3.6,0,2,2,0 170 | 63,1,0,130,254,0,0,147,0,1.4,1,1,3,0 171 | 53,1,0,140,203,1,0,155,1,3.1,0,0,3,0 172 | 56,1,2,130,256,1,0,142,1,0.6,1,1,1,0 173 | 48,1,1,110,229,0,1,168,0,1,0,0,3,0 174 | 58,1,1,120,284,0,0,160,0,1.8,1,0,2,0 175 | 58,1,2,132,224,0,0,173,0,3.2,2,2,3,0 176 | 60,1,0,130,206,0,0,132,1,2.4,1,2,3,0 177 | 40,1,0,110,167,0,0,114,1,2,1,0,3,0 178 | 60,1,0,117,230,1,1,160,1,1.4,2,2,3,0 179 | 64,1,2,140,335,0,1,158,0,0,2,0,2,0 180 | 43,1,0,120,177,0,0,120,1,2.5,1,0,3,0 181 | 57,1,0,150,276,0,0,112,1,0.6,1,1,1,0 182 | 55,1,0,132,353,0,1,132,1,1.2,1,1,3,0 183 | 65,0,0,150,225,0,0,114,0,1,1,3,3,0 184 | 61,0,0,130,330,0,0,169,0,0,2,0,2,0 185 | 58,1,2,112,230,0,0,165,0,2.5,1,1,3,0 186 | 50,1,0,150,243,0,0,128,0,2.6,1,0,3,0 187 | 44,1,0,112,290,0,0,153,0,0,2,1,2,0 188 | 60,1,0,130,253,0,1,144,1,1.4,2,1,3,0 189 | 54,1,0,124,266,0,0,109,1,2.2,1,1,3,0 190 | 50,1,2,140,233,0,1,163,0,0.6,1,1,3,0 191 | 41,1,0,110,172,0,0,158,0,0,2,0,3,0 192 | 51,0,0,130,305,0,1,142,1,1.2,1,0,3,0 193 | 58,1,0,128,216,0,0,131,1,2.2,1,3,3,0 194 | 54,1,0,120,188,0,1,113,0,1.4,1,1,3,0 195 | 60,1,0,145,282,0,0,142,1,2.8,1,2,3,0 196 | 60,1,2,140,185,0,0,155,0,3,1,0,2,0 197 | 59,1,0,170,326,0,0,140,1,3.4,0,0,3,0 198 | 46,1,2,150,231,0,1,147,0,3.6,1,0,2,0 199 | 67,1,0,125,254,1,1,163,0,0.2,1,2,3,0 200 | 62,1,0,120,267,0,1,99,1,1.8,1,2,3,0 201 | 65,1,0,110,248,0,0,158,0,0.6,2,2,1,0 202 | 44,1,0,110,197,0,0,177,0,0,2,1,2,0 203 | 60,1,0,125,258,0,0,141,1,2.8,1,1,3,0 204 | 58,1,0,150,270,0,0,111,1,0.8,2,0,3,0 205 | 68,1,2,180,274,1,0,150,1,1.6,1,0,3,0 206 | 62,0,0,160,164,0,0,145,0,6.2,0,3,3,0 207 | 52,1,0,128,255,0,1,161,1,0,2,1,3,0 208 | 59,1,0,110,239,0,0,142,1,1.2,1,1,3,0 209 | 60,0,0,150,258,0,0,157,0,2.6,1,2,3,0 210 | 49,1,2,120,188,0,1,139,0,2,1,3,3,0 211 | 59,1,0,140,177,0,1,162,1,0,2,1,3,0 212 | 57,1,2,128,229,0,0,150,0,0.4,1,1,3,0 213 | 61,1,0,120,260,0,1,140,1,3.6,1,1,3,0 214 | 39,1,0,118,219,0,1,140,0,1.2,1,0,3,0 215 | 61,0,0,145,307,0,0,146,1,1,1,0,3,0 216 | 56,1,0,125,249,1,0,144,1,1.2,1,1,2,0 217 | 43,0,0,132,341,1,0,136,1,3,1,0,3,0 218 | 62,0,2,130,263,0,1,97,0,1.2,1,1,3,0 219 | 63,1,0,130,330,1,0,132,1,1.8,2,3,3,0 220 | 65,1,0,135,254,0,0,127,0,2.8,1,1,3,0 221 | 48,1,0,130,256,1,0,150,1,0,2,2,3,0 222 | 63,0,0,150,407,0,0,154,0,4,1,3,3,0 223 | 55,1,0,140,217,0,1,111,1,5.6,0,0,3,0 224 | 65,1,3,138,282,1,0,174,0,1.4,1,1,2,0 225 | 56,0,0,200,288,1,0,133,1,4,0,2,3,0 226 | 54,1,0,110,239,0,1,126,1,2.8,1,1,3,0 227 | 70,1,0,145,174,0,1,125,1,2.6,0,0,3,0 228 | 62,1,1,120,281,0,0,103,0,1.4,1,1,3,0 229 | 35,1,0,120,198,0,1,130,1,1.6,1,0,3,0 230 | 59,1,3,170,288,0,0,159,0,0.2,1,0,3,0 231 | 64,1,2,125,309,0,1,131,1,1.8,1,0,3,0 232 | 47,1,2,108,243,0,1,152,0,0,2,0,2,0 233 | 57,1,0,165,289,1,0,124,0,1,1,3,3,0 234 | 55,1,0,160,289,0,0,145,1,0.8,1,1,3,0 235 | 64,1,0,120,246,0,0,96,1,2.2,0,1,2,0 236 | 70,1,0,130,322,0,0,109,0,2.4,1,3,2,0 237 | 51,1,0,140,299,0,1,173,1,1.6,2,0,3,0 238 | 58,1,0,125,300,0,0,171,0,0,2,2,3,0 239 | 60,1,0,140,293,0,0,170,0,1.2,1,2,3,0 240 | 77,1,0,125,304,0,0,162,1,0,2,3,2,0 241 | 35,1,0,126,282,0,0,156,1,0,2,0,3,0 242 | 70,1,2,160,269,0,1,112,1,2.9,1,1,3,0 243 | 59,0,0,174,249,0,1,143,1,0,1,0,2,0 244 | 64,1,0,145,212,0,0,132,0,2,1,2,1,0 245 | 57,1,0,152,274,0,1,88,1,1.2,1,1,3,0 246 | 56,1,0,132,184,0,0,105,1,2.1,1,1,1,0 247 | 48,1,0,124,274,0,0,166,0,0.5,1,0,3,0 248 | 56,0,0,134,409,0,0,150,1,1.9,1,2,3,0 249 | 66,1,1,160,246,0,1,120,1,0,1,3,1,0 250 | 54,1,1,192,283,0,0,195,0,0,2,1,3,0 251 | 69,1,2,140,254,0,0,146,0,2,1,3,3,0 252 | 51,1,0,140,298,0,1,122,1,4.2,1,3,3,0 253 | 43,1,0,132,247,1,0,143,1,0.1,1,4,3,0 254 | 62,0,0,138,294,1,1,106,0,1.9,1,3,2,0 255 | 67,1,0,100,299,0,0,125,1,0.9,1,2,2,0 256 | 59,1,3,160,273,0,0,125,0,0,2,0,2,0 257 | 45,1,0,142,309,0,0,147,1,0,1,3,3,0 258 | 58,1,0,128,259,0,0,130,1,3,1,2,3,0 259 | 50,1,0,144,200,0,0,126,1,0.9,1,0,3,0 260 | 62,0,0,150,244,0,1,154,1,1.4,1,0,2,0 261 | 38,1,3,120,231,0,1,182,1,3.8,1,0,3,0 262 | 66,0,0,178,228,1,1,165,1,1,1,2,3,0 263 | 52,1,0,112,230,0,1,160,0,0,2,1,2,0 264 | 53,1,0,123,282,0,1,95,1,2,1,2,3,0 265 | 63,0,0,108,269,0,1,169,1,1.8,1,2,2,0 266 | 54,1,0,110,206,0,0,108,1,0,1,1,2,0 267 | 66,1,0,112,212,0,0,132,1,0.1,2,1,2,0 268 | 55,0,0,180,327,0,2,117,1,3.4,1,0,2,0 269 | 49,1,2,118,149,0,0,126,0,0.8,2,3,2,0 270 | 54,1,0,122,286,0,0,116,1,3.2,1,2,2,0 271 | 56,1,0,130,283,1,0,103,1,1.6,0,0,3,0 272 | 46,1,0,120,249,0,0,144,0,0.8,2,0,3,0 273 | 61,1,3,134,234,0,1,145,0,2.6,1,2,2,0 274 | 67,1,0,120,237,0,1,71,0,1,1,0,2,0 275 | 58,1,0,100,234,0,1,156,0,0.1,2,1,3,0 276 | 47,1,0,110,275,0,0,118,1,1,1,1,2,0 277 | 52,1,0,125,212,0,1,168,0,1,2,2,3,0 278 | 58,1,0,146,218,0,1,105,0,2,1,1,3,0 279 | 57,1,1,124,261,0,1,141,0,0.3,2,0,3,0 280 | 58,0,1,136,319,1,0,152,0,0,2,2,2,0 281 | 61,1,0,138,166,0,0,125,1,3.6,1,1,2,0 282 | 42,1,0,136,315,0,1,125,1,1.8,1,0,1,0 283 | 52,1,0,128,204,1,1,156,1,1,1,0,0,0 284 | 59,1,2,126,218,1,1,134,0,2.2,1,1,1,0 285 | 40,1,0,152,223,0,1,181,0,0,2,0,3,0 286 | 61,1,0,140,207,0,0,138,1,1.9,2,1,3,0 287 | 46,1,0,140,311,0,1,120,1,1.8,1,2,3,0 288 | 59,1,3,134,204,0,1,162,0,0.8,2,2,2,0 289 | 57,1,1,154,232,0,0,164,0,0,2,1,2,0 290 | 57,1,0,110,335,0,1,143,1,3,1,1,3,0 291 | 55,0,0,128,205,0,2,130,1,2,1,1,3,0 292 | 61,1,0,148,203,0,1,161,0,0,2,1,3,0 293 | 58,1,0,114,318,0,2,140,0,4.4,0,3,1,0 294 | 58,0,0,170,225,1,0,146,1,2.8,1,2,1,0 295 | 67,1,2,152,212,0,0,150,0,0.8,1,0,3,0 296 | 44,1,0,120,169,0,1,144,1,2.8,0,0,1,0 297 | 63,1,0,140,187,0,0,144,1,4,2,2,3,0 298 | 63,0,0,124,197,0,1,136,1,0,1,0,2,0 299 | 59,1,0,164,176,1,0,90,0,1,1,2,1,0 300 | 57,0,0,140,241,0,1,123,1,0.2,1,0,3,0 301 | 45,1,3,110,264,0,1,132,0,1.2,1,0,3,0 302 | 68,1,0,144,193,1,1,141,0,3.4,1,2,3,0 303 | 57,1,0,130,131,0,1,115,1,1.2,1,1,3,0 304 | 57,0,1,130,236,0,0,174,0,0,1,1,2,0 305 | -------------------------------------------------------------------------------- /KNN/data/Social_Network_Ads.csv: -------------------------------------------------------------------------------- 1 | User ID,Gender,Age,EstimatedSalary,Purchased 2 | 15624510,Male,19,19000,0 3 | 15810944,Male,35,20000,0 4 | 15668575,Female,26,43000,0 5 | 15603246,Female,27,57000,0 6 | 15804002,Male,19,76000,0 7 | 15728773,Male,27,58000,0 8 | 15598044,Female,27,84000,0 9 | 15694829,Female,32,150000,1 10 | 15600575,Male,25,33000,0 11 | 15727311,Female,35,65000,0 12 | 15570769,Female,26,80000,0 13 | 15606274,Female,26,52000,0 14 | 15746139,Male,20,86000,0 15 | 15704987,Male,32,18000,0 16 | 15628972,Male,18,82000,0 17 | 15697686,Male,29,80000,0 18 | 15733883,Male,47,25000,1 19 | 15617482,Male,45,26000,1 20 | 15704583,Male,46,28000,1 21 | 15621083,Female,48,29000,1 22 | 15649487,Male,45,22000,1 23 | 15736760,Female,47,49000,1 24 | 15714658,Male,48,41000,1 25 | 15599081,Female,45,22000,1 26 | 15705113,Male,46,23000,1 27 | 15631159,Male,47,20000,1 28 | 15792818,Male,49,28000,1 29 | 15633531,Female,47,30000,1 30 | 15744529,Male,29,43000,0 31 | 15669656,Male,31,18000,0 32 | 15581198,Male,31,74000,0 33 | 15729054,Female,27,137000,1 34 | 15573452,Female,21,16000,0 35 | 15776733,Female,28,44000,0 36 | 15724858,Male,27,90000,0 37 | 15713144,Male,35,27000,0 38 | 15690188,Female,33,28000,0 39 | 15689425,Male,30,49000,0 40 | 15671766,Female,26,72000,0 41 | 15782806,Female,27,31000,0 42 | 15764419,Female,27,17000,0 43 | 15591915,Female,33,51000,0 44 | 15772798,Male,35,108000,0 45 | 15792008,Male,30,15000,0 46 | 15715541,Female,28,84000,0 47 | 15639277,Male,23,20000,0 48 | 15798850,Male,25,79000,0 49 | 15776348,Female,27,54000,0 50 | 15727696,Male,30,135000,1 51 | 15793813,Female,31,89000,0 52 | 15694395,Female,24,32000,0 53 | 15764195,Female,18,44000,0 54 | 15744919,Female,29,83000,0 55 | 15671655,Female,35,23000,0 56 | 15654901,Female,27,58000,0 57 | 15649136,Female,24,55000,0 58 | 15775562,Female,23,48000,0 59 | 15807481,Male,28,79000,0 60 | 15642885,Male,22,18000,0 61 | 15789109,Female,32,117000,0 62 | 15814004,Male,27,20000,0 63 | 15673619,Male,25,87000,0 64 | 15595135,Female,23,66000,0 65 | 15583681,Male,32,120000,1 66 | 15605000,Female,59,83000,0 67 | 15718071,Male,24,58000,0 68 | 15679760,Male,24,19000,0 69 | 15654574,Female,23,82000,0 70 | 15577178,Female,22,63000,0 71 | 15595324,Female,31,68000,0 72 | 15756932,Male,25,80000,0 73 | 15726358,Female,24,27000,0 74 | 15595228,Female,20,23000,0 75 | 15782530,Female,33,113000,0 76 | 15592877,Male,32,18000,0 77 | 15651983,Male,34,112000,1 78 | 15746737,Male,18,52000,0 79 | 15774179,Female,22,27000,0 80 | 15667265,Female,28,87000,0 81 | 15655123,Female,26,17000,0 82 | 15595917,Male,30,80000,0 83 | 15668385,Male,39,42000,0 84 | 15709476,Male,20,49000,0 85 | 15711218,Male,35,88000,0 86 | 15798659,Female,30,62000,0 87 | 15663939,Female,31,118000,1 88 | 15694946,Male,24,55000,0 89 | 15631912,Female,28,85000,0 90 | 15768816,Male,26,81000,0 91 | 15682268,Male,35,50000,0 92 | 15684801,Male,22,81000,0 93 | 15636428,Female,30,116000,0 94 | 15809823,Male,26,15000,0 95 | 15699284,Female,29,28000,0 96 | 15786993,Female,29,83000,0 97 | 15709441,Female,35,44000,0 98 | 15710257,Female,35,25000,0 99 | 15582492,Male,28,123000,1 100 | 15575694,Male,35,73000,0 101 | 15756820,Female,28,37000,0 102 | 15766289,Male,27,88000,0 103 | 15593014,Male,28,59000,0 104 | 15584545,Female,32,86000,0 105 | 15675949,Female,33,149000,1 106 | 15672091,Female,19,21000,0 107 | 15801658,Male,21,72000,0 108 | 15706185,Female,26,35000,0 109 | 15789863,Male,27,89000,0 110 | 15720943,Male,26,86000,0 111 | 15697997,Female,38,80000,0 112 | 15665416,Female,39,71000,0 113 | 15660200,Female,37,71000,0 114 | 15619653,Male,38,61000,0 115 | 15773447,Male,37,55000,0 116 | 15739160,Male,42,80000,0 117 | 15689237,Male,40,57000,0 118 | 15679297,Male,35,75000,0 119 | 15591433,Male,36,52000,0 120 | 15642725,Male,40,59000,0 121 | 15701962,Male,41,59000,0 122 | 15811613,Female,36,75000,0 123 | 15741049,Male,37,72000,0 124 | 15724423,Female,40,75000,0 125 | 15574305,Male,35,53000,0 126 | 15678168,Female,41,51000,0 127 | 15697020,Female,39,61000,0 128 | 15610801,Male,42,65000,0 129 | 15745232,Male,26,32000,0 130 | 15722758,Male,30,17000,0 131 | 15792102,Female,26,84000,0 132 | 15675185,Male,31,58000,0 133 | 15801247,Male,33,31000,0 134 | 15725660,Male,30,87000,0 135 | 15638963,Female,21,68000,0 136 | 15800061,Female,28,55000,0 137 | 15578006,Male,23,63000,0 138 | 15668504,Female,20,82000,0 139 | 15687491,Male,30,107000,1 140 | 15610403,Female,28,59000,0 141 | 15741094,Male,19,25000,0 142 | 15807909,Male,19,85000,0 143 | 15666141,Female,18,68000,0 144 | 15617134,Male,35,59000,0 145 | 15783029,Male,30,89000,0 146 | 15622833,Female,34,25000,0 147 | 15746422,Female,24,89000,0 148 | 15750839,Female,27,96000,1 149 | 15749130,Female,41,30000,0 150 | 15779862,Male,29,61000,0 151 | 15767871,Male,20,74000,0 152 | 15679651,Female,26,15000,0 153 | 15576219,Male,41,45000,0 154 | 15699247,Male,31,76000,0 155 | 15619087,Female,36,50000,0 156 | 15605327,Male,40,47000,0 157 | 15610140,Female,31,15000,0 158 | 15791174,Male,46,59000,0 159 | 15602373,Male,29,75000,0 160 | 15762605,Male,26,30000,0 161 | 15598840,Female,32,135000,1 162 | 15744279,Male,32,100000,1 163 | 15670619,Male,25,90000,0 164 | 15599533,Female,37,33000,0 165 | 15757837,Male,35,38000,0 166 | 15697574,Female,33,69000,0 167 | 15578738,Female,18,86000,0 168 | 15762228,Female,22,55000,0 169 | 15614827,Female,35,71000,0 170 | 15789815,Male,29,148000,1 171 | 15579781,Female,29,47000,0 172 | 15587013,Male,21,88000,0 173 | 15570932,Male,34,115000,0 174 | 15794661,Female,26,118000,0 175 | 15581654,Female,34,43000,0 176 | 15644296,Female,34,72000,0 177 | 15614420,Female,23,28000,0 178 | 15609653,Female,35,47000,0 179 | 15594577,Male,25,22000,0 180 | 15584114,Male,24,23000,0 181 | 15673367,Female,31,34000,0 182 | 15685576,Male,26,16000,0 183 | 15774727,Female,31,71000,0 184 | 15694288,Female,32,117000,1 185 | 15603319,Male,33,43000,0 186 | 15759066,Female,33,60000,0 187 | 15814816,Male,31,66000,0 188 | 