Initialising Repository
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608
DataScienceJobSalariesModel/ds_salaries.csv
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DataScienceJobSalariesModel/ds_salaries.csv
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,work_year,experience_level,employment_type,job_title,salary,salary_currency,salary_in_usd,employee_residence,remote_ratio,company_location,company_size
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0,2020,MI,FT,Data Scientist,70000,EUR,79833,DE,0,DE,L
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1,2020,SE,FT,Machine Learning Scientist,260000,USD,260000,JP,0,JP,S
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2,2020,SE,FT,Big Data Engineer,85000,GBP,109024,GB,50,GB,M
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3,2020,MI,FT,Product Data Analyst,20000,USD,20000,HN,0,HN,S
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4,2020,SE,FT,Machine Learning Engineer,150000,USD,150000,US,50,US,L
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5,2020,EN,FT,Data Analyst,72000,USD,72000,US,100,US,L
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6,2020,SE,FT,Lead Data Scientist,190000,USD,190000,US,100,US,S
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7,2020,MI,FT,Data Scientist,11000000,HUF,35735,HU,50,HU,L
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8,2020,MI,FT,Business Data Analyst,135000,USD,135000,US,100,US,L
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9,2020,SE,FT,Lead Data Engineer,125000,USD,125000,NZ,50,NZ,S
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10,2020,EN,FT,Data Scientist,45000,EUR,51321,FR,0,FR,S
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11,2020,MI,FT,Data Scientist,3000000,INR,40481,IN,0,IN,L
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12,2020,EN,FT,Data Scientist,35000,EUR,39916,FR,0,FR,M
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13,2020,MI,FT,Lead Data Analyst,87000,USD,87000,US,100,US,L
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14,2020,MI,FT,Data Analyst,85000,USD,85000,US,100,US,L
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15,2020,MI,FT,Data Analyst,8000,USD,8000,PK,50,PK,L
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16,2020,EN,FT,Data Engineer,4450000,JPY,41689,JP,100,JP,S
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17,2020,SE,FT,Big Data Engineer,100000,EUR,114047,PL,100,GB,S
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18,2020,EN,FT,Data Science Consultant,423000,INR,5707,IN,50,IN,M
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19,2020,MI,FT,Lead Data Engineer,56000,USD,56000,PT,100,US,M
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20,2020,MI,FT,Machine Learning Engineer,299000,CNY,43331,CN,0,CN,M
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21,2020,MI,FT,Product Data Analyst,450000,INR,6072,IN,100,IN,L
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22,2020,SE,FT,Data Engineer,42000,EUR,47899,GR,50,GR,L
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23,2020,MI,FT,BI Data Analyst,98000,USD,98000,US,0,US,M
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24,2020,MI,FT,Lead Data Scientist,115000,USD,115000,AE,0,AE,L
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25,2020,EX,FT,Director of Data Science,325000,USD,325000,US,100,US,L
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26,2020,EN,FT,Research Scientist,42000,USD,42000,NL,50,NL,L
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27,2020,SE,FT,Data Engineer,720000,MXN,33511,MX,0,MX,S
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28,2020,EN,CT,Business Data Analyst,100000,USD,100000,US,100,US,L
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29,2020,SE,FT,Machine Learning Manager,157000,CAD,117104,CA,50,CA,L
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30,2020,MI,FT,Data Engineering Manager,51999,EUR,59303,DE,100,DE,S
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31,2020,EN,FT,Big Data Engineer,70000,USD,70000,US,100,US,L
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32,2020,SE,FT,Data Scientist,60000,EUR,68428,GR,100,US,L
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33,2020,MI,FT,Research Scientist,450000,USD,450000,US,0,US,M
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34,2020,MI,FT,Data Analyst,41000,EUR,46759,FR,50,FR,L
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35,2020,MI,FT,Data Engineer,65000,EUR,74130,AT,50,AT,L
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36,2020,MI,FT,Data Science Consultant,103000,USD,103000,US,100,US,L
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37,2020,EN,FT,Machine Learning Engineer,250000,USD,250000,US,50,US,L
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38,2020,EN,FT,Data Analyst,10000,USD,10000,NG,100,NG,S
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39,2020,EN,FT,Machine Learning Engineer,138000,USD,138000,US,100,US,S
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40,2020,MI,FT,Data Scientist,45760,USD,45760,PH,100,US,S
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41,2020,EX,FT,Data Engineering Manager,70000,EUR,79833,ES,50,ES,L
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42,2020,MI,FT,Machine Learning Infrastructure Engineer,44000,EUR,50180,PT,0,PT,M
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43,2020,MI,FT,Data Engineer,106000,USD,106000,US,100,US,L
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44,2020,MI,FT,Data Engineer,88000,GBP,112872,GB,50,GB,L
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45,2020,EN,PT,ML Engineer,14000,EUR,15966,DE,100,DE,S
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46,2020,MI,FT,Data Scientist,60000,GBP,76958,GB,100,GB,S
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47,2020,SE,FT,Data Engineer,188000,USD,188000,US,100,US,L
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48,2020,MI,FT,Data Scientist,105000,USD,105000,US,100,US,L
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49,2020,MI,FT,Data Engineer,61500,EUR,70139,FR,50,FR,L
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50,2020,EN,FT,Data Analyst,450000,INR,6072,IN,0,IN,S
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51,2020,EN,FT,Data Analyst,91000,USD,91000,US,100,US,L
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52,2020,EN,FT,AI Scientist,300000,DKK,45896,DK,50,DK,S
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53,2020,EN,FT,Data Engineer,48000,EUR,54742,PK,100,DE,L
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54,2020,SE,FL,Computer Vision Engineer,60000,USD,60000,RU,100,US,S
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55,2020,SE,FT,Principal Data Scientist,130000,EUR,148261,DE,100,DE,M
|
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56,2020,MI,FT,Data Scientist,34000,EUR,38776,ES,100,ES,M
|
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57,2020,MI,FT,Data Scientist,118000,USD,118000,US,100,US,M
|
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58,2020,SE,FT,Data Scientist,120000,USD,120000,US,50,US,L
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59,2020,MI,FT,Data Scientist,138350,USD,138350,US,100,US,M
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60,2020,MI,FT,Data Engineer,110000,USD,110000,US,100,US,L
|
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61,2020,MI,FT,Data Engineer,130800,USD,130800,ES,100,US,M
|
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62,2020,EN,PT,Data Scientist,19000,EUR,21669,IT,50,IT,S
|
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63,2020,SE,FT,Data Scientist,412000,USD,412000,US,100,US,L
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64,2020,SE,FT,Machine Learning Engineer,40000,EUR,45618,HR,100,HR,S
|
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65,2020,EN,FT,Data Scientist,55000,EUR,62726,DE,50,DE,S
|
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66,2020,EN,FT,Data Scientist,43200,EUR,49268,DE,0,DE,S
|
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67,2020,SE,FT,Data Science Manager,190200,USD,190200,US,100,US,M
|
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68,2020,EN,FT,Data Scientist,105000,USD,105000,US,100,US,S
|
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69,2020,SE,FT,Data Scientist,80000,EUR,91237,AT,0,AT,S
|
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70,2020,MI,FT,Data Scientist,55000,EUR,62726,FR,50,LU,S
|
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71,2020,MI,FT,Data Scientist,37000,EUR,42197,FR,50,FR,S
|
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72,2021,EN,FT,Research Scientist,60000,GBP,82528,GB,50,GB,L
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73,2021,EX,FT,BI Data Analyst,150000,USD,150000,IN,100,US,L
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74,2021,EX,FT,Head of Data,235000,USD,235000,US,100,US,L
|
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75,2021,SE,FT,Data Scientist,45000,EUR,53192,FR,50,FR,L
|
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76,2021,MI,FT,BI Data Analyst,100000,USD,100000,US,100,US,M
|
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77,2021,MI,PT,3D Computer Vision Researcher,400000,INR,5409,IN,50,IN,M
|
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78,2021,MI,CT,ML Engineer,270000,USD,270000,US,100,US,L
|
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79,2021,EN,FT,Data Analyst,80000,USD,80000,US,100,US,M
|
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80,2021,SE,FT,Data Analytics Engineer,67000,EUR,79197,DE,100,DE,L
|
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81,2021,MI,FT,Data Engineer,140000,USD,140000,US,100,US,L
|
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82,2021,MI,FT,Applied Data Scientist,68000,CAD,54238,GB,50,CA,L
|
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83,2021,MI,FT,Machine Learning Engineer,40000,EUR,47282,ES,100,ES,S
|
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84,2021,EX,FT,Director of Data Science,130000,EUR,153667,IT,100,PL,L
|
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85,2021,MI,FT,Data Engineer,110000,PLN,28476,PL,100,PL,L
|
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86,2021,EN,FT,Data Analyst,50000,EUR,59102,FR,50,FR,M
|
||||
87,2021,MI,FT,Data Analytics Engineer,110000,USD,110000,US,100,US,L
|
||||
88,2021,SE,FT,Lead Data Analyst,170000,USD,170000,US,100,US,L
|
||||
89,2021,SE,FT,Data Analyst,80000,USD,80000,BG,100,US,S
|
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90,2021,SE,FT,Marketing Data Analyst,75000,EUR,88654,GR,100,DK,L
|