15724402,Female,20,82000,0 189 | 15571059,Female,33,41000,0 190 | 15674206,Male,35,72000,0 191 | 15715160,Male,28,32000,0 192 | 15730448,Male,24,84000,0 193 | 15662067,Female,19,26000,0 194 | 15779581,Male,29,43000,0 195 | 15662901,Male,19,70000,0 196 | 15689751,Male,28,89000,0 197 | 15667742,Male,34,43000,0 198 | 15738448,Female,30,79000,0 199 | 15680243,Female,20,36000,0 200 | 15745083,Male,26,80000,0 201 | 15708228,Male,35,22000,0 202 | 15628523,Male,35,39000,0 203 | 15708196,Male,49,74000,0 204 | 15735549,Female,39,134000,1 205 | 15809347,Female,41,71000,0 206 | 15660866,Female,58,101000,1 207 | 15766609,Female,47,47000,0 208 | 15654230,Female,55,130000,1 209 | 15794566,Female,52,114000,0 210 | 15800890,Female,40,142000,1 211 | 15697424,Female,46,22000,0 212 | 15724536,Female,48,96000,1 213 | 15735878,Male,52,150000,1 214 | 15707596,Female,59,42000,0 215 | 15657163,Male,35,58000,0 216 | 15622478,Male,47,43000,0 217 | 15779529,Female,60,108000,1 218 | 15636023,Male,49,65000,0 219 | 15582066,Male,40,78000,0 220 | 15666675,Female,46,96000,0 221 | 15732987,Male,59,143000,1 222 | 15789432,Female,41,80000,0 223 | 15663161,Male,35,91000,1 224 | 15694879,Male,37,144000,1 225 | 15593715,Male,60,102000,1 226 | 15575002,Female,35,60000,0 227 | 15622171,Male,37,53000,0 228 | 15795224,Female,36,126000,1 229 | 15685346,Male,56,133000,1 230 | 15691808,Female,40,72000,0 231 | 15721007,Female,42,80000,1 232 | 15794253,Female,35,147000,1 233 | 15694453,Male,39,42000,0 234 | 15813113,Male,40,107000,1 235 | 15614187,Male,49,86000,1 236 | 15619407,Female,38,112000,0 237 | 15646227,Male,46,79000,1 238 | 15660541,Male,40,57000,0 239 | 15753874,Female,37,80000,0 240 | 15617877,Female,46,82000,0 241 | 15772073,Female,53,143000,1 242 | 15701537,Male,42,149000,1 243 | 15736228,Male,38,59000,0 244 | 15780572,Female,50,88000,1 245 | 15769596,Female,56,104000,1 246 | 15586996,Female,41,72000,0 247 | 15722061,Female,51,146000,1 248 | 15638003,Female,35,50000,0 249 | 15775590,Female,57,122000,1 250 | 15730688,Male,41,52000,0 251 | 15753102,Female,35,97000,1 252 | 15810075,Female,44,39000,0 253 | 15723373,Male,37,52000,0 254 | 15795298,Female,48,134000,1 255 | 15584320,Female,37,146000,1 256 | 15724161,Female,50,44000,0 257 | 15750056,Female,52,90000,1 258 | 15609637,Female,41,72000,0 259 | 15794493,Male,40,57000,0 260 | 15569641,Female,58,95000,1 261 | 15815236,Female,45,131000,1 262 | 15811177,Female,35,77000,0 263 | 15680587,Male,36,144000,1 264 | 15672821,Female,55,125000,1 265 | 15767681,Female,35,72000,0 266 | 15600379,Male,48,90000,1 267 | 15801336,Female,42,108000,1 268 | 15721592,Male,40,75000,0 269 | 15581282,Male,37,74000,0 270 | 15746203,Female,47,144000,1 271 | 15583137,Male,40,61000,0 272 | 15680752,Female,43,133000,0 273 | 15688172,Female,59,76000,1 274 | 15791373,Male,60,42000,1 275 | 15589449,Male,39,106000,1 276 | 15692819,Female,57,26000,1 277 | 15727467,Male,57,74000,1 278 | 15734312,Male,38,71000,0 279 | 15764604,Male,49,88000,1 280 | 15613014,Female,52,38000,1 281 | 15759684,Female,50,36000,1 282 | 15609669,Female,59,88000,1 283 | 15685536,Male,35,61000,0 284 | 15750447,Male,37,70000,1 285 | 15663249,Female,52,21000,1 286 | 15638646,Male,48,141000,0 287 | 15734161,Female,37,93000,1 288 | 15631070,Female,37,62000,0 289 | 15761950,Female,48,138000,1 290 | 15649668,Male,41,79000,0 291 | 15713912,Female,37,78000,1 292 | 15586757,Male,39,134000,1 293 | 15596522,Male,49,89000,1 294 | 15625395,Male,55,39000,1 295 | 15760570,Male,37,77000,0 296 | 15566689,Female,35,57000,0 297 | 15725794,Female,36,63000,0 298 | 15673539,Male,42,73000,1 299 | 15705298,Female,43,112000,1 300 | 15675791,Male,45,79000,0 301 | 15747043,Male,46,117000,1 302 | 15736397,Female,58,38000,1 303 | 15678201,Male,48,74000,1 304 | 15720745,Female,37,137000,1 305 | 15637593,Male,37,79000,1 306 | 15598070,Female,40,60000,0 307 | 15787550,Male,42,54000,0 308 | 15603942,Female,51,134000,0 309 | 15733973,Female,47,113000,1 310 | 15596761,Male,36,125000,1 311 | 15652400,Female,38,50000,0 312 | 15717893,Female,42,70000,0 313 | 15622585,Male,39,96000,1 314 | 15733964,Female,38,50000,0 315 | 15753861,Female,49,141000,1 316 | 15747097,Female,39,79000,0 317 | 15594762,Female,39,75000,1 318 | 15667417,Female,54,104000,1 319 | 15684861,Male,35,55000,0 320 | 15742204,Male,45,32000,1 321 | 15623502,Male,36,60000,0 322 | 15774872,Female,52,138000,1 323 | 15611191,Female,53,82000,1 324 | 15674331,Male,41,52000,0 325 | 15619465,Female,48,30000,1 326 | 15575247,Female,48,131000,1 327 | 15695679,Female,41,60000,0 328 | 15713463,Male,41,72000,0 329 | 15785170,Female,42,75000,0 330 | 15796351,Male,36,118000,1 331 | 15639576,Female,47,107000,1 332 | 15693264,Male,38,51000,0 333 | 15589715,Female,48,119000,1 334 | 15769902,Male,42,65000,0 335 | 15587177,Male,40,65000,0 336 | 15814553,Male,57,60000,1 337 | 15601550,Female,36,54000,0 338 | 15664907,Male,58,144000,1 339 | 15612465,Male,35,79000,0 340 | 15810800,Female,38,55000,0 341 | 15665760,Male,39,122000,1 342 | 15588080,Female,53,104000,1 343 | 15776844,Male,35,75000,0 344 | 15717560,Female,38,65000,0 345 | 15629739,Female,47,51000,1 346 | 15729908,Male,47,105000,1 347 | 15716781,Female,41,63000,0 348 | 15646936,Male,53,72000,1 349 | 15768151,Female,54,108000,1 350 | 15579212,Male,39,77000,0 351 | 15721835,Male,38,61000,0 352 | 15800515,Female,38,113000,1 353 | 15591279,Male,37,75000,0 354 | 15587419,Female,42,90000,1 355 | 15750335,Female,37,57000,0 356 | 15699619,Male,36,99000,1 357 | 15606472,Male,60,34000,1 358 | 15778368,Male,54,70000,1 359 | 15671387,Female,41,72000,0 360 | 15573926,Male,40,71000,1 361 | 15709183,Male,42,54000,0 362 | 15577514,Male,43,129000,1 363 | 15778830,Female,53,34000,1 364 | 15768072,Female,47,50000,1 365 | 15768293,Female,42,79000,0 366 | 15654456,Male,42,104000,1 367 | 15807525,Female,59,29000,1 368 | 15574372,Female,58,47000,1 369 | 15671249,Male,46,88000,1 370 | 15779744,Male,38,71000,0 371 | 15624755,Female,54,26000,1 372 | 15611430,Female,60,46000,1 373 | 15774744,Male,60,83000,1 374 | 15629885,Female,39,73000,0 375 | 15708791,Male,59,130000,1 376 | 15793890,Female,37,80000,0 377 | 15646091,Female,46,32000,1 378 | 15596984,Female,46,74000,0 379 | 15800215,Female,42,53000,0 380 | 15577806,Male,41,87000,1 381 | 15749381,Female,58,23000,1 382 | 15683758,Male,42,64000,0 383 | 15670615,Male,48,33000,1 384 | 15715622,Female,44,139000,1 385 | 15707634,Male,49,28000,1 386 | 15806901,Female,57,33000,1 387 | 15775335,Male,56,60000,1 388 | 15724150,Female,49,39000,1 389 | 15627220,Male,39,71000,0 390 | 15672330,Male,47,34000,1 391 | 15668521,Female,48,35000,1 392 | 15807837,Male,48,33000,1 393 | 15592570,Male,47,23000,1 394 | 15748589,Female,45,45000,1 395 | 15635893,Male,60,42000,1 396 | 15757632,Female,39,59000,0 397 | 15691863,Female,46,41000,1 398 | 15706071,Male,51,23000,1 399 | 15654296,Female,50,20000,1 400 | 15755018,Male,36,33000,0 401 | 15594041,Female,49,36000,1 -------------------------------------------------------------------------------- /Decision Tree/data/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 -------------------------------------------------------------------------------- /Random Forest/Bagging/data/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 -------------------------------------------------------------------------------- /Random Forest/Bagging/bagging_diabetes_prediction.ipynb: -------------------------------------------------------------------------------- 1 | { 2 | "cells": [ 3 | { 4 | "cell_type": "markdown", 5 | "metadata": {}, 6 | "source": [ 7 | "

Ensemble Learning: Bagging Tutorial

" 8 | ] 9 | }, 10 | { 11 | "cell_type": "markdown", 12 | "metadata": {}, 13 | "source": [ 14 | "**We will use pima indian diabetes dataset to predict if a person has a diabetes or not based on certain features such as blood pressure, skin thickness, age etc. We will train a standalone model first and then use bagging ensemble technique to check how it can improve the performance of the model**\n", 15 | "\n", 16 | "dataset credit: https://www.kaggle.com/gargmanas/pima-indians-diabetes" 17 | ] 18 | }, 19 | { 20 | "cell_type": "code", 21 | "execution_count": 1, 22 | "metadata": {}, 23 | "outputs": [], 24 | "source": [ 25 | "import pandas as pd" 26 | ] 27 | }, 28 | { 29 | "cell_type": "code", 30 | "execution_count": 2, 31 | "metadata": {}, 32 | "outputs": [ 33 | { 34 | "data": { 35 | "text/html": [ 36 | "
\n", 37 | "\n", 50 | "\n", 51 | " \n", 52 | " \n", 53 | " \n", 54 | " \n", 55 | " \n", 56 | " \n", 57 | " \n", 58 | " \n", 59 | " \n", 60 | " \n", 61 | " \n", 62 | " \n", 63 | " \n", 64 | " \n", 65 | " \n", 66 | " \n", 67 | " \n", 68 | " \n", 69 | " \n", 70 | " \n", 71 | " \n", 72 | " \n", 73 | " \n", 74 | " \n", 75 | " \n", 76 | " \n", 77 | " \n", 78 | " \n", 79 | " \n", 80 | " \n", 81 | " \n", 82 | " \n", 83 | " \n", 84 | " \n", 85 | " \n", 86 | " \n", 87 | " \n", 88 | " \n", 89 | " \n", 90 | " \n", 91 | " \n", 92 | " \n", 93 | " \n", 94 | " \n", 95 | " \n", 96 | " \n", 97 | " \n", 98 | " \n", 99 | " \n", 100 | " \n", 101 | " \n", 102 | " \n", 103 | " \n", 104 | " \n", 105 | " \n", 106 | " \n", 107 | " \n", 108 | " \n", 109 | " \n", 110 | " \n", 111 | " \n", 112 | " \n", 113 | " \n", 114 | " \n", 115 | " \n", 116 | " \n", 117 | " \n", 118 | " \n", 119 | " \n", 120 | " \n", 121 | " \n", 122 | " \n", 123 | " \n", 124 | " \n", 125 | " \n", 126 | " \n", 127 | "
PregnanciesGlucoseBloodPressureSkinThicknessInsulinBMIDiabetesPedigreeFunctionAgeOutcome
061487235033.60.627501
11856629026.60.351310
28183640023.30.672321
318966239428.10.167210
40137403516843.12.288331
\n", 128 | "
" 129 | ], 130 | "text/plain": [ 131 | " Pregnancies Glucose BloodPressure SkinThickness Insulin BMI \\\n", 132 | "0 6 148 72 35 0 33.6 \n", 133 | "1 1 85 66 29 0 26.6 \n", 134 | "2 8 183 64 0 0 23.3 \n", 135 | "3 1 89 66 23 94 28.1 \n", 136 | "4 0 137 40 35 168 43.1 \n", 137 | "\n", 138 | " DiabetesPedigreeFunction Age Outcome \n", 139 | "0 0.627 50 1 \n", 140 | "1 0.351 31 0 \n", 141 | "2 0.672 32 1 \n", 142 | "3 0.167 21 0 \n", 143 | "4 2.288 33 1 " 144 | ] 145 | }, 146 | "execution_count": 2, 147 | "metadata": {}, 148 | "output_type": "execute_result" 149 | } 150 | ], 151 | "source": [ 152 | "df = pd.read_csv(\"data/diabetes.csv\")\n", 153 | "df.head()" 154 | ] 155 | }, 156 | { 157 | "cell_type": "code", 158 | "execution_count": 3, 159 | "metadata": {}, 160 | "outputs": [ 161 | { 162 | "data": { 163 | "text/plain": [ 164 | "Pregnancies 0\n", 165 | "Glucose 0\n", 166 | "BloodPressure 0\n", 167 | "SkinThickness 0\n", 168 | "Insulin 0\n", 169 | "BMI 0\n", 170 | "DiabetesPedigreeFunction 0\n", 171 | "Age 0\n", 172 | "Outcome 0\n", 173 | "dtype: int64" 174 | ] 175 | }, 176 | "execution_count": 3, 177 | "metadata": {}, 178 | "output_type": "execute_result" 179 | } 180 | ], 181 | "source": [ 182 | "df.isnull().sum()" 183 | ] 184 | }, 185 | { 186 | "cell_type": "code", 187 | "execution_count": 4, 188 | "metadata": { 189 | "scrolled": true 190 | }, 191 | "outputs": [ 192 | { 193 | "data": { 194 | "text/html": [ 195 | "
\n", 196 | "\n", 209 | "\n", 210 | " \n", 211 | " \n", 212 | " \n", 213 | " \n", 214 | " \n", 215 | " \n", 216 | " \n", 217 | " \n", 218 | " \n", 219 | " \n", 220 | " \n", 221 | " \n", 222 | " \n", 223 | " \n", 224 | " \n", 225 | " \n", 226 | " \n", 227 | " \n", 228 | " \n", 229 | " \n", 230 | " \n", 231 | " \n", 232 | " \n", 233 | " \n", 234 | " \n", 235 | " \n", 236 | " \n", 237 | " \n", 238 | " \n", 239 | " \n", 240 | " \n", 241 | " \n", 242 | " \n", 243 | " \n", 244 | " \n", 245 | " \n", 246 | " \n", 247 | " \n", 248 | " \n", 249 | " \n", 250 | " \n", 251 | " \n", 252 | " \n", 253 | " \n", 254 | " \n", 255 | " \n", 256 | " \n", 257 | " \n", 258 | " \n", 259 | " \n", 260 | " \n", 261 | " \n", 262 | " \n", 263 | " \n", 264 | " \n", 265 | " \n", 266 | " \n", 267 | " \n", 268 | " \n", 269 | " \n", 270 | " \n", 271 | " \n", 272 | " \n", 273 | " \n", 274 | " \n", 275 | " \n", 276 | " \n", 277 | " \n", 278 | " \n", 279 | " \n", 280 | " \n", 281 | " \n", 282 | " \n", 283 | " \n", 284 | " \n", 285 | " \n", 286 | " \n", 287 | " \n", 288 | " \n", 289 | " \n", 290 | " \n", 291 | " \n", 292 | " \n", 293 | " \n", 294 | " \n", 295 | " \n", 296 | " \n", 297 | " \n", 298 | " \n", 299 | " \n", 300 | " \n", 301 | " \n", 302 | " \n", 303 | " \n", 304 | " \n", 305 | " \n", 306 | " \n", 307 | " \n", 308 | " \n", 309 | " \n", 310 | " \n", 311 | " \n", 312 | " \n", 313 | " \n", 314 | " \n", 315 | " \n", 316 | " \n", 317 | " \n", 318 | " \n", 319 | " \n", 320 | " \n", 321 | " \n", 322 | "