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91,2021,EN,FT,Data Science Consultant,65000,EUR,76833,DE,100,DE,S
|
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92,2021,MI,FT,Lead Data Analyst,1450000,INR,19609,IN,100,IN,L
|
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93,2021,SE,FT,Lead Data Engineer,276000,USD,276000,US,0,US,L
|
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94,2021,EN,FT,Data Scientist,2200000,INR,29751,IN,50,IN,L
|
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95,2021,MI,FT,Cloud Data Engineer,120000,SGD,89294,SG,50,SG,L
|
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96,2021,EN,PT,AI Scientist,12000,USD,12000,BR,100,US,S
|
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97,2021,MI,FT,Financial Data Analyst,450000,USD,450000,US,100,US,L
|
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98,2021,EN,FT,Computer Vision Software Engineer,70000,USD,70000,US,100,US,M
|
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99,2021,MI,FT,Computer Vision Software Engineer,81000,EUR,95746,DE,100,US,S
|
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100,2021,MI,FT,Data Analyst,75000,USD,75000,US,0,US,L
|
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101,2021,SE,FT,Data Engineer,150000,USD,150000,US,100,US,L
|
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102,2021,MI,FT,BI Data Analyst,11000000,HUF,36259,HU,50,US,L
|
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103,2021,MI,FT,Data Analyst,62000,USD,62000,US,0,US,L
|
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104,2021,MI,FT,Data Scientist,73000,USD,73000,US,0,US,L
|
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105,2021,MI,FT,Data Analyst,37456,GBP,51519,GB,50,GB,L
|
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106,2021,MI,FT,Research Scientist,235000,CAD,187442,CA,100,CA,L
|
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107,2021,SE,FT,Data Engineer,115000,USD,115000,US,100,US,S
|
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108,2021,SE,FT,Data Engineer,150000,USD,150000,US,100,US,M
|
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109,2021,EN,FT,Data Engineer,2250000,INR,30428,IN,100,IN,L
|
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110,2021,SE,FT,Machine Learning Engineer,80000,EUR,94564,DE,50,DE,L
|
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111,2021,SE,FT,Director of Data Engineering,82500,GBP,113476,GB,100,GB,M
|
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112,2021,SE,FT,Lead Data Engineer,75000,GBP,103160,GB,100,GB,S
|
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113,2021,EN,PT,AI Scientist,12000,USD,12000,PK,100,US,M
|
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114,2021,MI,FT,Data Engineer,38400,EUR,45391,NL,100,NL,L
|
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115,2021,EN,FT,Machine Learning Scientist,225000,USD,225000,US,100,US,L
|
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116,2021,MI,FT,Data Scientist,50000,USD,50000,NG,100,NG,L
|
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117,2021,MI,FT,Data Science Engineer,34000,EUR,40189,GR,100,GR,M
|
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118,2021,EN,FT,Data Analyst,90000,USD,90000,US,100,US,S
|
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119,2021,MI,FT,Data Engineer,200000,USD,200000,US,100,US,L
|
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120,2021,MI,FT,Big Data Engineer,60000,USD,60000,ES,50,RO,M
|
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121,2021,SE,FT,Principal Data Engineer,200000,USD,200000,US,100,US,M
|
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122,2021,EN,FT,Data Analyst,50000,USD,50000,US,100,US,M
|
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123,2021,EN,FT,Applied Data Scientist,80000,GBP,110037,GB,0,GB,L
|
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124,2021,EN,PT,Data Analyst,8760,EUR,10354,ES,50,ES,M
|
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125,2021,MI,FT,Principal Data Scientist,151000,USD,151000,US,100,US,L
|
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126,2021,SE,FT,Machine Learning Scientist,120000,USD,120000,US,50,US,S
|
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127,2021,MI,FT,Data Scientist,700000,INR,9466,IN,0,IN,S
|
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128,2021,EN,FT,Machine Learning Engineer,20000,USD,20000,IN,100,IN,S
|
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129,2021,SE,FT,Lead Data Scientist,3000000,INR,40570,IN,50,IN,L
|
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130,2021,EN,FT,Machine Learning Developer,100000,USD,100000,IQ,50,IQ,S
|
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131,2021,EN,FT,Data Scientist,42000,EUR,49646,FR,50,FR,M
|
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132,2021,MI,FT,Applied Machine Learning Scientist,38400,USD,38400,VN,100,US,M
|
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133,2021,SE,FT,Computer Vision Engineer,24000,USD,24000,BR,100,BR,M
|
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134,2021,EN,FT,Data Scientist,100000,USD,100000,US,0,US,S
|
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135,2021,MI,FT,Data Analyst,90000,USD,90000,US,100,US,M
|
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136,2021,MI,FT,ML Engineer,7000000,JPY,63711,JP,50,JP,S
|
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137,2021,MI,FT,ML Engineer,8500000,JPY,77364,JP,50,JP,S
|
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138,2021,SE,FT,Principal Data Scientist,220000,USD,220000,US,0,US,L
|
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139,2021,EN,FT,Data Scientist,80000,USD,80000,US,100,US,M
|
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140,2021,MI,FT,Data Analyst,135000,USD,135000,US,100,US,L
|
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141,2021,SE,FT,Data Science Manager,240000,USD,240000,US,0,US,L
|
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142,2021,SE,FT,Data Engineering Manager,150000,USD,150000,US,0,US,L
|
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143,2021,MI,FT,Data Scientist,82500,USD,82500,US,100,US,S
|
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144,2021,MI,FT,Data Engineer,100000,USD,100000,US,100,US,L
|
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145,2021,SE,FT,Machine Learning Engineer,70000,EUR,82744,BE,50,BE,M
|
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146,2021,MI,FT,Research Scientist,53000,EUR,62649,FR,50,FR,M
|
||||
147,2021,MI,FT,Data Engineer,90000,USD,90000,US,100,US,L
|
||||
148,2021,SE,FT,Data Engineering Manager,153000,USD,153000,US,100,US,L
|
||||
149,2021,SE,FT,Cloud Data Engineer,160000,USD,160000,BR,100,US,S
|
||||
150,2021,SE,FT,Director of Data Science,168000,USD,168000,JP,0,JP,S
|
||||
151,2021,MI,FT,Data Scientist,150000,USD,150000,US,100,US,M
|
||||
152,2021,MI,FT,Data Scientist,95000,CAD,75774,CA,100,CA,L
|
||||
153,2021,EN,FT,Data Scientist,13400,USD,13400,UA,100,UA,L
|
||||
154,2021,SE,FT,Data Science Manager,144000,USD,144000,US,100,US,L
|
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155,2021,SE,FT,Data Science Engineer,159500,CAD,127221,CA,50,CA,L
|
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156,2021,MI,FT,Data Scientist,160000,SGD,119059,SG,100,IL,M
|
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157,2021,MI,FT,Applied Machine Learning Scientist,423000,USD,423000,US,50,US,L
|
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158,2021,SE,FT,Data Analytics Manager,120000,USD,120000,US,100,US,M
|
||||
159,2021,EN,FT,Machine Learning Engineer,125000,USD,125000,US,100,US,S
|
||||
160,2021,EX,FT,Head of Data,230000,USD,230000,RU,50,RU,L
|
||||
161,2021,EX,FT,Head of Data Science,85000,USD,85000,RU,0,RU,M
|
||||
162,2021,MI,FT,Data Engineer,24000,EUR,28369,MT,50,MT,L
|
||||
163,2021,EN,FT,Data Science Consultant,54000,EUR,63831,DE,50,DE,L
|
||||
164,2021,EX,FT,Director of Data Science,110000,EUR,130026,DE,50,DE,M
|
||||
165,2021,SE,FT,Data Specialist,165000,USD,165000,US,100,US,L
|
||||
166,2021,EN,FT,Data Engineer,80000,USD,80000,US,100,US,L
|
||||
167,2021,EX,FT,Director of Data Science,250000,USD,250000,US,0,US,L
|
||||
168,2021,EN,FT,BI Data Analyst,55000,USD,55000,US,50,US,S
|
||||
169,2021,MI,FT,Data Architect,150000,USD,150000,US,100,US,L
|
||||
170,2021,MI,FT,Data Architect,170000,USD,170000,US,100,US,L
|
||||
171,2021,MI,FT,Data Engineer,60000,GBP,82528,GB,100,GB,L
|
||||
172,2021,EN,FT,Data Analyst,60000,USD,60000,US,100,US,S
|
||||
173,2021,SE,FT,Principal Data Scientist,235000,USD,235000,US,100,US,L
|
||||
174,2021,SE,FT,Research Scientist,51400,EUR,60757,PT,50,PT,L
|
||||
175,2021,SE,FT,Data Engineering Manager,174000,USD,174000,US,100,US,L
|
||||
176,2021,MI,FT,Data Scientist,58000,MXN,2859,MX,0,MX,S
|
||||
177,2021,MI,FT,Data Scientist,30400000,CLP,40038,CL,100,CL,L
|
||||
178,2021,EN,FT,Machine Learning Engineer,81000,USD,81000,US,50,US,S
|
||||
179,2021,MI,FT,Data Scientist,420000,INR,5679,IN,100,US,S
|
||||
180,2021,MI,FT,Big Data Engineer,1672000,INR,22611,IN,0,IN,L
|
||||
181,2021,MI,FT,Data Scientist,76760,EUR,90734,DE,50,DE,L
|
||||
182,2021,MI,FT,Data Engineer,22000,EUR,26005,RO,0,US,L
|
||||
183,2021,SE,FT,Finance Data Analyst,45000,GBP,61896,GB,50,GB,L
|
||||
184,2021,MI,FL,Machine Learning Scientist,12000,USD,12000,PK,50,PK,M
|
||||
185,2021,MI,FT,Data Engineer,4000,USD,4000,IR,100,IR,M
|
||||
186,2021,SE,FT,Data Analytics Engineer,50000,USD,50000,VN,100,GB,M
|
||||
187,2021,EX,FT,Data Science Consultant,59000,EUR,69741,FR,100,ES,S
|
||||
188,2021,SE,FT,Data Engineer,65000,EUR,76833,RO,50,GB,S
|
||||
189,2021,MI,FT,Machine Learning Engineer,74000,USD,74000,JP,50,JP,S
|
||||
190,2021,SE,FT,Data Science Manager,152000,USD,152000,US,100,FR,L
|
||||
191,2021,EN,FT,Machine Learning Engineer,21844,USD,21844,CO,50,CO,M
|
||||
192,2021,MI,FT,Big Data Engineer,18000,USD,18000,MD,0,MD,S
|
||||
193,2021,SE,FT,Data Science Manager,174000,USD,174000,US,100,US,L
|
||||
194,2021,SE,FT,Research Scientist,120500,CAD,96113,CA,50,CA,L
|
||||
195,2021,MI,FT,Data Scientist,147000,USD,147000,US,50,US,L
|
||||
196,2021,EN,FT,BI Data Analyst,9272,USD,9272,KE,100,KE,S
|
||||
197,2021,SE,FT,Machine Learning Engineer,1799997,INR,24342,IN,100,IN,L
|
||||
198,2021,SE,FT,Data Science Manager,4000000,INR,54094,IN,50,US,L
|
||||
199,2021,EN,FT,Data Science Consultant,90000,USD,90000,US,100,US,S
|
||||
200,2021,MI,FT,Data Scientist,52000,EUR,61467,DE,50,AT,M
|
||||
201,2021,SE,FT,Machine Learning Infrastructure Engineer,195000,USD,195000,US,100,US,M
|
||||
202,2021,MI,FT,Data Scientist,32000,EUR,37825,ES,100,ES,L