PregnanciesGlucoseBloodPressureSkinThicknessInsulinBMIDiabetesPedigreeFunctionAgeOutcome
count768.000000768.000000768.000000768.000000768.000000768.000000768.000000768.000000768.000000
mean3.845052120.89453169.10546920.53645879.79947931.9925780.47187633.2408850.348958
std3.36957831.97261819.35580715.952218115.2440027.8841600.33132911.7602320.476951
min0.0000000.0000000.0000000.0000000.0000000.0000000.07800021.0000000.000000
25%1.00000099.00000062.0000000.0000000.00000027.3000000.24375024.0000000.000000
50%3.000000117.00000072.00000023.00000030.50000032.0000000.37250029.0000000.000000
75%6.000000140.25000080.00000032.000000127.25000036.6000000.62625041.0000001.000000
max17.000000199.000000122.00000099.000000846.00000067.1000002.42000081.0000001.000000
\n", 323 | "
" 324 | ], 325 | "text/plain": [ 326 | " Pregnancies Glucose BloodPressure SkinThickness Insulin \\\n", 327 | "count 768.000000 768.000000 768.000000 768.000000 768.000000 \n", 328 | "mean 3.845052 120.894531 69.105469 20.536458 79.799479 \n", 329 | "std 3.369578 31.972618 19.355807 15.952218 115.244002 \n", 330 | "min 0.000000 0.000000 0.000000 0.000000 0.000000 \n", 331 | "25% 1.000000 99.000000 62.000000 0.000000 0.000000 \n", 332 | "50% 3.000000 117.000000 72.000000 23.000000 30.500000 \n", 333 | "75% 6.000000 140.250000 80.000000 32.000000 127.250000 \n", 334 | "max 17.000000 199.000000 122.000000 99.000000 846.000000 \n", 335 | "\n", 336 | " BMI DiabetesPedigreeFunction Age Outcome \n", 337 | "count 768.000000 768.000000 768.000000 768.000000 \n", 338 | "mean 31.992578 0.471876 33.240885 0.348958 \n", 339 | "std 7.884160 0.331329 11.760232 0.476951 \n", 340 | "min 0.000000 0.078000 21.000000 0.000000 \n", 341 | "25% 27.300000 0.243750 24.000000 0.000000 \n", 342 | "50% 32.000000 0.372500 29.000000 0.000000 \n", 343 | "75% 36.600000 0.626250 41.000000 1.000000 \n", 344 | "max 67.100000 2.420000 81.000000 1.000000 " 345 | ] 346 | }, 347 | "execution_count": 4, 348 | "metadata": {}, 349 | "output_type": "execute_result" 350 | } 351 | ], 352 | "source": [ 353 | "df.describe()" 354 | ] 355 | }, 356 | { 357 | "cell_type": "code", 358 | "execution_count": 5, 359 | "metadata": {}, 360 | "outputs": [ 361 | { 362 | "data": { 363 | "text/plain": [ 364 | "Outcome\n", 365 | "0 500\n", 366 | "1 268\n", 367 | "Name: count, dtype: int64" 368 | ] 369 | }, 370 | "execution_count": 5, 371 | "metadata": {}, 372 | "output_type": "execute_result" 373 | } 374 | ], 375 | "source": [ 376 | "df.Outcome.value_counts()" 377 | ] 378 | }, 379 | { 380 | "cell_type": "markdown", 381 | "metadata": {}, 382 | "source": [ 383 | "There is slight imbalance in our dataset but since it is not major we will not worry about it!" 384 | ] 385 | }, 386 | { 387 | "cell_type": "markdown", 388 | "metadata": {}, 389 | "source": [ 390 | "

Train test split

" 391 | ] 392 | }, 393 | { 394 | "cell_type": "code", 395 | "execution_count": 6, 396 | "metadata": {}, 397 | "outputs": [], 398 | "source": [ 399 | "X = df.drop(\"Outcome\",axis=\"columns\")\n", 400 | "y = df.Outcome" 401 | ] 402 | }, 403 | { 404 | "cell_type": "code", 405 | "execution_count": 7, 406 | "metadata": {}, 407 | "outputs": [ 408 | { 409 | "data": { 410 | "text/html": [ 411 | "
\n", 412 | "\n", 425 | "\n", 426 | " \n", 427 | " \n", 428 | " \n", 429 | " \n", 430 | " \n", 431 | " \n", 432 | " \n", 433 | " \n", 434 | " \n", 435 | " \n", 436 | " \n", 437 | " \n", 438 | " \n", 439 | " \n", 440 | " \n", 441 | " \n", 442 | " \n", 443 | " \n", 444 | " \n", 445 | " \n", 446 | " \n", 447 | " \n", 448 | " \n", 449 | " \n", 450 | " \n", 451 | " \n", 452 | " \n", 453 | " \n", 454 | " \n", 455 | " \n", 456 | " \n", 457 | " \n", 458 | " \n", 459 | " \n", 460 | " \n", 461 | " \n", 462 | " \n", 463 | " \n", 464 | " \n", 465 | " \n", 466 | " \n", 467 | " \n", 468 | " \n", 469 | " \n", 470 | " \n", 471 | " \n", 472 | " \n", 473 | " \n", 474 | " \n", 475 | " \n", 476 | " \n", 477 | " \n", 478 | " \n", 479 | " \n", 480 | " \n", 481 | " \n", 482 | " \n", 483 | " \n", 484 | " \n", 485 | " \n", 486 | " \n", 487 | " \n", 488 | " \n", 489 | " \n", 490 | " \n", 491 | " \n", 492 | " \n", 493 | " \n", 494 | " \n", 495 | " \n", 496 | " \n", 497 | " \n", 498 | " \n", 499 | " \n", 500 | " \n", 501 | " \n", 502 | " \n", 503 | " \n", 504 | " \n", 505 | " \n", 506 | " \n", 507 | " \n", 508 | " \n", 509 | " \n", 510 | " \n", 511 | " \n", 512 | " \n", 513 | " \n", 514 | " \n", 515 | " \n", 516 | " \n", 517 | " \n", 518 | " \n", 519 | " \n", 520 | " \n", 521 | " \n", 522 | " \n", 523 | " \n", 524 | " \n", 525 | " \n", 526 | " \n", 527 | " \n", 528 | " \n", 529 | " \n", 530 | " \n", 531 | " \n", 532 | " \n", 533 | " \n", 534 | " \n", 535 | " \n", 536 | " \n", 537 | " \n", 538 | " \n", 539 | " \n", 540 | " \n", 541 | " \n", 542 | " \n", 543 | " \n", 544 | " \n", 545 | " \n", 546 | " \n", 547 | " \n", 548 | " \n", 549 | " \n", 550 | " \n", 551 | " \n", 552 | " \n", 553 | " \n", 554 | " \n", 555 | " \n", 556 | " \n", 557 | " \n", 558 | " \n", 559 | " \n", 560 | " \n", 561 | " \n", 562 | "
PregnanciesGlucoseBloodPressureSkinThicknessInsulinBMIDiabetesPedigreeFunctionAge
061487235033.60.62750
11856629026.60.35131
28183640023.30.67232
318966239428.10.16721
40137403516843.12.28833
...........................