|
||||
203,2021,SE,FT,Research Scientist,50000,USD,50000,FR,100,US,S
|
||||
204,2021,MI,FT,Data Scientist,160000,USD,160000,US,100,US,L
|
||||
205,2021,MI,FT,Data Scientist,69600,BRL,12901,BR,0,BR,S
|
||||
206,2021,SE,FT,Machine Learning Engineer,200000,USD,200000,US,100,US,L
|
||||
207,2021,SE,FT,Data Engineer,165000,USD,165000,US,0,US,M
|
||||
208,2021,MI,FL,Data Engineer,20000,USD,20000,IT,0,US,L
|
||||
209,2021,SE,FT,Data Analytics Manager,120000,USD,120000,US,0,US,L
|
||||
210,2021,MI,FT,Machine Learning Engineer,21000,EUR,24823,SI,50,SI,L
|
||||
211,2021,MI,FT,Research Scientist,48000,EUR,56738,FR,50,FR,S
|
||||
212,2021,MI,FT,Data Engineer,48000,GBP,66022,HK,50,GB,S
|
||||
213,2021,EN,FT,Big Data Engineer,435000,INR,5882,IN,0,CH,L
|
||||
214,2021,EN,FT,Machine Learning Engineer,21000,EUR,24823,DE,50,DE,M
|
||||
215,2021,SE,FT,Principal Data Engineer,185000,USD,185000,US,100,US,L
|
||||
216,2021,EN,PT,Computer Vision Engineer,180000,DKK,28609,DK,50,DK,S
|
||||
217,2021,MI,FT,Data Scientist,76760,EUR,90734,DE,50,DE,L
|
||||
218,2021,MI,FT,Machine Learning Engineer,75000,EUR,88654,BE,100,BE,M
|
||||
219,2021,SE,FT,Data Analytics Manager,140000,USD,140000,US,100,US,L
|
||||
220,2021,MI,FT,Machine Learning Engineer,180000,PLN,46597,PL,100,PL,L
|
||||
221,2021,MI,FT,Data Scientist,85000,GBP,116914,GB,50,GB,L
|
||||
222,2021,MI,FT,Data Scientist,2500000,INR,33808,IN,0,IN,M
|
||||
223,2021,MI,FT,Data Scientist,40900,GBP,56256,GB,50,GB,L
|
||||
224,2021,SE,FT,Machine Learning Scientist,225000,USD,225000,US,100,CA,L
|
||||
225,2021,EX,CT,Principal Data Scientist,416000,USD,416000,US,100,US,S
|
||||
226,2021,SE,FT,Data Scientist,110000,CAD,87738,CA,100,CA,S
|
||||
227,2021,MI,FT,Data Scientist,75000,EUR,88654,DE,50,DE,L
|
||||
228,2021,SE,FT,Data Scientist,135000,USD,135000,US,0,US,L
|
||||
229,2021,SE,FT,Data Analyst,90000,CAD,71786,CA,100,CA,M
|
||||
230,2021,EN,FT,Big Data Engineer,1200000,INR,16228,IN,100,IN,L
|
||||
231,2021,SE,FT,ML Engineer,256000,USD,256000,US,100,US,S
|
||||
232,2021,SE,FT,Director of Data Engineering,200000,USD,200000,US,100,US,L
|
||||
233,2021,SE,FT,Data Analyst,200000,USD,200000,US,100,US,L
|
||||
234,2021,MI,FT,Data Architect,180000,USD,180000,US,100,US,L
|
||||
235,2021,MI,FT,Head of Data Science,110000,USD,110000,US,0,US,S
|
||||
236,2021,MI,FT,Research Scientist,80000,CAD,63810,CA,100,CA,M
|
||||
237,2021,MI,FT,Data Scientist,39600,EUR,46809,ES,100,ES,M
|
||||
238,2021,EN,FT,Data Scientist,4000,USD,4000,VN,0,VN,M
|
||||
239,2021,EN,FT,Data Engineer,1600000,INR,21637,IN,50,IN,M
|
||||
240,2021,SE,FT,Data Scientist,130000,CAD,103691,CA,100,CA,L
|
||||
241,2021,MI,FT,Data Analyst,80000,USD,80000,US,100,US,L
|
||||
242,2021,MI,FT,Data Engineer,110000,USD,110000,US,100,US,L
|
||||
243,2021,SE,FT,Data Scientist,165000,USD,165000,US,100,US,L
|
||||
244,2021,EN,FT,AI Scientist,1335000,INR,18053,IN,100,AS,S
|
||||
245,2021,MI,FT,Data Engineer,52500,GBP,72212,GB,50,GB,L
|
||||
246,2021,EN,FT,Data Scientist,31000,EUR,36643,FR,50,FR,L
|
||||
247,2021,MI,FT,Data Engineer,108000,TRY,12103,TR,0,TR,M
|
||||
248,2021,SE,FT,Data Engineer,70000,GBP,96282,GB,50,GB,L
|
||||
249,2021,SE,FT,Principal Data Analyst,170000,USD,170000,US,100,US,M
|
||||
250,2021,MI,FT,Data Scientist,115000,USD,115000,US,50,US,L
|
||||
251,2021,EN,FT,Data Scientist,90000,USD,90000,US,100,US,S
|
||||
252,2021,EX,FT,Principal Data Engineer,600000,USD,600000,US,100,US,L
|
||||
253,2021,EN,FT,Data Scientist,2100000,INR,28399,IN,100,IN,M
|
||||
254,2021,MI,FT,Data Analyst,93000,USD,93000,US,100,US,L
|
||||
255,2021,SE,FT,Big Data Architect,125000,CAD,99703,CA,50,CA,M
|
||||
256,2021,MI,FT,Data Engineer,200000,USD,200000,US,100,US,L
|
||||
257,2021,SE,FT,Principal Data Scientist,147000,EUR,173762,DE,100,DE,M
|
||||
258,2021,SE,FT,Machine Learning Engineer,185000,USD,185000,US,50,US,L
|
||||
259,2021,EX,FT,Director of Data Science,120000,EUR,141846,DE,0,DE,L
|
||||
260,2021,MI,FT,Data Scientist,130000,USD,130000,US,50,US,L
|
||||
261,2021,SE,FT,Data Analyst,54000,EUR,63831,DE,50,DE,L
|
||||
262,2021,MI,FT,Data Scientist,1250000,INR,16904,IN,100,IN,S
|
||||
263,2021,SE,FT,Machine Learning Engineer,4900000,INR,66265,IN,0,IN,L
|
||||
264,2021,MI,FT,Data Scientist,21600,EUR,25532,RS,100,DE,S
|
||||
265,2021,SE,FT,Lead Data Engineer,160000,USD,160000,PR,50,US,S
|
||||
266,2021,MI,FT,Data Engineer,93150,USD,93150,US,0,US,M
|
||||
267,2021,MI,FT,Data Engineer,111775,USD,111775,US,0,US,M
|
||||
268,2021,MI,FT,Data Engineer,250000,TRY,28016,TR,100,TR,M
|
||||
269,2021,EN,FT,Data Engineer,55000,EUR,65013,DE,50,DE,M
|
||||
270,2021,EN,FT,Data Engineer,72500,USD,72500,US,100,US,L
|
||||
271,2021,SE,FT,Computer Vision Engineer,102000,BRL,18907,BR,0,BR,M
|
||||
272,2021,EN,FT,Data Science Consultant,65000,EUR,76833,DE,0,DE,L
|
||||
273,2021,EN,FT,Machine Learning Engineer,85000,USD,85000,NL,100,DE,S
|
||||
274,2021,SE,FT,Data Scientist,65720,EUR,77684,FR,50,FR,M
|
||||
275,2021,EN,FT,Data Scientist,100000,USD,100000,US,100,US,M
|
||||
276,2021,EN,FT,Data Scientist,58000,USD,58000,US,50,US,L
|
||||
277,2021,SE,FT,AI Scientist,55000,USD,55000,ES,100,ES,L
|
||||
278,2021,SE,FT,Data Scientist,180000,TRY,20171,TR,50,TR,L
|
||||
279,2021,EN,FT,Business Data Analyst,50000,EUR,59102,LU,100,LU,L
|
||||
280,2021,MI,FT,Data Engineer,112000,USD,112000,US,100,US,L
|
||||
281,2021,EN,FT,Research Scientist,100000,USD,100000,JE,0,CN,L
|
||||
282,2021,MI,PT,Data Engineer,59000,EUR,69741,NL,100,NL,L
|
||||
283,2021,SE,CT,Staff Data Scientist,105000,USD,105000,US,100,US,M
|
||||
284,2021,MI,FT,Research Scientist,69999,USD,69999,CZ,50,CZ,L
|
||||
285,2021,SE,FT,Data Science Manager,7000000,INR,94665,IN,50,IN,L
|
||||
286,2021,SE,FT,Head of Data,87000,EUR,102839,SI,100,SI,L
|
||||
287,2021,MI,FT,Data Scientist,109000,USD,109000,US,50,US,L
|
||||
288,2021,MI,FT,Machine Learning Engineer,43200,EUR,51064,IT,50,IT,L
|
||||
289,2022,SE,FT,Data Engineer,135000,USD,135000,US,100,US,M
|
||||
290,2022,SE,FT,Data Analyst,155000,USD,155000,US,100,US,M
|
||||
291,2022,SE,FT,Data Analyst,120600,USD,120600,US,100,US,M
|
||||
292,2022,MI,FT,Data Scientist,130000,USD,130000,US,0,US,M
|
||||
293,2022,MI,FT,Data Scientist,90000,USD,90000,US,0,US,M
|
||||
294,2022,MI,FT,Data Engineer,170000,USD,170000,US,100,US,M
|
||||
295,2022,MI,FT,Data Engineer,150000,USD,150000,US,100,US,M
|
||||
296,2022,SE,FT,Data Analyst,102100,USD,102100,US,100,US,M
|
||||
297,2022,SE,FT,Data Analyst,84900,USD,84900,US,100,US,M
|
||||
298,2022,SE,FT,Data Scientist,136620,USD,136620,US,100,US,M
|
||||
299,2022,SE,FT,Data Scientist,99360,USD,99360,US,100,US,M
|
||||
300,2022,SE,FT,Data Scientist,90000,GBP,117789,GB,0,GB,M
|
||||
301,2022,SE,FT,Data Scientist,80000,GBP,104702,GB,0,GB,M
|
||||
302,2022,SE,FT,Data Scientist,146000,USD,146000,US,100,US,M
|
||||
303,2022,SE,FT,Data Scientist,123000,USD,123000,US,100,US,M
|
||||
304,2022,EN,FT,Data Engineer,40000,GBP,52351,GB,100,GB,M
|
||||
305,2022,SE,FT,Data Analyst,99000,USD,99000,US,0,US,M
|
||||
306,2022,SE,FT,Data Analyst,116000,USD,116000,US,0,US,M
|
||||
307,2022,MI,FT,Data Analyst,106260,USD,106260,US,0,US,M
|
||||
308,2022,MI,FT,Data Analyst,126500,USD,126500,US,0,US,M
|
||||
309,2022,EX,FT,Data Engineer,242000,USD,242000,US,100,US,M
|
||||
310,2022,EX,FT,Data Engineer,200000,USD,200000,US,100,US,M
|
||||
311,2022,MI,FT,Data Scientist,50000,GBP,65438,GB,0,GB,M
|
||||
312,2022,MI,FT,Data Scientist,30000,GBP,39263,GB,0,GB,M
|
||||
313,2022,MI,FT,Data Engineer,60000,GBP,78526,GB,0,GB,M
|
||||
314,2022,MI,FT,Data Engineer,40000,GBP,52351,GB,0,GB,M
|
||||
315,2022,SE,FT,Data Scientist,165220,USD,165220,US,100,US,M
|
||||
316,2022,EN,FT,Data Engineer,35000,GBP,45807,GB,100,GB,M
|
||||
317,2022,SE,FT,Data Scientist,120160,USD,120160,US,100,US,M
|
||||
318,2022,SE,FT,Data Analyst,90320,USD,90320,US,100,US,M
|
||||
319,2022,SE,FT,Data Engineer,181940,USD,181940,US,0,US,M
|
||||
320,2022,SE,FT,Data Engineer,132320,USD,132320,US,0,US,M
|
||||
321,2022,SE,FT,Data Engineer,220110,USD,220110,US,0,US,M
|
||||
322,2022,SE,FT,Data Engineer,160080,USD,160080,US,0,US,M
|
||||
323,2022,SE,FT,Data Scientist,180000,USD,180000,US,0,US,L
|
||||
324,2022,SE,FT,Data Scientist,120000,USD,120000,US,0,US,L
|
||||
325,2022,SE,FT,Data Analyst,124190,USD,124190,US,100,US,M
|
||||
326,2022,EX,FT,Data Analyst,130000,USD,130000,US,100,US,M
|
||||
327,2022,EX,FT,Data Analyst,110000,USD,110000,US,100,US,M
|
||||
328,2022,SE,FT,Data Analyst,170000,USD,170000,US,100,US,M
|
||||
329,2022,MI,FT,Data Analyst,115500,USD,115500,US,100,US,M
|
||||
330,2022,SE,FT,Data Analyst,112900,USD,112900,US,100,US,M
|
||||
331,2022,SE,FT,Data Analyst,90320,USD,90320,US,100,US,M
|
||||
332,2022,SE,FT,Data Analyst,112900,USD,112900,US,100,US,M
|
||||
333,2022,SE,FT,Data Analyst,90320,USD,90320,US,100,US,M
|
||||
334,2022,SE,FT,Data Engineer,165400,USD,165400,US,100,US,M
|
||||
335,2022,SE,FT,Data Engineer,132320,USD,132320,US,100,US,M
|
||||
336,2022,MI,FT,Data Analyst,167000,USD,167000,US,100,US,M
|
||||
337,2022,SE,FT,Data Engineer,243900,USD,243900,US,100,US,M
|
||||
338,2022,SE,FT,Data Analyst,136600,USD,136600,US,100,US,M
|
||||
339,2022,SE,FT,Data Analyst,109280,USD,109280,US,100,US,M
|
||||
340,2022,SE,FT,Data Engineer,128875,USD,128875,US,100,US,M
|
||||
341,2022,SE,FT,Data Engineer,93700,USD,93700,US,100,US,M
|
||||
342,2022,EX,FT,Head of Data Science,224000,USD,224000,US,100,US,M
|
||||
343,2022,EX,FT,Head of Data Science,167875,USD,167875,US,100,US,M
|
||||
344,2022,EX,FT,Analytics Engineer,175000,USD,175000,US,100,US,M
|
||||
345,2022,SE,FT,Data Engineer,156600,USD,156600,US,100,US,M
|
||||
346,2022,SE,FT,Data Engineer,108800,USD,108800,US,0,US,M
|
||||
347,2022,SE,FT,Data Scientist,95550,USD,95550,US,0,US,M
|
||||
348,2022,SE,FT,Data Engineer,113000,USD,113000,US,0,US,L
|
||||
349,2022,SE,FT,Data Analyst,135000,USD,135000,US,100,US,M
|
||||
350,2022,SE,FT,Data Science Manager,161342,USD,161342,US,100,US,M
|
||||
351,2022,SE,FT,Data Science Manager,137141,USD,137141,US,100,US,M
|
||||
352,2022,SE,FT,Data Scientist,167000,USD,167000,US,100,US,M
|
||||
353,2022,SE,FT,Data Scientist,123000,USD,123000,US,100,US,M
|
||||
354,2022,SE,FT,Data Engineer,60000,GBP,78526,GB,0,GB,M
|
||||
355,2022,SE,FT,Data Engineer,50000,GBP,65438,GB,0,GB,M
|
||||
356,2022,SE,FT,Data Scientist,150000,USD,150000,US,0,US,M
|
||||
357,2022,SE,FT,Data Scientist,211500,USD,211500,US,100,US,M
|
||||
358,2022,SE,FT,Data Architect,192400,USD,192400,CA,100,CA,M
|
||||
359,2022,SE,FT,Data Architect,90700,USD,90700,CA,100,CA,M
|
||||
360,2022,SE,FT,Data Analyst,130000,USD,130000,CA,100,CA,M
|
||||
361,2022,SE,FT,Data Analyst,61300,USD,61300,CA,100,CA,M
|
||||
362,2022,SE,FT,Data Analyst,130000,USD,130000,CA,100,CA,M
|
||||
363,2022,SE,FT,Data Analyst,61300,USD,61300,CA,100,CA,M
|
||||
364,2022,SE,FT,Data Engineer,160000,USD,160000,US,0,US,L
|
||||
365,2022,SE,FT,Data Scientist,138600,USD,138600,US,100,US,M
|
||||
366,2022,SE,FT,Data Engineer,136000,USD,136000,US,0,US,M
|
||||
367,2022,MI,FT,Data Analyst,58000,USD,58000,US,0,US,S
|
||||
368,2022,EX,FT,Analytics Engineer,135000,USD,135000,US,100,US,M
|
||||
369,2022,SE,FT,Data Scientist,170000,USD,170000,US,100,US,M
|
||||
370,2022,SE,FT,Data Scientist,123000,USD,123000,US,100,US,M
|
||||
371,2022,SE,FT,Machine Learning Engineer,189650,USD,189650,US,0,US,M
|
||||
372,2022,SE,FT,Machine Learning Engineer,164996,USD,164996,US,0,US,M
|
||||
373,2022,MI,FT,ETL Developer,50000,EUR,54957,GR,0,GR,M
|
||||
374,2022,MI,FT,ETL Developer,50000,EUR,54957,GR,0,GR,M
|
||||
375,2022,EX,FT,Lead Data Engineer,150000,CAD,118187,CA,100,CA,S
|
||||
376,2022,SE,FT,Data Analyst,132000,USD,132000,US,0,US,M
|
||||
377,2022,SE,FT,Data Engineer,165400,USD,165400,US,100,US,M
|
||||
378,2022,SE,FT,Data Architect,208775,USD,208775,US,100,US,M
|
||||
379,2022,SE,FT,Data Architect,147800,USD,147800,US,100,US,M
|
||||
380,2022,SE,FT,Data Engineer,136994,USD,136994,US,100,US,M
|
||||
381,2022,SE,FT,Data Engineer,101570,USD,101570,US,100,US,M
|
||||
382,2022,SE,FT,Data Analyst,128875,USD,128875,US,100,US,M
|
||||
383,2022,SE,FT,Data Analyst,93700,USD,93700,US,100,US,M
|
||||
384,2022,EX,FT,Head of Machine Learning,6000000,INR,79039,IN,50,IN,L
|
||||
385,2022,SE,FT,Data Engineer,132320,USD,132320,US,100,US,M
|
||||
386,2022,EN,FT,Machine Learning Engineer,28500,GBP,37300,GB,100,GB,L
|
||||
387,2022,SE,FT,Data Analyst,164000,USD,164000,US,0,US,M
|
||||
388,2022,SE,FT,Data Engineer,155000,USD,155000,US,100,US,M
|
||||
389,2022,MI,FT,Machine Learning Engineer,95000,GBP,124333,GB,0,GB,M
|
||||
390,2022,MI,FT,Machine Learning Engineer,75000,GBP,98158,GB,0,GB,M
|
||||
391,2022,MI,FT,AI Scientist,120000,USD,120000,US,0,US,M
|
||||
392,2022,SE,FT,Data Analyst,112900,USD,112900,US,100,US,M
|
||||
393,2022,SE,FT,Data Analyst,90320,USD,90320,US,100,US,M
|
||||
394,2022,SE,FT,Data Analytics Manager,145000,USD,145000,US,100,US,M
|
||||
395,2022,SE,FT,Data Analytics Manager,105400,USD,105400,US,100,US,M
|
||||
396,2022,MI,FT,Machine Learning Engineer,80000,EUR,87932,FR,100,DE,M
|
||||
397,2022,MI,FT,Data Engineer,90000,GBP,117789,GB,0,GB,M
|
||||
398,2022,SE,FT,Data Scientist,215300,USD,215300,US,100,US,L
|
||||
399,2022,SE,FT,Data Scientist,158200,USD,158200,US,100,US,L
|
||||
400,2022,SE,FT,Data Engineer,209100,USD,209100,US,100,US,L
|
||||
401,2022,SE,FT,Data Engineer,154600,USD,154600,US,100,US,L
|
||||
402,2022,SE,FT,Data Analyst,115934,USD,115934,US,0,US,M
|
||||
403,2022,SE,FT,Data Analyst,81666,USD,81666,US,0,US,M
|
||||
404,2022,SE,FT,Data Engineer,175000,USD,175000,US,100,US,M
|
||||
405,2022,MI,FT,Data Engineer,75000,GBP,98158,GB,0,GB,M
|
||||
406,2022,MI,FT,Data Analyst,58000,USD,58000,US,0,US,S
|
||||
407,2022,SE,FT,Data Engineer,183600,USD,183600,US,100,US,L
|
||||
408,2022,MI,FT,Data Analyst,40000,GBP,52351,GB,100,GB,M
|
||||
409,2022,SE,FT,Data Scientist,180000,USD,180000,US,100,US,M
|
||||
410,2022,MI,FT,Data Scientist,55000,GBP,71982,GB,0,GB,M
|
||||
411,2022,MI,FT,Data Scientist,35000,GBP,45807,GB,0,GB,M
|
||||
412,2022,MI,FT,Data Engineer,60000,EUR,65949,GR,100,GR,M
|
||||
413,2022,MI,FT,Data Engineer,45000,EUR,49461,GR,100,GR,M
|
||||
414,2022,MI,FT,Data Engineer,60000,GBP,78526,GB,100,GB,M
|
||||
415,2022,MI,FT,Data Engineer,45000,GBP,58894,GB,100,GB,M
|
||||
416,2022,SE,FT,Data Scientist,260000,USD,260000,US,100,US,M
|
||||
417,2022,SE,FT,Data Science Engineer,60000,USD,60000,AR,100,MX,L
|
||||
418,2022,MI,FT,Data Engineer,63900,USD,63900,US,0,US,M
|
||||
419,2022,MI,FT,Machine Learning Scientist,160000,USD,160000,US,100,US,L
|
||||
420,2022,MI,FT,Machine Learning Scientist,112300,USD,112300,US,100,US,L
|
||||
421,2022,MI,FT,Data Science Manager,241000,USD,241000,US,100,US,M
|
||||
422,2022,MI,FT,Data Science Manager,159000,USD,159000,US,100,US,M
|
||||
423,2022,SE,FT,Data Scientist,180000,USD,180000,US,0,US,M
|
||||
424,2022,SE,FT,Data Scientist,80000,USD,80000,US,0,US,M
|
||||
425,2022,MI,FT,Data Engineer,82900,USD,82900,US,0,US,M
|
||||
426,2022,SE,FT,Data Engineer,100800,USD,100800,US,100,US,L
|
||||
427,2022,MI,FT,Data Engineer,45000,EUR,49461,ES,100,ES,M
|
||||
428,2022,SE,FT,Data Scientist,140400,USD,140400,US,0,US,L
|
||||
429,2022,MI,FT,Data Analyst,30000,GBP,39263,GB,100,GB,M
|
||||
430,2022,MI,FT,Data Analyst,40000,EUR,43966,ES,100,ES,M
|
||||
431,2022,MI,FT,Data Analyst,30000,EUR,32974,ES,100,ES,M
|
||||
432,2022,MI,FT,Data Engineer,80000,EUR,87932,ES,100,ES,M
|
||||
433,2022,MI,FT,Data Engineer,70000,EUR,76940,ES,100,ES,M
|
||||
434,2022,MI,FT,Data Engineer,80000,GBP,104702,GB,100,GB,M
|
||||
435,2022,MI,FT,Data Engineer,70000,GBP,91614,GB,100,GB,M
|
||||
436,2022,MI,FT,Data Engineer,60000,EUR,65949,ES,100,ES,M
|
||||
437,2022,MI,FT,Data Engineer,80000,EUR,87932,GR,100,GR,M
|
||||
438,2022,SE,FT,Machine Learning Engineer,189650,USD,189650,US,0,US,M
|
||||
439,2022,SE,FT,Machine Learning Engineer,164996,USD,164996,US,0,US,M
|
||||
440,2022,MI,FT,Data Analyst,40000,EUR,43966,GR,100,GR,M
|
||||
441,2022,MI,FT,Data Analyst,30000,EUR,32974,GR,100,GR,M
|
||||
442,2022,MI,FT,Data Engineer,75000,GBP,98158,GB,100,GB,M
|
||||
443,2022,MI,FT,Data Engineer,60000,GBP,78526,GB,100,GB,M
|
||||
444,2022,SE,FT,Data Scientist,215300,USD,215300,US,0,US,L
|
||||
445,2022,MI,FT,Data Engineer,70000,EUR,76940,GR,100,GR,M
|
||||
446,2022,SE,FT,Data Engineer,209100,USD,209100,US,100,US,L
|
||||
447,2022,SE,FT,Data Engineer,154600,USD,154600,US,100,US,L
|
||||
448,2022,SE,FT,Data Engineer,180000,USD,180000,US,100,US,M
|
||||
449,2022,EN,FT,ML Engineer,20000,EUR,21983,PT,100,PT,L
|
||||
450,2022,SE,FT,Data Engineer,80000,USD,80000,US,100,US,M
|
||||
451,2022,MI,FT,Machine Learning Developer,100000,CAD,78791,CA,100,CA,M
|
||||
452,2022,EX,FT,Director of Data Science,250000,CAD,196979,CA,50,CA,L
|
||||
453,2022,MI,FT,Machine Learning Engineer,120000,USD,120000,US,100,US,S
|
||||
454,2022,EN,FT,Computer Vision Engineer,125000,USD,125000,US,0,US,M
|
||||
455,2022,MI,FT,NLP Engineer,240000,CNY,37236,US,50,US,L
|
||||
456,2022,SE,FT,Data Engineer,105000,USD,105000,US,100,US,M
|
||||
457,2022,SE,FT,Lead Machine Learning Engineer,80000,EUR,87932,DE,0,DE,M
|
||||
458,2022,MI,FT,Business Data Analyst,1400000,INR,18442,IN,100,IN,M
|
||||
459,2022,MI,FT,Data Scientist,2400000,INR,31615,IN,100,IN,L
|
||||
460,2022,MI,FT,Machine Learning Infrastructure Engineer,53000,EUR,58255,PT,50,PT,L
|
||||
461,2022,EN,FT,Financial Data Analyst,100000,USD,100000,US,50,US,L
|
||||
462,2022,MI,PT,Data Engineer,50000,EUR,54957,DE,50,DE,L
|
||||
463,2022,EN,FT,Data Scientist,1400000,INR,18442,IN,100,IN,M
|
||||
464,2022,SE,FT,Principal Data Scientist,148000,EUR,162674,DE,100,DE,M
|
||||
465,2022,EN,FT,Data Engineer,120000,USD,120000,US,100,US,M
|
||||
466,2022,SE,FT,Research Scientist,144000,USD,144000,US,50,US,L
|
||||
467,2022,SE,FT,Data Scientist,104890,USD,104890,US,100,US,M
|
||||
468,2022,SE,FT,Data Engineer,100000,USD,100000,US,100,US,M
|
||||
469,2022,SE,FT,Data Scientist,140000,USD,140000,US,100,US,M
|
||||
470,2022,MI,FT,Data Analyst,135000,USD,135000,US,100,US,M
|
||||
471,2022,MI,FT,Data Analyst,50000,USD,50000,US,100,US,M
|
||||
472,2022,SE,FT,Data Scientist,220000,USD,220000,US,100,US,M
|
||||
473,2022,SE,FT,Data Scientist,140000,USD,140000,US,100,US,M
|
||||
474,2022,MI,FT,Data Scientist,140000,GBP,183228,GB,0,GB,M
|
||||
475,2022,MI,FT,Data Scientist,70000,GBP,91614,GB,0,GB,M
|
||||
476,2022,SE,FT,Data Scientist,185100,USD,185100,US,100,US,M
|
||||
477,2022,SE,FT,Machine Learning Engineer,220000,USD,220000,US,100,US,M
|
||||
478,2022,MI,FT,Data Scientist,200000,USD,200000,US,100,US,M
|
||||
479,2022,MI,FT,Data Scientist,120000,USD,120000,US,100,US,M
|
||||
480,2022,SE,FT,Machine Learning Engineer,120000,USD,120000,AE,100,AE,S
|
||||
481,2022,SE,FT,Machine Learning Engineer,65000,USD,65000,AE,100,AE,S
|
||||
482,2022,EX,FT,Data Engineer,324000,USD,324000,US,100,US,M
|
||||
483,2022,EX,FT,Data Engineer,216000,USD,216000,US,100,US,M
|
||||
484,2022,SE,FT,Data Engineer,210000,USD,210000,US,100,US,M
|
||||
485,2022,SE,FT,Machine Learning Engineer,120000,USD,120000,US,100,US,M
|
||||
486,2022,SE,FT,Data Scientist,230000,USD,230000,US,100,US,M
|
||||
487,2022,EN,PT,Data Scientist,100000,USD,100000,DZ,50,DZ,M
|
||||
488,2022,MI,FL,Data Scientist,100000,USD,100000,CA,100,US,M
|
||||
489,2022,EN,CT,Applied Machine Learning Scientist,29000,EUR,31875,TN,100,CZ,M
|
||||
490,2022,SE,FT,Head of Data,200000,USD,200000,MY,100,US,M
|
||||
491,2022,MI,FT,Principal Data Analyst,75000,USD,75000,CA,100,CA,S
|
||||
492,2022,MI,FT,Data Scientist,150000,PLN,35590,PL,100,PL,L
|
||||
493,2022,SE,FT,Machine Learning Developer,100000,CAD,78791,CA,100,CA,M
|
||||
494,2022,SE,FT,Data Scientist,100000,USD,100000,BR,100,US,M
|
||||
495,2022,MI,FT,Machine Learning Scientist,153000,USD,153000,US,50,US,M
|
||||
496,2022,EN,FT,Data Engineer,52800,EUR,58035,PK,100,DE,M
|
||||
497,2022,SE,FT,Data Scientist,165000,USD,165000,US,100,US,M
|
||||
498,2022,SE,FT,Research Scientist,85000,EUR,93427,FR,50,FR,L
|
||||
499,2022,EN,FT,Data Scientist,66500,CAD,52396,CA,100,CA,L
|
||||
500,2022,SE,FT,Machine Learning Engineer,57000,EUR,62651,NL,100,NL,L
|
||||
501,2022,MI,FT,Head of Data,30000,EUR,32974,EE,100,EE,S
|
||||
502,2022,EN,FT,Data Scientist,40000,USD,40000,JP,100,MY,L
|
||||
503,2022,MI,FT,Machine Learning Engineer,121000,AUD,87425,AU,100,AU,L
|
||||
504,2022,SE,FT,Data Engineer,115000,USD,115000,US,100,US,M
|
||||
505,2022,EN,FT,Data Scientist,120000,AUD,86703,AU,50,AU,M
|
||||
506,2022,MI,FT,Applied Machine Learning Scientist,75000,USD,75000,BO,100,US,L
|
||||
507,2022,MI,FT,Research Scientist,59000,EUR,64849,AT,0,AT,L
|
||||
508,2022,EN,FT,Research Scientist,120000,USD,120000,US,100,US,L
|
||||
509,2022,MI,FT,Applied Data Scientist,157000,USD,157000,US,100,US,L
|
||||
510,2022,EN,FT,Computer Vision Software Engineer,150000,USD,150000,AU,100,AU,S
|
||||
511,2022,MI,FT,Business Data Analyst,90000,CAD,70912,CA,50,CA,L
|
||||
512,2022,EN,FT,Data Engineer,65000,USD,65000,US,100,US,S
|
||||
513,2022,SE,FT,Machine Learning Engineer,65000,EUR,71444,IE,100,IE,S
|
||||
514,2022,EN,FT,Data Analytics Engineer,20000,USD,20000,PK,0,PK,M
|
||||
515,2022,MI,FT,Data Scientist,48000,USD,48000,RU,100,US,S
|
||||
516,2022,SE,FT,Data Science Manager,152500,USD,152500,US,100,US,M
|
||||
517,2022,MI,FT,Data Engineer,62000,EUR,68147,FR,100,FR,M
|
||||