76310101764818032.90.17163
76421227027036.80.34027
7655121722311226.20.24530
7661126600030.10.34947
7671937031030.40.31523
\n", 563 | "

768 rows × 8 columns

\n", 564 | "
" 565 | ], 566 | "text/plain": [ 567 | " Pregnancies Glucose BloodPressure SkinThickness Insulin BMI \\\n", 568 | "0 6 148 72 35 0 33.6 \n", 569 | "1 1 85 66 29 0 26.6 \n", 570 | "2 8 183 64 0 0 23.3 \n", 571 | "3 1 89 66 23 94 28.1 \n", 572 | "4 0 137 40 35 168 43.1 \n", 573 | ".. ... ... ... ... ... ... \n", 574 | "763 10 101 76 48 180 32.9 \n", 575 | "764 2 122 70 27 0 36.8 \n", 576 | "765 5 121 72 23 112 26.2 \n", 577 | "766 1 126 60 0 0 30.1 \n", 578 | "767 1 93 70 31 0 30.4 \n", 579 | "\n", 580 | " DiabetesPedigreeFunction Age \n", 581 | "0 0.627 50 \n", 582 | "1 0.351 31 \n", 583 | "2 0.672 32 \n", 584 | "3 0.167 21 \n", 585 | "4 2.288 33 \n", 586 | ".. ... ... \n", 587 | "763 0.171 63 \n", 588 | "764 0.340 27 \n", 589 | "765 0.245 30 \n", 590 | "766 0.349 47 \n", 591 | "767 0.315 23 \n", 592 | "\n", 593 | "[768 rows x 8 columns]" 594 | ] 595 | }, 596 | "execution_count": 7, 597 | "metadata": {}, 598 | "output_type": "execute_result" 599 | } 600 | ], 601 | "source": [ 602 | "X" 603 | ] 604 | }, 605 | { 606 | "cell_type": "code", 607 | "execution_count": 8, 608 | "metadata": {}, 609 | "outputs": [ 610 | { 611 | "data": { 612 | "text/plain": [ 613 | "0 1\n", 614 | "1 0\n", 615 | "2 1\n", 616 | "3 0\n", 617 | "4 1\n", 618 | " ..\n", 619 | "763 0\n", 620 | "764 0\n", 621 | "765 0\n", 622 | "766 1\n", 623 | "767 0\n", 624 | "Name: Outcome, Length: 768, dtype: int64" 625 | ] 626 | }, 627 | "execution_count": 8, 628 | "metadata": {}, 629 | "output_type": "execute_result" 630 | } 631 | ], 632 | "source": [ 633 | "y" 634 | ] 635 | }, 636 | { 637 | "cell_type": "code", 638 | "execution_count": 9, 639 | "metadata": {}, 640 | "outputs": [ 641 | { 642 | "data": { 643 | "text/plain": [ 644 | "array([[ 0.63994726, 0.84832379, 0.14964075, 0.90726993, -0.69289057,\n", 645 | " 0.20401277, 0.46849198, 1.4259954 ],\n", 646 | " [-0.84488505, -1.12339636, -0.16054575, 0.53090156, -0.69289057,\n", 647 | " -0.68442195, -0.36506078, -0.19067191],\n", 648 | " [ 1.23388019, 1.94372388, -0.26394125, -1.28821221, -0.69289057,\n", 649 | " -1.10325546, 0.60439732, -0.10558415]])" 650 | ] 651 | }, 652 | "execution_count": 9, 653 | "metadata": {}, 654 | "output_type": "execute_result" 655 | } 656 | ], 657 | "source": [ 658 | "from sklearn.preprocessing import StandardScaler\n", 659 | "\n", 660 | "scaler = StandardScaler()\n", 661 | "X_scaled = scaler.fit_transform(X)\n", 662 | "X_scaled[:3]" 663 | ] 664 | }, 665 | { 666 | "cell_type": "code", 667 | "execution_count": 10, 668 | "metadata": {}, 669 | "outputs": [], 670 | "source": [ 671 | "from sklearn.model_selection import train_test_split\n", 672 | "\n", 673 | "X_train, X_test, y_train, y_test = train_test_split(X_scaled, y, stratify=y, random_state=10)" 674 | ] 675 | }, 676 | { 677 | "cell_type": "code", 678 | "execution_count": 11, 679 | "metadata": {}, 680 | "outputs": [ 681 | { 682 | "data": { 683 | "text/plain": [ 684 | "(576, 8)" 685 | ] 686 | }, 687 | "execution_count": 11, 688 | "metadata": {}, 689 | "output_type": "execute_result" 690 | } 691 | ], 692 | "source": [ 693 | "X_train.shape" 694 | ] 695 | }, 696 | { 697 | "cell_type": "code", 698 | "execution_count": 10, 699 | "metadata": {}, 700 | "outputs": [ 701 | { 702 | "data": { 703 | "text/plain": [ 704 | "(192, 8)" 705 | ] 706 | }, 707 | "execution_count": 10, 708 | "metadata": {}, 709 | "output_type": "execute_result" 710 | } 711 | ], 712 | "source": [ 713 | "X_test.shape" 714 | ] 715 | }, 716 | { 717 | "cell_type": "code", 718 | "execution_count": 13, 719 | "metadata": { 720 | "scrolled": true 721 | }, 722 | "outputs": [ 723 | { 724 | "data": { 725 | "text/plain": [ 726 | "Outcome\n", 727 | "0 375\n", 728 | "1 201\n", 729 | "Name: count, dtype: int64" 730 | ] 731 | }, 732 | "execution_count": 13, 733 | "metadata": {}, 734 | "output_type": "execute_result" 735 | } 736 | ], 737 | "source": [ 738 | "y_train.value_counts()" 739 | ] 740 | }, 741 | { 742 | "cell_type": "code", 743 | "execution_count": 14, 744 | "metadata": {}, 745 | "outputs": [ 746 | { 747 | "data": { 748 | "text/plain": [ 749 | "0.536" 750 | ] 751 | }, 752 | "execution_count": 14, 753 | "metadata": {}, 754 | "output_type": "execute_result" 755 | } 756 | ], 757 | "source": [ 758 | "201/375" 759 | ] 760 | }, 761 | { 762 | "cell_type": "code", 763 | "execution_count": 15, 764 | "metadata": {}, 765 | "outputs": [ 766 | { 767 | "data": { 768 | "text/plain": [ 769 | "Outcome\n", 770 | "0 125\n", 771 | "1 67\n", 772 | "Name: count, dtype: int64" 773 | ] 774 | }, 775 | "execution_count": 15, 776 | "metadata": {}, 777 | "output_type": "execute_result" 778 | } 779 | ], 780 | "source": [ 781 | "y_test.value_counts()" 782 | ] 783 | }, 784 | { 785 | "cell_type": "code", 786 | "execution_count": 16, 787 | "metadata": { 788 | "scrolled": true 789 | }, 790 | "outputs": [ 791 | { 792 | "data": { 793 | "text/plain": [ 794 | "0.536" 795 | ] 796 | }, 797 | "execution_count": 16, 798 | "metadata": {}, 799 | "output_type": "execute_result" 800 | } 801 | ], 802 | "source": [ 803 | "67/125" 804 | ] 805 | }, 806 | { 807 | "cell_type": "markdown", 808 | "metadata": {}, 809 | "source": [ 810 | "

Train using stand alone model

" 811 | ] 812 | }, 813 | { 814 | "cell_type": "code", 815 | "execution_count": 21, 816 | "metadata": {}, 817 | "outputs": [ 818 | { 819 | "data": { 820 | "text/plain": [ 821 | "array([0.7012987 , 0.74025974, 0.74025974, 0.5974026 , 0.68831169,\n", 822 | " 0.72727273, 0.83116883, 0.79220779, 0.61842105, 0.75 ])" 823 | ] 824 | }, 825 | "execution_count": 21, 826 | "metadata": {}, 827 | "output_type": "execute_result" 828 | } 829 | ], 830 | "source": [ 831 | "from sklearn.model_selection import cross_val_score\n", 832 | "from sklearn.tree import DecisionTreeClassifier\n", 833 | "\n", 834 | "scores = cross_val_score(DecisionTreeClassifier(), X, y, cv=10)\n", 835 | "scores" 836 | ] 837 | }, 838 | { 839 | "cell_type": "code", 840 | "execution_count": 23, 841 | "metadata": {}, 842 | "outputs": [ 843 | { 844 | "data": { 845 | "text/plain": [ 846 | "0.7186602870813397" 847 | ] 848 | }, 849 | "execution_count": 23, 850 | "metadata": {}, 851 | "output_type": "execute_result" 852 | } 853 | ], 854 | "source": [ 855 | "scores.mean()" 856 | ] 857 | }, 858 | { 859 | "cell_type": "markdown", 860 | "metadata": {}, 861 | "source": [ 862 | "

Train using Bagging