518,2022,MI,FT,Data Scientist,115000,CHF,122346,CH,0,CH,L
|
||||
519,2022,SE,FT,Applied Data Scientist,380000,USD,380000,US,100,US,L
|
||||
520,2022,MI,FT,Data Scientist,88000,CAD,69336,CA,100,CA,M
|
||||
521,2022,EN,FT,Computer Vision Engineer,10000,USD,10000,PT,100,LU,M
|
||||
522,2022,MI,FT,Data Analyst,20000,USD,20000,GR,100,GR,S
|
||||
523,2022,SE,FT,Data Analytics Lead,405000,USD,405000,US,100,US,L
|
||||
524,2022,MI,FT,Data Scientist,135000,USD,135000,US,100,US,L
|
||||
525,2022,SE,FT,Applied Data Scientist,177000,USD,177000,US,100,US,L
|
||||
526,2022,MI,FT,Data Scientist,78000,USD,78000,US,100,US,M
|
||||
527,2022,SE,FT,Data Analyst,135000,USD,135000,US,100,US,M
|
||||
528,2022,SE,FT,Data Analyst,100000,USD,100000,US,100,US,M
|
||||
529,2022,SE,FT,Data Analyst,90320,USD,90320,US,100,US,M
|
||||
530,2022,MI,FT,Data Analyst,85000,USD,85000,CA,0,CA,M
|
||||
531,2022,MI,FT,Data Analyst,75000,USD,75000,CA,0,CA,M
|
||||
532,2022,SE,FT,Machine Learning Engineer,214000,USD,214000,US,100,US,M
|
||||
533,2022,SE,FT,Machine Learning Engineer,192600,USD,192600,US,100,US,M
|
||||
534,2022,SE,FT,Data Architect,266400,USD,266400,US,100,US,M
|
||||
535,2022,SE,FT,Data Architect,213120,USD,213120,US,100,US,M
|
||||
536,2022,SE,FT,Data Analyst,112900,USD,112900,US,100,US,M
|
||||
537,2022,SE,FT,Data Engineer,155000,USD,155000,US,100,US,M
|
||||
538,2022,MI,FT,Data Scientist,141300,USD,141300,US,0,US,M
|
||||
539,2022,MI,FT,Data Scientist,102100,USD,102100,US,0,US,M
|
||||
540,2022,SE,FT,Data Analyst,115934,USD,115934,US,100,US,M
|
||||
541,2022,SE,FT,Data Analyst,81666,USD,81666,US,100,US,M
|
||||
542,2022,MI,FT,Data Engineer,206699,USD,206699,US,0,US,M
|
||||
543,2022,MI,FT,Data Engineer,99100,USD,99100,US,0,US,M
|
||||
544,2022,SE,FT,Data Engineer,130000,USD,130000,US,100,US,M
|
||||
545,2022,SE,FT,Data Engineer,115000,USD,115000,US,100,US,M
|
||||
546,2022,SE,FT,Data Engineer,110500,USD,110500,US,100,US,M
|
||||
547,2022,SE,FT,Data Engineer,130000,USD,130000,US,100,US,M
|
||||
548,2022,SE,FT,Data Analyst,99050,USD,99050,US,100,US,M
|
||||
549,2022,SE,FT,Data Engineer,160000,USD,160000,US,100,US,M
|
||||
550,2022,SE,FT,Data Scientist,205300,USD,205300,US,0,US,L
|
||||
551,2022,SE,FT,Data Scientist,140400,USD,140400,US,0,US,L
|
||||
552,2022,SE,FT,Data Scientist,176000,USD,176000,US,100,US,M
|
||||
553,2022,SE,FT,Data Scientist,144000,USD,144000,US,100,US,M
|
||||
554,2022,SE,FT,Data Engineer,200100,USD,200100,US,100,US,M
|
||||
555,2022,SE,FT,Data Engineer,160000,USD,160000,US,100,US,M
|
||||
556,2022,SE,FT,Data Engineer,145000,USD,145000,US,100,US,M
|
||||
557,2022,SE,FT,Data Engineer,70500,USD,70500,US,0,US,M
|
||||
558,2022,SE,FT,Data Scientist,205300,USD,205300,US,0,US,M
|
||||
559,2022,SE,FT,Data Scientist,140400,USD,140400,US,0,US,M
|
||||
560,2022,SE,FT,Analytics Engineer,205300,USD,205300,US,0,US,M
|
||||
561,2022,SE,FT,Analytics Engineer,184700,USD,184700,US,0,US,M
|
||||
562,2022,SE,FT,Data Engineer,175100,USD,175100,US,100,US,M
|
||||
563,2022,SE,FT,Data Engineer,140250,USD,140250,US,100,US,M
|
||||
564,2022,SE,FT,Data Analyst,116150,USD,116150,US,100,US,M
|
||||
565,2022,SE,FT,Data Engineer,54000,USD,54000,US,0,US,M
|
||||
566,2022,SE,FT,Data Analyst,170000,USD,170000,US,100,US,M
|
||||
567,2022,MI,FT,Data Analyst,50000,GBP,65438,GB,0,GB,M
|
||||
568,2022,SE,FT,Data Analyst,80000,USD,80000,US,100,US,M
|
||||
569,2022,SE,FT,Data Scientist,140000,USD,140000,US,100,US,M
|
||||
570,2022,SE,FT,Data Scientist,210000,USD,210000,US,100,US,M
|
||||
571,2022,SE,FT,Data Scientist,140000,USD,140000,US,100,US,M
|
||||
572,2022,SE,FT,Data Analyst,100000,USD,100000,US,100,US,M
|
||||
573,2022,SE,FT,Data Analyst,69000,USD,69000,US,100,US,M
|
||||
574,2022,SE,FT,Data Scientist,210000,USD,210000,US,100,US,M
|
||||
575,2022,SE,FT,Data Scientist,140000,USD,140000,US,100,US,M
|
||||
576,2022,SE,FT,Data Scientist,210000,USD,210000,US,100,US,M
|
||||
577,2022,SE,FT,Data Analyst,150075,USD,150075,US,100,US,M
|
||||
578,2022,SE,FT,Data Engineer,100000,USD,100000,US,100,US,M
|
||||
579,2022,SE,FT,Data Engineer,25000,USD,25000,US,100,US,M
|
||||
580,2022,SE,FT,Data Analyst,126500,USD,126500,US,100,US,M
|
||||
581,2022,SE,FT,Data Analyst,106260,USD,106260,US,100,US,M
|
||||
582,2022,SE,FT,Data Engineer,220110,USD,220110,US,100,US,M
|
||||
583,2022,SE,FT,Data Engineer,160080,USD,160080,US,100,US,M
|
||||
584,2022,SE,FT,Data Analyst,105000,USD,105000,US,100,US,M
|
||||
585,2022,SE,FT,Data Analyst,110925,USD,110925,US,100,US,M
|
||||
586,2022,MI,FT,Data Analyst,35000,GBP,45807,GB,0,GB,M
|
||||
587,2022,SE,FT,Data Scientist,140000,USD,140000,US,100,US,M
|
||||
588,2022,SE,FT,Data Analyst,99000,USD,99000,US,0,US,M
|
||||
589,2022,SE,FT,Data Analyst,60000,USD,60000,US,100,US,M
|
||||
590,2022,SE,FT,Data Architect,192564,USD,192564,US,100,US,M
|
||||
591,2022,SE,FT,Data Architect,144854,USD,144854,US,100,US,M
|
||||
592,2022,SE,FT,Data Scientist,230000,USD,230000,US,100,US,M
|
||||
593,2022,SE,FT,Data Scientist,150000,USD,150000,US,100,US,M
|
||||
594,2022,SE,FT,Data Analytics Manager,150260,USD,150260,US,100,US,M
|
||||
595,2022,SE,FT,Data Analytics Manager,109280,USD,109280,US,100,US,M
|
||||
596,2022,SE,FT,Data Scientist,210000,USD,210000,US,100,US,M
|
||||
597,2022,SE,FT,Data Analyst,170000,USD,170000,US,100,US,M
|
||||
598,2022,MI,FT,Data Scientist,160000,USD,160000,US,100,US,M
|
||||
599,2022,MI,FT,Data Scientist,130000,USD,130000,US,100,US,M
|
||||
600,2022,EN,FT,Data Analyst,67000,USD,67000,CA,0,CA,M
|
||||
601,2022,EN,FT,Data Analyst,52000,USD,52000,CA,0,CA,M
|
||||
602,2022,SE,FT,Data Engineer,154000,USD,154000,US,100,US,M
|
||||
603,2022,SE,FT,Data Engineer,126000,USD,126000,US,100,US,M
|
||||
604,2022,SE,FT,Data Analyst,129000,USD,129000,US,0,US,M
|
||||
605,2022,SE,FT,Data Analyst,150000,USD,150000,US,100,US,M
|
||||
606,2022,MI,FT,AI Scientist,200000,USD,200000,IN,100,US,L
|
||||
|
11
README.MD
Normal file
11
README.MD
Normal file
@@ -0,0 +1,11 @@
|
||||
# Machine Learning Models
|
||||
|
||||
I have created this repository for all the Machine Learning models I develop.
|
||||
You can find all the projects and their documentation in their respective directories.
|
||||
Below is a table of all the Machine Learning models created:
|
||||
|
||||
## Models
|
||||
|
||||
|Model Name|Link to directory|
|
||||
|----------|-----------------|
|
||||
| Test Model | |
|
||||
101
StrokePredictionModel/README.MD
Normal file
101
StrokePredictionModel/README.MD
Normal file
@@ -0,0 +1,101 @@
|
||||
# Brain Stroke prediction- DecisionTree
|
||||
|
||||
A stroke is an interruption of the blood supply to any part of the brain. If blood flow was stopped for longer than a few seconds and the brain cannot get blood and oxygen, brain cells can die, and the abilities controlled by that area of the brain are lost. In this Notebook we will use some features to see wether we will be able to predict the stoke or not? This is just a theoretical Machine Learning Model that will analyze the data and determine where the stroke can occur.
|
||||
|
||||
## Basic Outline For A Machine Learning Model:
|
||||
|
||||
Points to keep in mind when working with a machine learning model
|
||||
|
||||
1. Import the Data
|
||||
2. Clean the Data
|
||||
3. Split the Data into Training/Test Sets
|
||||
4. Create Model
|
||||
5. Train the Model
|
||||
6. Make Predictions
|
||||
7. Evaluate and Improve
|
||||
|
||||
## Libraries Needed:
|
||||
|
||||
1. Numpy
|
||||
2. Pandas
|
||||
3. Matplotlib
|
||||
4. Scikit-Learn
|
||||
5. Seaborn
|
||||
6. Cufflinks
|
||||
|
||||
## Tools Needed:
|
||||
|
||||
1. Jupyter (IDE)
|
||||
2. https://www.kaggle.com (To download the dataset)
|
||||
3. https://dreampuf.github.io/GraphvizOnline/ (To visualize the graph)
|
||||
|
||||
## Methodology:
|
||||
|
||||
1. IMPORTING LIBRARIES AND LOADING DATA
|
||||
2. DATA EXPLORATION
|
||||
3. VIZUALIZATION
|
||||
4. DATA PREPROCESSING
|
||||
|
||||
a. Target and Feature values / Train Test Split
|
||||
5. MODEL BUILDING
|
||||
|
||||
a. Decision Tree Classifier and Gini method
|
||||
|
||||
b. Prediction Model File Generation
|
||||
|
||||
c. Prediction Model File Loading
|
||||
|
||||
d. Model accuracy score
|
||||
|
||||
i. Testing Accuracy
|
||||
ii. Training Accuracy
|
||||
6. MODEL WORKING GRAPH
|
||||
|
||||
|
||||
## In-Depth Working of this model:
|
||||
|
||||
### Imports
|
||||
|
||||
1. Numpy: For working with arrays.
|
||||
2. Pandas: Used to analyze the data.
|
||||
3. OS Module: For working with files/directories.
|
||||
4. matplotlib: Used for programmatic plot generation.
|
||||
5. Seaborn: Used for statistical graphics.
|
||||
6. Warnings: Used to control warnings in Python.
|
||||
7. sklearn: These are simple and efficient tools for predictive data analsis.
|
||||
|
||||
### Data Exploration
|
||||
|
||||
1. pd.read_csv: Used to load cvs files in pandas dataframe.
|
||||
2. df.head: It returns first 'n' rows.
|
||||
3. pd.info: It prints information about the dataframe.
|
||||
4. df.describe: It generates descriptive statistics.
|
||||
5. unique: It returns unique values from the dataframe.
|
||||
|
||||
### Vizualization
|
||||
|
||||
1. Cufflinks: It connects plotly with pandas to create charts directly on the dataframe.
|
||||
2. go_offline: This enables us to use plotly offline rather than online.
|
||||
3. offline=Flase: It enables the charts to not render locally.
|
||||
4. df.groupby: It enables us to group dataframe using a mapper or a series of colomns.
|
||||
5. df.values: It returns a numpy representation of the dataframe.
|
||||
6. df.iplot: It is for building interactive plots.
|
||||
7. df.sum: It returns a sum of values over the requested axis.
|
||||
|
||||
### Data Preprocessing
|
||||
|
||||
1. df.isnull: It detects missing values.
|
||||
2. df.drop: It drops speficied labels from rows and columns.
|
||||
3. get_dummies: It converts categorial variable into dummy/indicator variable.
|
||||
|
||||
### Model Building
|
||||
|
||||
1. criterion: The function to measure the quality of a split.
|
||||
2. random_state: It controls the randomness of the estimator.
|
||||
3. max_depth: The maximum depth of the tree.
|
||||
4. clf_gini.fit: It is used to fit training data.
|
||||
5. joblib.dump: It collects all the learning and dumps it in one file.
|
||||
6. joblib.load: It reconstructs the file for use which is created by 'dump' method.
|
||||
7. clf_gini.predict: It is a method which operates using numpy.argmax function on the output of predic_probo.
|
||||
8. clf_gini.score: It returns the mean accuracy on the given test data and labels.
|
||||
9. export_graphviz: It is used to export a decision tree in a '.dot' format.