" 863 | ] 864 | }, 865 | { 866 | "cell_type": "code", 867 | "execution_count": 24, 868 | "metadata": {}, 869 | "outputs": [ 870 | { 871 | "data": { 872 | "text/plain": [ 873 | "0.7534722222222222" 874 | ] 875 | }, 876 | "execution_count": 24, 877 | "metadata": {}, 878 | "output_type": "execute_result" 879 | } 880 | ], 881 | "source": [ 882 | "from sklearn.ensemble import BaggingClassifier\n", 883 | "\n", 884 | "bag_model = BaggingClassifier(\n", 885 | " estimator=DecisionTreeClassifier(), \n", 886 | " n_estimators=100, \n", 887 | " max_samples=0.8, \n", 888 | " oob_score=True,\n", 889 | " random_state=0\n", 890 | ")\n", 891 | "bag_model.fit(X_train, y_train)\n", 892 | "bag_model.oob_score_" 893 | ] 894 | }, 895 | { 896 | "cell_type": "code", 897 | "execution_count": 25, 898 | "metadata": {}, 899 | "outputs": [ 900 | { 901 | "data": { 902 | "text/plain": [ 903 | "0.7760416666666666" 904 | ] 905 | }, 906 | "execution_count": 25, 907 | "metadata": {}, 908 | "output_type": "execute_result" 909 | } 910 | ], 911 | "source": [ 912 | "bag_model.score(X_test, y_test)" 913 | ] 914 | }, 915 | { 916 | "cell_type": "code", 917 | "execution_count": 28, 918 | "metadata": {}, 919 | "outputs": [ 920 | { 921 | "data": { 922 | "text/plain": [ 923 | "array([0.71428571, 0.76623377, 0.77922078, 0.66233766, 0.75324675,\n", 924 | " 0.81818182, 0.81818182, 0.83116883, 0.67105263, 0.80263158])" 925 | ] 926 | }, 927 | "execution_count": 28, 928 | "metadata": {}, 929 | "output_type": "execute_result" 930 | } 931 | ], 932 | "source": [ 933 | "bag_model = BaggingClassifier(\n", 934 | " estimator=DecisionTreeClassifier(), \n", 935 | " n_estimators=100, \n", 936 | " max_samples=0.8, \n", 937 | " oob_score=True,\n", 938 | " random_state=0\n", 939 | ")\n", 940 | "scores = cross_val_score(bag_model, X, y, cv=10)\n", 941 | "scores" 942 | ] 943 | }, 944 | { 945 | "cell_type": "code", 946 | "execution_count": 29, 947 | "metadata": {}, 948 | "outputs": [ 949 | { 950 | "data": { 951 | "text/plain": [ 952 | "0.7616541353383459" 953 | ] 954 | }, 955 | "execution_count": 29, 956 | "metadata": {}, 957 | "output_type": "execute_result" 958 | } 959 | ], 960 | "source": [ 961 | "scores.mean()" 962 | ] 963 | }, 964 | { 965 | "cell_type": "markdown", 966 | "metadata": {}, 967 | "source": [ 968 | "We can see some improvement in test score with bagging classifier as compared to a standalone classifier" 969 | ] 970 | } 971 | ], 972 | "metadata": { 973 | "kernelspec": { 974 | "display_name": "Python 3", 975 | "language": "python", 976 | "name": "python3" 977 | }, 978 | "language_info": { 979 | "codemirror_mode": { 980 | "name": "ipython", 981 | "version": 3 982 | }, 983 | "file_extension": ".py", 984 | "mimetype": "text/x-python", 985 | "name": "python", 986 | "nbconvert_exporter": "python", 987 | "pygments_lexer": "ipython3", 988 | "version": "3.12.0" 989 | } 990 | }, 991 | "nbformat": 4, 992 | "nbformat_minor": 4 993 | } 994 | -------------------------------------------------------------------------------- /Logistic_Regression/logistic_regression_insurance.ipynb: -------------------------------------------------------------------------------- 1 | { 2 | "cells": [ 3 | { 4 | "cell_type": "markdown", 5 | "metadata": {}, 6 | "source": [ 7 | "

Predicting if a person would buy life insurnace based on his age using logistic regression

" 8 | ] 9 | }, 10 | { 11 | "cell_type": "markdown", 12 | "metadata": {}, 13 | "source": [ 14 | "Above is a binary logistic regression problem as there are only two possible outcomes (i.e. if person buys insurance or he/she doesn't). " 15 | ] 16 | }, 17 | { 18 | "cell_type": "code", 19 | "execution_count": 1, 20 | "metadata": {}, 21 | "outputs": [], 22 | "source": [ 23 | "import pandas as pd\n", 24 | "from matplotlib import pyplot as plt" 25 | ] 26 | }, 27 | { 28 | "cell_type": "code", 29 | "execution_count": 2, 30 | "metadata": {}, 31 | "outputs": [ 32 | { 33 | "data": { 34 | "text/html": [ 35 | "
\n", 36 | "\n", 49 | "\n", 50 | " \n", 51 | " \n", 52 | " \n", 53 | " \n", 54 | " \n", 55 | " \n", 56 | " \n", 57 | " \n", 58 | " \n", 59 | " \n", 60 | " \n", 61 | " \n", 62 | " \n", 63 | " \n", 64 | " \n", 65 | " \n", 66 | " \n", 67 | " \n", 68 | " \n", 69 | " \n", 70 | " \n", 71 | " \n", 72 | " \n", 73 | " \n", 74 | " \n", 75 | " \n", 76 | " \n", 77 | " \n", 78 | " \n", 79 | " \n", 80 | " \n", 81 | " \n", 82 | " \n", 83 | " \n", 84 | "
agebought_insurance
0220
1250
2471
3520
4461
\n", 85 | "
" 86 | ], 87 | "text/plain": [ 88 | " age bought_insurance\n", 89 | "0 22 0\n", 90 | "1 25 0\n", 91 | "2 47 1\n", 92 | "3 52 0\n", 93 | "4 46 1" 94 | ] 95 | }, 96 | "execution_count": 2, 97 | "metadata": {}, 98 | "output_type": "execute_result" 99 | } 100 | ], 101 | "source": [ 102 | "df = pd.read_csv(\"data/insurance_data.csv\")\n", 103 | "df.head()" 104 | ] 105 | }, 106 | { 107 | "cell_type": "code", 108 | "execution_count": 3, 109 | "metadata": {}, 110 | "outputs": [ 111 | { 112 | "name": "stdout", 113 | "output_type": "stream", 114 | "text": [ 115 | "\n", 116 | "RangeIndex: 27 entries, 0 to 26\n", 117 | "Data columns (total 2 columns):\n", 118 | " # Column Non-Null Count Dtype\n", 119 | "--- ------ -------------- -----\n", 120 | " 0 age 27 non-null int64\n", 121 | " 1 bought_insurance 27 non-null int64\n", 122 | "dtypes: int64(2)\n", 123 | "memory usage: 564.0 bytes\n" 124 | ] 125 | } 126 | ], 127 | "source": [ 128 | "df.info()" 129 | ] 130 | }, 131 | { 132 | "cell_type": "code", 133 | "execution_count": 4, 134 | "metadata": {}, 135 | "outputs": [ 136 | { 137 | "data": { 138 | "text/plain": [ 139 | "" 140 | ] 141 | }, 142 | "execution_count": 4, 143 | "metadata": {}, 144 | "output_type": "execute_result" 145 | }, 146 | { 147 | "data": { 148 | "image/png": "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", 149 | "text/plain": [ 150 | "
" 151 | ] 152 | }, 153 | "metadata": {}, 154 | "output_type": "display_data" 155 | } 156 | ], 157 | "source": [ 158 | "plt.scatter(df.age,df.bought_insurance,marker='+',color='red')" 159 | ] 160 | }, 161 | { 162 | "cell_type": "code", 163 | "execution_count": 8, 164 | "metadata": {}, 165 | "outputs": [ 166 | { 167 | "data": { 168 | "text/plain": [ 169 | "(27, 2)" 170 | ] 171 | }, 172 | "execution_count": 8, 173 | "metadata": {}, 174 | "output_type": "execute_result" 175 | } 176 | ], 177 | "source": [ 178 | "df.shape" 179 | ] 180 | }, 181 | { 182 | "cell_type": "code", 183 | "execution_count": 5, 184 | "metadata": {}, 185 | "outputs": [], 186 | "source": [ 187 | "from sklearn.model_selection import train_test_split" 188 | ] 189 | }, 190 | { 191 | "cell_type": "code", 192 | "execution_count": 11, 193 | "metadata": {}, 194 | "outputs": [], 195 | "source": [ 196 | "X_train, X_test, y_train, y_test = train_test_split(df[['age']],df.bought_insurance,train_size=0.8)" 197 | ] 198 | }, 199 | { 200 | "cell_type": "code", 201 | "execution_count": 12, 202 | "metadata": {}, 203 | "outputs": [ 204 | { 205 | "data": { 206 | "text/html": [ 207 | "
\n", 208 | "\n", 221 | "\n", 222 | " \n", 223 | " \n", 224 | " \n", 225 | " \n", 226 | " \n", 227 | " \n", 228 | " \n", 229 | " \n", 230 | " \n", 231 | " \n", 232 | " \n", 233 | " \n", 234 | " \n", 235 | " \n", 236 | " \n", 237 | " \n", 238 | " \n", 239 | " \n", 240 | " \n", 241 | " \n", 242 | " \n", 243 | " \n", 244 | " \n", 245 | " \n", 246 | " \n", 247 | " \n", 248 | " \n", 249 | " \n", 250 | " \n", 251 | " \n", 252 | " \n", 253 | " \n", 254 | "
age
1329
961
446
247
1758
2126
\n", 255 | "
" 256 | ], 257 | "text/plain": [ 258 | " age\n", 259 | "13 29\n", 260 | "9 61\n", 261 | "4 46\n", 262 | "2 47\n", 263 | "17 58\n", 264 | "21 26" 265 | ] 266 | }, 267 | "execution_count": 12, 268 | "metadata": {}, 269 | "output_type": "execute_result" 270 | } 271 | ], 272 | "source": [ 273 | "X_test" 274 | ] 275 | }, 276 | { 277 | "cell_type": "code", 278 | "execution_count": 13, 279 | "metadata": {}, 280 | "outputs": [], 281 | "source": [ 282 | "from sklearn.linear_model import LogisticRegression\n", 283 | "model = LogisticRegression()" 284 | ] 285 | }, 286 | { 287 | "cell_type": "code", 288 | "execution_count": 19, 289 | "metadata": { 290 | "scrolled": true 291 | }, 292 | "outputs": [ 293 | { 294 | "data": { 295 | "text/html": [ 296 | "
LogisticRegression()
In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook.
On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.