|
||||
4982
StrokePredictionModel/full_data.csv
Normal file
4982
StrokePredictionModel/full_data.csv
Normal file
File diff suppressed because it is too large
Load Diff
202
StrokePredictionModel/full_filled_stroke_data (1).csv
Normal file
202
StrokePredictionModel/full_filled_stroke_data (1).csv
Normal file
@@ -0,0 +1,202 @@
|
||||
gender,age,hypertension,heart_disease,ever_married,work_type,Residence_type,avg_glucose_level,bmi,smoking_status,stroke
|
||||
Female,61.0,0,0,Yes,Self-employed,Rural,202.21,31.555602417555065,never smoked,1
|
||||
Female,59.0,0,0,Yes,Private,Rural,76.15,30.24293671780551,Unknown,1
|
||||
Male,78.0,0,1,Yes,Private,Urban,219.84,30.698951437189447,Unknown,1
|
||||
Male,57.0,0,1,No,Govt_job,Urban,217.08,33.80840960032553,Unknown,1
|
||||
Male,58.0,0,0,Yes,Private,Rural,189.84,31.378533851873115,Unknown,1
|
||||
Male,59.0,0,0,Yes,Private,Rural,211.78,33.4845680869329,formerly smoked,1
|
||||
Female,63.0,0,0,Yes,Private,Urban,90.9,30.042545164065906,formerly smoked,1
|
||||
Female,75.0,0,1,No,Self-employed,Urban,109.78,28.31827337114426,Unknown,1
|
||||
Female,76.0,0,0,No,Private,Urban,89.96,28.397892673424312,Unknown,1
|
||||
Male,78.0,1,0,Yes,Private,Urban,75.32,29.139780244728872,formerly smoked,1
|
||||
Female,63.0,0,0,Yes,Govt_job,Urban,197.54,31.602317143886843,never smoked,1
|
||||
Male,78.0,0,0,Yes,Private,Urban,237.75,29.316691843518786,formerly smoked,1
|
||||
Male,75.0,0,0,Yes,Private,Urban,104.72,28.31827337114426,Unknown,1
|
||||
Female,76.0,0,0,Yes,Govt_job,Rural,62.57,27.954912232096934,formerly smoked,1
|
||||
Female,51.0,0,0,Yes,Private,Urban,165.31,30.491106831242366,never smoked,1
|
||||
Female,66.0,0,0,Yes,Self-employed,Urban,101.45,29.292953098797682,Unknown,1
|
||||
Male,58.0,0,0,Yes,Private,Urban,71.2,30.003880754655786,Unknown,1
|
||||
Male,58.0,0,0,Yes,Private,Urban,82.3,30.199570565349735,smokes,1
|
||||
Female,76.0,0,0,Yes,Self-employed,Urban,106.41,28.202069360450302,formerly smoked,1
|
||||
Female,72.0,0,0,Yes,Private,Urban,219.91,32.120369077356244,Unknown,1
|
||||
Male,78.0,1,0,Yes,Self-employed,Urban,93.13,29.210833962046138,formerly smoked,1
|
||||
Female,75.0,0,0,Yes,Govt_job,Urban,62.48,28.070471960148872,Unknown,1
|
||||
Female,38.0,0,0,Yes,Private,Rural,101.45,29.863774950891848,formerly smoked,1
|
||||
Male,65.0,0,0,Yes,Self-employed,Urban,68.43,29.583323235372124,formerly smoked,1
|
||||
Female,79.0,0,0,Yes,Private,Rural,169.67,27.971856904548776,Unknown,1
|
||||
Female,76.0,0,0,Yes,Private,Urban,57.92,27.940892278838152,formerly smoked,1
|
||||
Male,71.0,0,1,Yes,Private,Urban,81.76,28.9457498615031,smokes,1
|
||||
Female,1.32,0,0,No,children,Urban,70.37,18.719259745575055,Unknown,1
|
||||
Male,79.0,1,0,Yes,Private,Rural,75.02,29.139780244728872,never smoked,1
|
||||
Male,64.0,0,0,Yes,Self-employed,Rural,111.98,29.79052811527944,formerly smoked,1
|
||||
Female,79.0,1,1,No,Self-employed,Rural,60.94,28.759856260413944,never smoked,1
|
||||
Female,78.0,0,0,Yes,Self-employed,Rural,60.67,27.08627617177477,formerly smoked,1
|
||||
Female,80.0,0,0,Yes,Govt_job,Urban,110.66,27.282122315103724,Unknown,1
|
||||
Female,77.0,0,0,No,Private,Urban,81.32,28.08147438013183,Unknown,1
|
||||
Male,61.0,0,1,Yes,Private,Urban,209.86,32.94704544116234,Unknown,1
|
||||
Male,79.0,0,0,Yes,Private,Rural,114.77,27.243396290267707,formerly smoked,1
|
||||
Male,74.0,0,0,Yes,Private,Urban,167.13,28.736400149250798,Unknown,1
|
||||
Female,76.0,1,1,Yes,Self-employed,Urban,199.86,31.684031473836264,smokes,1
|
||||
Male,74.0,0,0,Yes,Self-employed,Rural,60.98,28.070471960148872,never smoked,1
|
||||
Male,71.0,1,0,Yes,Self-employed,Rural,87.8,30.763683412344054,Unknown,1
|
||||
Male,34.0,0,1,Yes,Private,Urban,106.23,29.70237259796381,formerly smoked,0
|
||||
Female,76.0,1,0,Yes,Self-employed,Urban,209.58,33.07902301871912,never smoked,0
|
||||
Female,63.0,0,0,Yes,Govt_job,Rural,79.92,29.970218681520958,smokes,0
|
||||
Male,61.0,0,0,Yes,Govt_job,Urban,184.15,30.863162932887985,Unknown,0
|
||||
Male,54.0,1,0,Yes,Private,Rural,198.69,33.737576205047304,smokes,0
|
||||
Male,40.0,0,0,Yes,Private,Rural,89.77,30.0668053524742,smokes,0
|
||||
Female,48.0,1,0,No,Private,Rural,118.14,31.697085992503364,formerly smoked,0
|
||||
Male,61.0,0,1,Yes,Private,Urban,88.27,29.99730179513789,never smoked,0
|
||||
Male,31.0,1,0,Yes,Govt_job,Urban,92.11,31.198140115564627,never smoked,0
|
||||
Female,43.0,0,0,Yes,Govt_job,Rural,107.42,29.854190333221652,never smoked,0
|
||||
Female,9.0,0,0,No,children,Urban,95.81,20.018427356531838,Unknown,0
|
||||
Male,52.0,0,0,Yes,Private,Urban,226.7,33.117246460733575,smokes,0
|
||||
Female,77.0,0,1,Yes,Private,Rural,183.1,28.982843563019326,never smoked,0
|
||||
Female,17.0,0,0,No,Private,Rural,83.23,26.420807382569834,never smoked,0
|
||||
Female,71.0,0,0,Yes,Self-employed,Urban,91.35,29.016504284733173,formerly smoked,0
|
||||
Female,35.0,0,0,No,Govt_job,Urban,83.76,29.789599917563965,smokes,0
|
||||
Female,23.0,0,0,No,Private,Urban,110.16,26.997282265398667,never smoked,0
|
||||
Male,40.0,0,0,No,Private,Urban,88.27,30.0668053524742,formerly smoked,0
|
||||
Female,23.0,0,0,No,Private,Rural,193.22,28.383477868385224,smokes,0
|
||||
Female,71.0,1,0,Yes,Self-employed,Rural,66.12,30.375307026800215,never smoked,0
|
||||
Male,13.0,0,0,No,children,Urban,71.73,22.562797542765956,Unknown,0
|
||||
Male,73.0,1,0,Yes,Self-employed,Rural,102.06,30.19632033911637,Unknown,0
|
||||
Female,3.0,0,0,No,children,Urban,79.63,18.896758341975225,Unknown,0
|
||||
Male,51.0,0,0,Yes,Private,Rural,217.71,33.749649271841086,formerly smoked,0
|
||||
Male,35.0,0,0,Yes,Private,Rural,115.92,29.612603644460908,formerly smoked,0
|
||||
Female,73.0,0,0,Yes,Self-employed,Rural,79.69,28.596578523400677,formerly smoked,0
|
||||
Female,6.0,0,0,No,children,Urban,201.25,20.50813016611414,Unknown,0
|
||||
Male,46.0,1,0,Yes,Private,Rural,73.72,31.606570696468122,smokes,0
|
||||
Female,71.0,0,0,Yes,Private,Urban,214.77,32.27387095184459,Unknown,0
|
||||
Female,54.0,1,0,Yes,Private,Rural,98.74,31.89971211530221,never smoked,0
|
||||
Female,80.0,0,0,Yes,Govt_job,Urban,84.86,27.411314469808467,Unknown,0
|
||||
Female,49.0,0,0,Yes,Private,Rural,67.27,29.876490922740366,formerly smoked,0
|
||||
Male,72.0,0,0,Yes,Self-employed,Rural,72.09,28.54569321378707,smokes,0
|
||||
Male,25.0,0,0,Yes,Private,Rural,78.29,27.173222235093398,smokes,0
|
||||
Male,27.0,0,0,No,Private,Rural,191.79,28.89969575625761,smokes,0
|
||||
Male,51.0,1,0,Yes,Private,Rural,163.56,32.29329460570186,formerly smoked,0
|
||||
Male,48.0,0,0,Yes,Self-employed,Rural,216.88,33.69541657142078,smokes,0
|
||||
Male,7.0,0,0,No,children,Urban,87.94,19.483194786207072,Unknown,0
|
||||
Female,61.0,1,1,Yes,Private,Urban,237.58,33.746401289230256,formerly smoked,0
|
||||
Female,25.0,0,0,Yes,Govt_job,Urban,93.23,27.24120740794051,smokes,0
|
||||
Male,30.0,0,0,Yes,Private,Urban,91.23,29.28032172443538,smokes,0
|
||||
Male,71.0,1,0,Yes,Self-employed,Rural,93.6,30.719672786758665,never smoked,0
|
||||
Male,47.0,0,0,No,Private,Rural,237.17,32.05850252051601,Unknown,0
|
||||
Male,76.0,0,1,Yes,Private,Urban,79.05,28.328095943797504,Unknown,0
|
||||
Female,29.0,0,0,No,Private,Urban,81.43,28.61994283496398,formerly smoked,0
|
||||
Female,48.0,0,1,Yes,Self-employed,Urban,101.22,30.006573102139797,formerly smoked,0
|
||||
Female,57.0,1,0,Yes,Private,Urban,210.0,35.18860462517498,never smoked,0
|
||||
Male,58.0,0,0,Yes,Private,Urban,94.0,30.272074039541987,Unknown,0
|
||||
Male,45.0,0,1,Yes,Private,Rural,93.77,30.013810750424852,Unknown,0
|
||||
Male,66.0,0,0,Yes,Private,Urban,190.4,30.538184095161768,formerly smoked,0
|
||||
Male,59.0,0,1,Yes,Govt_job,Urban,188.69,31.378533851873115,formerly smoked,0
|
||||
Male,34.0,0,0,Yes,Private,Rural,86.51,29.904709563307257,formerly smoked,0
|
||||
Male,69.0,1,0,Yes,Private,Rural,107.11,30.624831677910333,smokes,0
|
||||
Male,66.0,0,0,Yes,Self-employed,Urban,71.38,29.18710939880596,formerly smoked,0
|
||||
Female,48.0,0,0,Yes,Self-employed,Rural,209.9,33.17294727137241,smokes,0
|
||||
Male,32.0,0,0,No,Private,Rural,95.58,29.752554356966748,smokes,0
|
||||
Male,60.0,0,0,Yes,Self-employed,Urban,185.71,31.378533851873115,Unknown,0
|
||||
Female,30.0,0,0,No,Govt_job,Urban,88.2,29.28032172443538,smokes,0
|
||||
Female,60.0,0,0,Yes,Self-employed,Urban,203.04,32.130179775170376,smokes,0
|
||||
Male,10.0,0,0,No,children,Rural,99.87,20.263791073638025,formerly smoked,0
|
||||
Male,20.0,0,0,No,Private,Rural,70.96,26.305040919138523,Unknown,0
|
||||
Male,77.0,0,0,Yes,Private,Urban,74.26,28.004566476544774,formerly smoked,0
|
||||
Male,67.0,0,0,Yes,Private,Urban,92.73,29.081488595208445,never smoked,0
|
||||
Female,42.0,0,0,Yes,Private,Urban,208.06,32.49735905629397,smokes,0
|
||||
Female,60.0,1,0,Yes,Private,Urban,109.0,31.89971211530221,Unknown,0
|
||||
Male,0.48,0,0,No,children,Urban,73.02,17.89929451491795,Unknown,0
|
||||
Male,35.0,0,0,Yes,Private,Rural,77.48,29.832593105583623,formerly smoked,0
|
||||
Male,50.0,1,0,No,Private,Urban,81.96,31.97914377106592,formerly smoked,0
|
||||
Female,19.0,0,0,No,Private,Rural,72.39,26.284524627193612,smokes,0
|
||||
Female,77.0,1,0,Yes,Self-employed,Urban,109.51,29.663123711666024,never smoked,0
|
||||
Male,67.0,0,1,Yes,Private,Rural,97.24,28.944067909678317,Unknown,0
|
||||
Female,20.0,0,0,No,Private,Urban,89.03,26.59200375926125,smokes,0
|
||||
Male,49.0,0,0,Yes,Private,Rural,79.64,30.188174647677695,smokes,0
|
||||
Male,77.0,0,1,Yes,Govt_job,Rural,106.03,27.956143010263066,Unknown,0
|
||||
Female,52.0,1,0,Yes,Self-employed,Rural,111.38,31.791922363152473,smokes,0
|
||||
Male,43.0,0,0,Yes,Govt_job,Rural,80.07,29.984830834123173,never smoked,0
|
||||
Female,69.0,1,0,Yes,Govt_job,Urban,112.2,30.574928449662846,never smoked,0
|
||||
Female,34.0,1,0,Yes,Self-employed,Urban,100.61,31.47526322033101,Unknown,0
|
||||
Male,78.0,0,1,Yes,Self-employed,Urban,243.73,28.02386637636746,smokes,0
|
||||
Male,76.0,0,1,Yes,Self-employed,Urban,67.03,28.02871647102671,never smoked,0
|
||||
Male,62.0,1,1,Yes,Private,Rural,176.25,32.43801447032291,never smoked,0
|
||||
Female,71.0,1,0,Yes,Private,Urban,105.55,30.557687319717626,smokes,0
|
||||
Male,79.0,0,1,Yes,Private,Urban,213.38,30.504871562608173,Unknown,0
|
||||
Male,79.0,0,1,Yes,Private,Rural,82.27,27.411314469808467,never smoked,0
|
||||
Male,54.0,0,0,Yes,Private,Rural,74.06,30.162336272799983,never smoked,0
|
||||
Female,73.0,1,0,Yes,Private,Rural,217.84,33.899575602263056,never smoked,0
|
||||
Female,5.0,0,0,No,children,Rural,105.18,19.03363307440957,Unknown,0
|
||||
Female,38.0,0,0,Yes,Private,Rural,217.55,33.541750072202845,smokes,0
|
||||
Male,72.0,1,0,Yes,Private,Rural,231.71,33.22529657056137,Unknown,0
|
||||
Male,14.0,0,0,No,Private,Rural,110.72,25.92538060204817,never smoked,0
|
||||
Male,50.0,0,0,Yes,Private,Urban,67.02,29.876490922740366,formerly smoked,0
|
||||
Male,29.0,1,0,Yes,Private,Urban,77.55,30.352153075720764,formerly smoked,0
|
||||
Male,75.0,1,0,Yes,Private,Rural,198.79,31.809102388818047,smokes,0
|
||||
Female,68.0,1,1,Yes,Private,Rural,233.3,33.480734073500464,Unknown,0
|
||||
Female,33.0,1,0,No,Private,Rural,97.87,31.430239364510037,smokes,0
|
||||
Male,63.0,0,1,Yes,Self-employed,Urban,82.72,29.927100195480005,never smoked,0
|
||||
Female,56.0,0,0,Yes,Private,Urban,102.97,30.111585941603717,smokes,0
|
||||
Male,70.0,0,0,Yes,Govt_job,Urban,202.55,30.781985990405303,formerly smoked,0
|
||||
Male,71.0,0,1,Yes,Private,Urban,204.98,30.976758580936625,formerly smoked,0
|
||||
Female,73.0,0,0,No,Self-employed,Rural,69.35,28.36584508965892,never smoked,0
|
||||
Female,67.0,1,0,Yes,Private,Rural,85.48,30.75318633749586,smokes,0
|
||||
Female,62.0,1,0,Yes,Self-employed,Urban,75.78,31.75309029478169,smokes,0
|
||||
Female,38.0,0,0,Yes,Private,Urban,91.44,30.0668053524742,Unknown,0
|
||||
Female,47.0,0,0,Yes,Self-employed,Rural,195.61,31.720165526413464,never smoked,0
|
||||
Female,42.0,0,0,Yes,Private,Urban,73.37,29.948147355072038,smokes,0
|
||||
Male,58.0,0,0,Yes,Govt_job,Urban,160.87,30.546215505510165,formerly smoked,0
|
||||
Male,44.0,1,0,Yes,Private,Rural,84.1,31.723509495457375,Unknown,0
|
||||
Male,42.0,0,0,Yes,Private,Urban,177.91,30.68964575227439,Unknown,0
|
||||
Male,78.0,1,0,Yes,Self-employed,Urban,90.19,29.210833962046138,Unknown,0
|
||||
Female,68.0,0,0,No,Private,Urban,82.85,28.968393628910906,smokes,0
|
||||
Male,39.0,0,0,Yes,Private,Rural,84.18,29.951301422935355,smokes,0
|
||||
Male,60.0,0,0,Yes,Self-employed,Rural,212.02,33.4845680869329,Unknown,0
|
||||
Female,31.0,0,0,Yes,Self-employed,Urban,87.23,29.435237286859376,formerly smoked,0
|
||||
Male,67.0,0,0,Yes,Private,Urban,260.85,29.09959343959128,Unknown,0
|
||||
Female,52.0,1,0,Yes,Self-employed,Rural,104.45,31.843071754782194,never smoked,0
|
||||
Female,53.0,0,0,Yes,Private,Urban,227.68,33.117246460733575,never smoked,0
|
||||
Female,33.0,0,0,No,Private,Urban,84.4,29.746116027365073,smokes,0
|
||||
Female,53.0,0,0,No,Private,Rural,235.45,32.48706845856044,formerly smoked,0
|
||||
Female,49.0,0,0,Yes,Private,Rural,107.55,30.056974383018026,Unknown,0
|
||||
Male,52.0,0,1,No,Self-employed,Rural,79.81,30.188174647677695,formerly smoked,0
|
||||
Female,41.0,0,0,Yes,Self-employed,Rural,76.66,29.99444194055943,Unknown,0
|
||||
Male,1.88,0,0,No,children,Rural,143.97,19.018775636939367,Unknown,0
|
||||
Male,34.0,0,0,Yes,Private,Urban,99.23,29.70237259796381,smokes,0
|
||||
Female,16.0,0,0,No,Private,Urban,89.45,26.52751396445424,Unknown,0
|
||||
Female,45.0,0,0,Yes,Private,Urban,202.66,31.86199054694296,never smoked,0
|
||||
Male,1.08,0,0,No,children,Rural,74.5,18.797637948568624,Unknown,0
|
||||
Male,1.8,0,0,No,children,Urban,68.8,18.719259745575055,Unknown,0
|
||||
Female,13.0,0,0,No,children,Rural,219.81,25.654078421843533,Unknown,0
|
||||
Female,61.0,0,0,Yes,Private,Rural,219.38,33.46726940704127,never smoked,0
|
||||
Female,37.0,0,0,No,Govt_job,Rural,72.08,29.595595561791818,formerly smoked,0
|
||||
Male,32.0,1,0,No,Govt_job,Urban,58.24,31.098300161525284,formerly smoked,0
|
||||
Female,79.0,0,0,Yes,Private,Rural,208.05,29.78694460325683,smokes,0
|
||||
Male,8.0,0,0,No,children,Urban,78.76,19.428195618403347,Unknown,0
|
||||
Female,75.0,0,1,Yes,Self-employed,Urban,83.88,28.402910258289666,smokes,0
|
||||
Female,79.0,1,0,Yes,Self-employed,Rural,92.43,29.210833962046138,never smoked,0
|
||||
Female,69.0,0,1,Yes,Private,Urban,207.6,31.231186313834275,never smoked,0
|
||||
Male,31.0,0,0,Yes,Private,Urban,108.62,29.23491653621969,smokes,0
|
||||
Female,82.0,1,0,Yes,Private,Urban,222.52,31.75340319266665,formerly smoked,0
|
||||
Male,32.0,1,0,No,Private,Rural,74.43,31.497422037342364,Unknown,0
|
||||
Female,17.0,0,0,No,Private,Urban,92.97,26.52751396445424,formerly smoked,0
|
||||
Female,18.0,0,0,No,Private,Rural,101.12,26.437222755448115,smokes,0
|
||||
Male,59.0,1,0,Yes,Govt_job,Rural,253.93,32.1306496403525,formerly smoked,0
|
||||
Male,3.0,0,0,No,children,Rural,194.75,20.11991532687849,Unknown,0
|
||||
Female,20.0,0,0,No,Govt_job,Rural,79.53,26.52504773335733,never smoked,0
|
||||
Female,78.0,0,0,Yes,Govt_job,Urban,101.76,27.32855855724956,smokes,0
|
||||
Male,52.0,1,0,Yes,Govt_job,Rural,116.62,31.749263451594498,smokes,0
|
||||
Female,65.0,0,1,Yes,Private,Rural,57.52,29.420252347958517,formerly smoked,0
|
||||
Male,59.0,0,0,Yes,Private,Urban,223.16,33.17530861079809,Unknown,0
|
||||
Female,78.0,1,1,Yes,Private,Rural,206.53,31.17927645645906,never smoked,0
|
||||
Female,70.0,0,1,Yes,Self-employed,Rural,65.68,28.64225263181359,Unknown,0
|
||||
Female,70.0,0,1,Yes,Self-employed,Urban,240.69,30.87306207992168,smokes,0
|
||||
Male,37.0,0,0,Yes,Private,Rural,107.06,29.70237259796381,smokes,0
|
||||
Male,72.0,0,1,Yes,Private,Rural,238.27,30.69765019772622,smokes,0
|
||||
Male,1.32,0,0,No,children,Rural,107.02,18.799209785253908,Unknown,0
|
||||
Male,58.0,0,0,Yes,Govt_job,Urban,84.94,30.199570565349735,never smoked,0
|
||||
Male,31.0,0,0,No,Private,Urban,215.07,32.721655047898544,smokes,0
|
||||
Male,41.0,0,0,No,Private,Rural,70.15,29.75663127443438,formerly smoked,0
|
||||
Male,40.0,0,0,Yes,Private,Urban,191.15,31.12417217394211,smokes,0
|
||||
Female,45.0,1,0,Yes,Govt_job,Rural,95.02,31.79830364006429,smokes,0
|
||||
Male,40.0,0,0,Yes,Private,Rural,83.94,29.951301422935355,smokes,0
|
||||
Female,80.0,1,0,Yes,Private,Urban,83.75,29.09742107186247,never smoked,0
|
||||
|
16500
StrokePredictionModel/main.html
Normal file
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StrokePredictionModel/main.html
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3268
StrokePredictionModel/main.ipynb
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StrokePredictionModel/main.ipynb
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StrokePredictionModel/stroke-prediction-model.joblib
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StrokePredictionModel/stroke-prediction-model.joblib
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StrokePredictionModel/stroke-prediction-visual-model.dot
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StrokePredictionModel/stroke-prediction-visual-model.dot
Normal file
@@ -0,0 +1,949 @@
|
||||
digraph Tree {
|
||||
node [shape=box, style="filled, rounded", color="black", fontname="helvetica"] ;
|
||||
edge [fontname="helvetica"] ;
|
||||
0 [label="age <= 65.5\ngini = 0.093\nsamples = 3337\nvalue = [3174, 163]", fillcolor="#e68743"] ;
|
||||
1 [label="age <= 48.5\ngini = 0.043\nsamples = 2671\nvalue = [2612, 59]", fillcolor="#e6843d"] ;
|
||||
0 -> 1 [labeldistance=2.5, labelangle=45, headlabel="True"] ;
|
||||
2 [label="age <= 37.5\ngini = 0.012\nsamples = 1835\nvalue = [1824, 11]", fillcolor="#e5823a"] ;
|
||||
1 -> 2 ;
|
||||
3 [label="smoking_status_smokes <= 0.5\ngini = 0.002\nsamples = 1322\nvalue = [1321, 1]", fillcolor="#e58139"] ;
|
||||
2 -> 3 ;
|
||||
4 [label="gini = 0.0\nsamples = 1164\nvalue = [1164, 0]", fillcolor="#e58139"] ;
|
||||
3 -> 4 ;
|
||||
5 [label="age <= 31.5\ngini = 0.013\nsamples = 158\nvalue = [157, 1]", fillcolor="#e5823a"] ;
|
||||
3 -> 5 ;
|
||||
6 [label="gini = 0.0\nsamples = 116\nvalue = [116, 0]", fillcolor="#e58139"] ;
|
||||
5 -> 6 ;
|
||||
7 [label="avg_glucose_level <= 76.645\ngini = 0.046\nsamples = 42\nvalue = [41, 1]", fillcolor="#e6843e"] ;
|
||||
5 -> 7 ;
|
||||
8 [label="avg_glucose_level <= 75.285\ngini = 0.165\nsamples = 11\nvalue = [10, 1]", fillcolor="#e88e4d"] ;
|
||||
7 -> 8 ;
|
||||
9 [label="gini = 0.0\nsamples = 10\nvalue = [10, 0]", fillcolor="#e58139"] ;
|
||||
8 -> 9 ;
|
||||
10 [label="gini = 0.0\nsamples = 1\nvalue = [0, 1]", fillcolor="#399de5"] ;
|
||||
8 -> 10 ;
|
||||
11 [label="gini = 0.0\nsamples = 31\nvalue = [31, 0]", fillcolor="#e58139"] ;
|
||||
7 -> 11 ;
|
||||
12 [label="avg_glucose_level <= 86.965\ngini = 0.038\nsamples = 513\nvalue = [503, 10]", fillcolor="#e6843d"] ;
|
||||
2 -> 12 ;
|
||||
13 [label="avg_glucose_level <= 86.935\ngini = 0.069\nsamples = 225\nvalue = [217, 8]", fillcolor="#e68640"] ;
|
||||
12 -> 13 ;
|
||||
14 [label="avg_glucose_level <= 82.26\ngini = 0.061\nsamples = 224\nvalue = [217, 7]", fillcolor="#e6853f"] ;
|
||||
13 -> 14 ;
|
||||
15 [label="hypertension <= 0.5\ngini = 0.035\nsamples = 170\nvalue = [167, 3]", fillcolor="#e5833d"] ;
|
||||
14 -> 15 ;
|
||||
16 [label="age <= 44.5\ngini = 0.025\nsamples = 161\nvalue = [159, 2]", fillcolor="#e5833b"] ;
|
||||
15 -> 16 ;
|
||||
17 [label="gini = 0.0\nsamples = 97\nvalue = [97, 0]", fillcolor="#e58139"] ;
|
||||
16 -> 17 ;
|
||||
18 [label="smoking_status_never smoked <= 0.5\ngini = 0.061\nsamples = 64\nvalue = [62, 2]", fillcolor="#e6853f"] ;
|
||||
16 -> 18 ;
|
||||
19 [label="gini = 0.0\nsamples = 38\nvalue = [38, 0]", fillcolor="#e58139"] ;
|
||||
18 -> 19 ;
|
||||
20 [label="age <= 46.5\ngini = 0.142\nsamples = 26\nvalue = [24, 2]", fillcolor="#e78c49"] ;
|
||||
18 -> 20 ;
|
||||
21 [label="work_type_Private <= 0.5\ngini = 0.278\nsamples = 12\nvalue = [10, 2]", fillcolor="#ea9a61"] ;
|
||||
20 -> 21 ;
|
||||
22 [label="gini = 0.0\nsamples = 7\nvalue = [7, 0]", fillcolor="#e58139"] ;
|
||||
21 -> 22 ;
|
||||
23 [label="avg_glucose_level <= 79.6\ngini = 0.48\nsamples = 5\nvalue = [3, 2]", fillcolor="#f6d5bd"] ;
|
||||
21 -> 23 ;
|
||||
24 [label="bmi <= 29.05\ngini = 0.444\nsamples = 3\nvalue = [1, 2]", fillcolor="#9ccef2"] ;
|
||||
23 -> 24 ;
|
||||
25 [label="gini = 0.0\nsamples = 1\nvalue = [1, 0]", fillcolor="#e58139"] ;
|
||||
24 -> 25 ;
|
||||
26 [label="gini = 0.0\nsamples = 2\nvalue = [0, 2]", fillcolor="#399de5"] ;
|
||||
24 -> 26 ;
|
||||