" 701 | ], 702 | "text/plain": [ 703 | "LogisticRegression()" 704 | ] 705 | }, 706 | "execution_count": 19, 707 | "metadata": {}, 708 | "output_type": "execute_result" 709 | } 710 | ], 711 | "source": [ 712 | "model.fit(X_train, y_train)" 713 | ] 714 | }, 715 | { 716 | "cell_type": "code", 717 | "execution_count": 20, 718 | "metadata": {}, 719 | "outputs": [ 720 | { 721 | "data": { 722 | "text/html": [ 723 | "
\n", 724 | "\n", 737 | "\n", 738 | " \n", 739 | " \n", 740 | " \n", 741 | " \n", 742 | " \n", 743 | " \n", 744 | " \n", 745 | " \n", 746 | " \n", 747 | " \n", 748 | " \n", 749 | " \n", 750 | " \n", 751 | " \n", 752 | " \n", 753 | " \n", 754 | " \n", 755 | " \n", 756 | " \n", 757 | " \n", 758 | " \n", 759 | " \n", 760 | " \n", 761 | " \n", 762 | " \n", 763 | " \n", 764 | " \n", 765 | " \n", 766 | " \n", 767 | " \n", 768 | " \n", 769 | " \n", 770 | "
age
1329
961
446
247
1758
2126
\n", 771 | "
" 772 | ], 773 | "text/plain": [ 774 | " age\n", 775 | "13 29\n", 776 | "9 61\n", 777 | "4 46\n", 778 | "2 47\n", 779 | "17 58\n", 780 | "21 26" 781 | ] 782 | }, 783 | "execution_count": 20, 784 | "metadata": {}, 785 | "output_type": "execute_result" 786 | } 787 | ], 788 | "source": [ 789 | "X_test" 790 | ] 791 | }, 792 | { 793 | "cell_type": "code", 794 | "execution_count": 21, 795 | "metadata": {}, 796 | "outputs": [ 797 | { 798 | "data": { 799 | "text/plain": [ 800 | "13 0\n", 801 | "9 1\n", 802 | "4 1\n", 803 | "2 1\n", 804 | "17 1\n", 805 | "21 0\n", 806 | "Name: bought_insurance, dtype: int64" 807 | ] 808 | }, 809 | "execution_count": 21, 810 | "metadata": {}, 811 | "output_type": "execute_result" 812 | } 813 | ], 814 | "source": [ 815 | "y_test" 816 | ] 817 | }, 818 | { 819 | "cell_type": "code", 820 | "execution_count": 22, 821 | "metadata": {}, 822 | "outputs": [ 823 | { 824 | "data": { 825 | "text/plain": [ 826 | "array([0, 1, 1, 1, 1, 0], dtype=int64)" 827 | ] 828 | }, 829 | "execution_count": 22, 830 | "metadata": {}, 831 | "output_type": "execute_result" 832 | } 833 | ], 834 | "source": [ 835 | "y_predicted = model.predict(X_test)\n", 836 | "y_predicted" 837 | ] 838 | }, 839 | { 840 | "cell_type": "code", 841 | "execution_count": 23, 842 | "metadata": {}, 843 | "outputs": [ 844 | { 845 | "data": { 846 | "text/plain": [ 847 | "array([[0.77387262, 0.22612738],\n", 848 | " [0.08061541, 0.91938459],\n", 849 | " [0.32819437, 0.67180563],\n", 850 | " [0.30346048, 0.69653952],\n", 851 | " [0.11002514, 0.88997486],\n", 852 | " [0.82833022, 0.17166978]])" 853 | ] 854 | }, 855 | "execution_count": 23, 856 | "metadata": {}, 857 | "output_type": "execute_result" 858 | } 859 | ], 860 | "source": [ 861 | "model.predict_proba(X_test)" 862 | ] 863 | }, 864 | { 865 | "cell_type": "code", 866 | "execution_count": 24, 867 | "metadata": { 868 | "scrolled": true 869 | }, 870 | "outputs": [ 871 | { 872 | "data": { 873 | "text/plain": [ 874 | "1.0" 875 | ] 876 | }, 877 | "execution_count": 24, 878 | "metadata": {}, 879 | "output_type": "execute_result" 880 | } 881 | ], 882 | "source": [ 883 | "model.score(X_test,y_test)" 884 | ] 885 | }, 886 | { 887 | "cell_type": "code", 888 | "execution_count": 25, 889 | "metadata": {}, 890 | "outputs": [ 891 | { 892 | "data": { 893 | "text/plain": [ 894 | "array([0, 1, 1, 1, 1, 0], dtype=int64)" 895 | ] 896 | }, 897 | "execution_count": 25, 898 | "metadata": {}, 899 | "output_type": "execute_result" 900 | } 901 | ], 902 | "source": [ 903 | "y_predicted" 904 | ] 905 | }, 906 | { 907 | "cell_type": "code", 908 | "execution_count": 26, 909 | "metadata": { 910 | "scrolled": true 911 | }, 912 | "outputs": [ 913 | { 914 | "data": { 915 | "text/html": [ 916 | "
\n", 917 | "\n", 930 | "\n", 931 | " \n", 932 | " \n", 933 | " \n", 934 | " \n", 935 | " \n", 936 | " \n", 937 | " \n", 938 | " \n", 939 | " \n", 940 | " \n", 941 | " \n", 942 | " \n", 943 | " \n", 944 | " \n", 945 | " \n", 946 | " \n", 947 | " \n", 948 | " \n", 949 | " \n", 950 | " \n", 951 | " \n", 952 | " \n", 953 | " \n", 954 | " \n", 955 | " \n", 956 | " \n", 957 | " \n", 958 | " \n", 959 | " \n", 960 | " \n", 961 | " \n", 962 | " \n", 963 | "
age
1329
961
446
247
1758
2126
\n", 964 | "
" 965 | ], 966 | "text/plain": [ 967 | " age\n", 968 | "13 29\n", 969 | "9 61\n", 970 | "4 46\n", 971 | "2 47\n", 972 | "17 58\n", 973 | "21 26" 974 | ] 975 | }, 976 | "execution_count": 26, 977 | "metadata": {}, 978 | "output_type": "execute_result" 979 | } 980 | ], 981 | "source": [ 982 | "X_test" 983 | ] 984 | }, 985 | { 986 | "cell_type": "markdown", 987 | "metadata": {}, 988 | "source": [ 989 | "**model.coef_ indicates value of m in y=m*x + b equation**" 990 | ] 991 | }, 992 | { 993 | "cell_type": "code", 994 | "execution_count": 27, 995 | "metadata": { 996 | "scrolled": true 997 | }, 998 | "outputs": [ 999 | { 1000 | "data": { 1001 | "text/plain": [ 1002 | "array([[0.11451011]])" 1003 | ] 1004 | }, 1005 | "execution_count": 27, 1006 | "metadata": {}, 1007 | "output_type": "execute_result" 1008 | } 1009 | ], 1010 | "source": [ 1011 | "model.coef_" 1012 | ] 1013 | }, 1014 | { 1015 | "cell_type": "markdown", 1016 | "metadata": {}, 1017 | "source": [ 1018 | "**model.intercept_ indicates value of b in y=m*x + b equation**" 1019 | ] 1020 | }, 1021 | { 1022 | "cell_type": "code", 1023 | "execution_count": 28, 1024 | "metadata": { 1025 | "scrolled": true 1026 | }, 1027 | "outputs": [ 1028 | { 1029 | "data": { 1030 | "text/plain": [ 1031 | "array([-4.55110194])" 1032 | ] 1033 | }, 1034 | "execution_count": 28, 1035 | "metadata": {}, 1036 | "output_type": "execute_result" 1037 | } 1038 | ], 1039 | "source": [ 1040 | "model.intercept_" 1041 | ] 1042 | }, 1043 | { 1044 | "cell_type": "markdown", 1045 | "metadata": {}, 1046 | "source": [ 1047 | "**Lets defined sigmoid function now and do the math with hand**" 1048 | ] 1049 | }, 1050 | { 1051 | "cell_type": "code", 1052 | "execution_count": 29, 1053 | "metadata": {}, 1054 | "outputs": [], 1055 | "source": [ 1056 | "import math\n", 1057 | "def sigmoid(x):\n", 1058 | " return 1 / (1 + math.exp(-x))" 1059 | ] 1060 | }, 1061 | { 1062 | "cell_type": "code", 1063 | "execution_count": 30, 1064 | "metadata": {}, 1065 | "outputs": [], 1066 | "source": [ 1067 | "def prediction_function(age):\n", 1068 | " z = 0.042 * age - 1.53 # 0.04150133 ~ 0.042 and -1.52726963 ~ -1.53\n", 1069 | " y = sigmoid(z)\n", 1070 | " return y" 1071 | ] 1072 | }, 1073 | { 1074 | "cell_type": "code", 1075 | "execution_count": 31, 1076 | "metadata": {}, 1077 | "outputs": [ 1078 | { 1079 | "data": { 1080 | "text/plain": [ 1081 | "0.4850044983805899" 1082 | ] 1083 | }, 1084 | "execution_count": 31, 1085 | "metadata": {}, 1086 | "output_type": "execute_result" 1087 | } 1088 | ], 1089 | "source": [ 1090 | "age = 35\n", 1091 | "prediction_function(age)" 1092 | ] 1093 | }, 1094 | { 1095 | "cell_type": "markdown", 1096 | "metadata": {}, 1097 | "source": [ 1098 | "**0.485 is less than 0.5 which means person with 35 age will *not* buy insurance**" 1099 | ] 1100 | }, 1101 | { 1102 | "cell_type": "code", 1103 | "execution_count": 32, 1104 | "metadata": { 1105 | "scrolled": true 1106 | }, 1107 | "outputs": [ 1108 | { 1109 | "data": { 1110 | "text/plain": [ 1111 | "0.568565299077705" 1112 | ] 1113 | }, 1114 | "execution_count": 32, 1115 | "metadata": {}, 1116 | "output_type": "execute_result" 1117 | } 1118 | ], 1119 | "source": [ 1120 | "age = 43\n", 1121 | "prediction_function(age)" 1122 | ] 1123 | }, 1124 | { 1125 | "cell_type": "markdown", 1126 | "metadata": {}, 1127 | "source": [ 1128 | "**0.485 is more than 0.5 which means person with 43 will buy the insurance**" 1129 | ] 1130 | } 1131 | ], 1132 | "metadata": { 1133 | "kernelspec": { 1134 | "display_name": "Python 3", 1135 | "language": "python", 1136 | "name": "python3" 1137 | }, 1138 | "language_info": { 1139 | "codemirror_mode": { 1140 | "name": "ipython", 1141 | "version": 3 1142 | }, 1143 | "file_extension": ".py", 1144 | "mimetype": "text/x-python", 1145 | "name": "python", 1146 | "nbconvert_exporter": "python", 1147 | "pygments_lexer": "ipython3", 1148 | "version": "3.12.0" 1149 | } 1150 | }, 1151 | "nbformat": 4, 1152 | "nbformat_minor": 2 1153 | } 1154 | --------------------------------------------------------------------------------