27 [label="gini = 0.0\nsamples = 2\nvalue = [2, 0]", fillcolor="#e58139"] ;
|
||||
23 -> 27 ;
|
||||
28 [label="gini = 0.0\nsamples = 14\nvalue = [14, 0]", fillcolor="#e58139"] ;
|
||||
20 -> 28 ;
|
||||
29 [label="smoking_status_smokes <= 0.5\ngini = 0.198\nsamples = 9\nvalue = [8, 1]", fillcolor="#e89152"] ;
|
||||
15 -> 29 ;
|
||||
30 [label="gini = 0.0\nsamples = 8\nvalue = [8, 0]", fillcolor="#e58139"] ;
|
||||
29 -> 30 ;
|
||||
31 [label="gini = 0.0\nsamples = 1\nvalue = [0, 1]", fillcolor="#399de5"] ;
|
||||
29 -> 31 ;
|
||||
32 [label="avg_glucose_level <= 82.3\ngini = 0.137\nsamples = 54\nvalue = [50, 4]", fillcolor="#e78b49"] ;
|
||||
14 -> 32 ;
|
||||
33 [label="gini = 0.0\nsamples = 1\nvalue = [0, 1]", fillcolor="#399de5"] ;
|
||||
32 -> 33 ;
|
||||
34 [label="avg_glucose_level <= 83.425\ngini = 0.107\nsamples = 53\nvalue = [50, 3]", fillcolor="#e78945"] ;
|
||||
32 -> 34 ;
|
||||
35 [label="avg_glucose_level <= 83.19\ngini = 0.278\nsamples = 12\nvalue = [10, 2]", fillcolor="#ea9a61"] ;
|
||||
34 -> 35 ;
|
||||
36 [label="gini = 0.0\nsamples = 9\nvalue = [9, 0]", fillcolor="#e58139"] ;
|
||||
35 -> 36 ;
|
||||
37 [label="smoking_status_smokes <= 0.5\ngini = 0.444\nsamples = 3\nvalue = [1, 2]", fillcolor="#9ccef2"] ;
|
||||
35 -> 37 ;
|
||||
38 [label="gini = 0.0\nsamples = 2\nvalue = [0, 2]", fillcolor="#399de5"] ;
|
||||
37 -> 38 ;
|
||||
39 [label="gini = 0.0\nsamples = 1\nvalue = [1, 0]", fillcolor="#e58139"] ;
|
||||
37 -> 39 ;
|
||||
40 [label="age <= 47.5\ngini = 0.048\nsamples = 41\nvalue = [40, 1]", fillcolor="#e6843e"] ;
|
||||
34 -> 40 ;
|
||||
41 [label="gini = 0.0\nsamples = 37\nvalue = [37, 0]", fillcolor="#e58139"] ;
|
||||
40 -> 41 ;
|
||||
42 [label="avg_glucose_level <= 84.38\ngini = 0.375\nsamples = 4\nvalue = [3, 1]", fillcolor="#eeab7b"] ;
|
||||
40 -> 42 ;
|
||||
43 [label="gini = 0.0\nsamples = 1\nvalue = [0, 1]", fillcolor="#399de5"] ;
|
||||
42 -> 43 ;
|
||||
44 [label="gini = 0.0\nsamples = 3\nvalue = [3, 0]", fillcolor="#e58139"] ;
|
||||
42 -> 44 ;
|
||||
45 [label="gini = 0.0\nsamples = 1\nvalue = [0, 1]", fillcolor="#399de5"] ;
|
||||
13 -> 45 ;
|
||||
46 [label="bmi <= 45.45\ngini = 0.014\nsamples = 288\nvalue = [286, 2]", fillcolor="#e5823a"] ;
|
||||
12 -> 46 ;
|
||||
47 [label="age <= 38.5\ngini = 0.007\nsamples = 283\nvalue = [282, 1]", fillcolor="#e5813a"] ;
|
||||
46 -> 47 ;
|
||||
48 [label="smoking_status_formerly smoked <= 0.5\ngini = 0.074\nsamples = 26\nvalue = [25, 1]", fillcolor="#e68641"] ;
|
||||
47 -> 48 ;
|
||||
49 [label="gini = 0.0\nsamples = 20\nvalue = [20, 0]", fillcolor="#e58139"] ;
|
||||
48 -> 49 ;
|
||||
50 [label="bmi <= 30.35\ngini = 0.278\nsamples = 6\nvalue = [5, 1]", fillcolor="#ea9a61"] ;
|
||||
48 -> 50 ;
|
||||
51 [label="gini = 0.0\nsamples = 1\nvalue = [0, 1]", fillcolor="#399de5"] ;
|
||||
50 -> 51 ;
|
||||
52 [label="gini = 0.0\nsamples = 5\nvalue = [5, 0]", fillcolor="#e58139"] ;
|
||||
50 -> 52 ;
|
||||
53 [label="gini = 0.0\nsamples = 257\nvalue = [257, 0]", fillcolor="#e58139"] ;
|
||||
47 -> 53 ;
|
||||
54 [label="bmi <= 46.2\ngini = 0.32\nsamples = 5\nvalue = [4, 1]", fillcolor="#eca06a"] ;
|
||||
46 -> 54 ;
|
||||
55 [label="gini = 0.0\nsamples = 1\nvalue = [0, 1]", fillcolor="#399de5"] ;
|
||||
54 -> 55 ;
|
||||
56 [label="gini = 0.0\nsamples = 4\nvalue = [4, 0]", fillcolor="#e58139"] ;
|
||||
54 -> 56 ;
|
||||
57 [label="avg_glucose_level <= 100.97\ngini = 0.108\nsamples = 836\nvalue = [788, 48]", fillcolor="#e78945"] ;
|
||||
1 -> 57 ;
|
||||
58 [label="bmi <= 25.55\ngini = 0.068\nsamples = 482\nvalue = [465, 17]", fillcolor="#e68640"] ;
|
||||
57 -> 58 ;
|
||||
59 [label="gini = 0.0\nsamples = 81\nvalue = [81, 0]", fillcolor="#e58139"] ;
|
||||
58 -> 59 ;
|
||||
60 [label="bmi <= 30.4\ngini = 0.081\nsamples = 401\nvalue = [384, 17]", fillcolor="#e68742"] ;
|
||||
58 -> 60 ;
|
||||
61 [label="bmi <= 29.95\ngini = 0.121\nsamples = 185\nvalue = [173, 12]", fillcolor="#e78a47"] ;
|
||||
60 -> 61 ;
|
||||
62 [label="bmi <= 26.45\ngini = 0.071\nsamples = 162\nvalue = [156, 6]", fillcolor="#e68641"] ;
|
||||
61 -> 62 ;
|
||||
63 [label="avg_glucose_level <= 92.03\ngini = 0.204\nsamples = 26\nvalue = [23, 3]", fillcolor="#e89153"] ;
|
||||
62 -> 63 ;
|
||||
64 [label="gini = 0.0\nsamples = 21\nvalue = [21, 0]", fillcolor="#e58139"] ;
|
||||
63 -> 64 ;
|
||||
65 [label="bmi <= 26.25\ngini = 0.48\nsamples = 5\nvalue = [2, 3]", fillcolor="#bddef6"] ;
|
||||
63 -> 65 ;
|
||||
66 [label="work_type_Self-employed <= 0.5\ngini = 0.444\nsamples = 3\nvalue = [2, 1]", fillcolor="#f2c09c"] ;
|
||||
65 -> 66 ;
|
||||
67 [label="gini = 0.0\nsamples = 2\nvalue = [2, 0]", fillcolor="#e58139"] ;
|
||||
66 -> 67 ;
|
||||
68 [label="gini = 0.0\nsamples = 1\nvalue = [0, 1]", fillcolor="#399de5"] ;
|
||||
66 -> 68 ;
|
||||
69 [label="gini = 0.0\nsamples = 2\nvalue = [0, 2]", fillcolor="#399de5"] ;
|
||||
65 -> 69 ;
|
||||
70 [label="work_type_Govt_job <= 0.5\ngini = 0.043\nsamples = 136\nvalue = [133, 3]", fillcolor="#e6843d"] ;
|
||||
62 -> 70 ;
|
||||
71 [label="age <= 54.5\ngini = 0.017\nsamples = 120\nvalue = [119, 1]", fillcolor="#e5823b"] ;
|
||||
70 -> 71 ;
|
||||
72 [label="age <= 53.5\ngini = 0.048\nsamples = 41\nvalue = [40, 1]", fillcolor="#e6843e"] ;
|
||||
71 -> 72 ;
|
||||
73 [label="gini = 0.0\nsamples = 32\nvalue = [32, 0]", fillcolor="#e58139"] ;
|
||||
72 -> 73 ;
|
||||
74 [label="gender <= 0.5\ngini = 0.198\nsamples = 9\nvalue = [8, 1]", fillcolor="#e89152"] ;
|
||||
72 -> 74 ;
|
||||
75 [label="avg_glucose_level <= 64.39\ngini = 0.444\nsamples = 3\nvalue = [2, 1]", fillcolor="#f2c09c"] ;
|
||||
74 -> 75 ;
|
||||
76 [label="gini = 0.0\nsamples = 1\nvalue = [1, 0]", fillcolor="#e58139"] ;
|
||||
75 -> 76 ;
|
||||
77 [label="bmi <= 28.95\ngini = 0.5\nsamples = 2\nvalue = [1, 1]", fillcolor="#ffffff"] ;
|
||||
75 -> 77 ;
|
||||
78 [label="gini = 0.0\nsamples = 1\nvalue = [0, 1]", fillcolor="#399de5"] ;
|
||||
77 -> 78 ;
|
||||
79 [label="gini = 0.0\nsamples = 1\nvalue = [1, 0]", fillcolor="#e58139"] ;
|
||||
77 -> 79 ;
|
||||
80 [label="gini = 0.0\nsamples = 6\nvalue = [6, 0]", fillcolor="#e58139"] ;
|
||||
74 -> 80 ;
|
||||
81 [label="gini = 0.0\nsamples = 79\nvalue = [79, 0]", fillcolor="#e58139"] ;
|
||||
71 -> 81 ;
|
||||
82 [label="heart_disease <= 0.5\ngini = 0.219\nsamples = 16\nvalue = [14, 2]", fillcolor="#e99355"] ;
|
||||
70 -> 82 ;
|
||||
83 [label="hypertension <= 0.5\ngini = 0.124\nsamples = 15\nvalue = [14, 1]", fillcolor="#e78a47"] ;
|
||||
82 -> 83 ;
|
||||
84 [label="gini = 0.0\nsamples = 14\nvalue = [14, 0]", fillcolor="#e58139"] ;
|
||||
83 -> 84 ;
|
||||
85 [label="gini = 0.0\nsamples = 1\nvalue = [0, 1]", fillcolor="#399de5"] ;
|
||||
83 -> 85 ;
|
||||
86 [label="gini = 0.0\nsamples = 1\nvalue = [0, 1]", fillcolor="#399de5"] ;
|
||||
82 -> 86 ;
|
||||
87 [label="Residence_type_Rural <= 0.5\ngini = 0.386\nsamples = 23\nvalue = [17, 6]", fillcolor="#eead7f"] ;
|
||||
61 -> 87 ;
|
||||
88 [label="age <= 63.5\ngini = 0.469\nsamples = 8\nvalue = [3, 5]", fillcolor="#b0d8f5"] ;
|
||||
87 -> 88 ;
|
||||
89 [label="work_type_Govt_job <= 0.5\ngini = 0.278\nsamples = 6\nvalue = [1, 5]", fillcolor="#61b1ea"] ;
|
||||
88 -> 89 ;
|
||||
90 [label="gini = 0.0\nsamples = 5\nvalue = [0, 5]", fillcolor="#399de5"] ;
|
||||
89 -> 90 ;
|
||||
91 [label="gini = 0.0\nsamples = 1\nvalue = [1, 0]", fillcolor="#e58139"] ;
|
||||
89 -> 91 ;
|
||||
92 [label="gini = 0.0\nsamples = 2\nvalue = [2, 0]", fillcolor="#e58139"] ;
|
||||
88 -> 92 ;
|
||||
93 [label="age <= 50.5\ngini = 0.124\nsamples = 15\nvalue = [14, 1]", fillcolor="#e78a47"] ;
|
||||
87 -> 93 ;
|
||||
94 [label="gini = 0.0\nsamples = 1\nvalue = [0, 1]", fillcolor="#399de5"] ;
|
||||
93 -> 94 ;
|
||||
95 [label="gini = 0.0\nsamples = 14\nvalue = [14, 0]", fillcolor="#e58139"] ;
|
||||
93 -> 95 ;
|
||||
96 [label="ever_married <= 0.5\ngini = 0.045\nsamples = 216\nvalue = [211, 5]", fillcolor="#e6843e"] ;
|
||||
60 -> 96 ;
|
||||
97 [label="bmi <= 37.2\ngini = 0.208\nsamples = 17\nvalue = [15, 2]", fillcolor="#e89253"] ;
|
||||
96 -> 97 ;
|
||||
98 [label="gini = 0.0\nsamples = 11\nvalue = [11, 0]", fillcolor="#e58139"] ;
|
||||
97 -> 98 ;
|
||||
99 [label="Residence_type_Rural <= 0.5\ngini = 0.444\nsamples = 6\nvalue = [4, 2]", fillcolor="#f2c09c"] ;
|
||||
97 -> 99 ;
|
||||
100 [label="gini = 0.0\nsamples = 2\nvalue = [0, 2]", fillcolor="#399de5"] ;
|
||||
99 -> 100 ;
|
||||
101 [label="gini = 0.0\nsamples = 4\nvalue = [4, 0]", fillcolor="#e58139"] ;
|
||||
99 -> 101 ;
|
||||
102 [label="work_type_Govt_job <= 0.5\ngini = 0.03\nsamples = 199\nvalue = [196, 3]", fillcolor="#e5833c"] ;
|
||||
96 -> 102 ;
|
||||
103 [label="smoking_status_Unknown <= 0.5\ngini = 0.012\nsamples = 163\nvalue = [162, 1]", fillcolor="#e5823a"] ;
|
||||
102 -> 103 ;
|
||||
104 [label="gini = 0.0\nsamples = 132\nvalue = [132, 0]", fillcolor="#e58139"] ;
|
||||
103 -> 104 ;
|
||||
105 [label="bmi <= 36.65\ngini = 0.062\nsamples = 31\nvalue = [30, 1]", fillcolor="#e68540"] ;
|
||||
103 -> 105 ;
|
||||
106 [label="gini = 0.0\nsamples = 27\nvalue = [27, 0]", fillcolor="#e58139"] ;
|
||||
105 -> 106 ;
|
||||
107 [label="bmi <= 37.85\ngini = 0.375\nsamples = 4\nvalue = [3, 1]", fillcolor="#eeab7b"] ;
|
||||
105 -> 107 ;
|
||||
108 [label="gini = 0.0\nsamples = 1\nvalue = [0, 1]", fillcolor="#399de5"] ;
|
||||
107 -> 108 ;
|
||||
109 [label="gini = 0.0\nsamples = 3\nvalue = [3, 0]", fillcolor="#e58139"] ;
|
||||
107 -> 109 ;
|
||||
110 [label="avg_glucose_level <= 68.26\ngini = 0.105\nsamples = 36\nvalue = [34, 2]", fillcolor="#e78845"] ;
|
||||
102 -> 110 ;
|
||||
111 [label="avg_glucose_level <= 63.415\ngini = 0.48\nsamples = 5\nvalue = [3, 2]", fillcolor="#f6d5bd"] ;
|
||||
110 -> 111 ;
|
||||
112 [label="gini = 0.0\nsamples = 2\nvalue = [2, 0]", fillcolor="#e58139"] ;
|
||||
111 -> 112 ;
|
||||
113 [label="bmi <= 43.85\ngini = 0.444\nsamples = 3\nvalue = [1, 2]", fillcolor="#9ccef2"] ;
|
||||
111 -> 113 ;
|
||||
114 [label="gini = 0.0\nsamples = 2\nvalue = [0, 2]", fillcolor="#399de5"] ;
|
||||
113 -> 114 ;
|
||||
115 [label="gini = 0.0\nsamples = 1\nvalue = [1, 0]", fillcolor="#e58139"] ;
|
||||
113 -> 115 ;
|
||||
116 [label="gini = 0.0\nsamples = 31\nvalue = [31, 0]", fillcolor="#e58139"] ;
|
||||
110 -> 116 ;
|
||||
117 [label="avg_glucose_level <= 101.015\ngini = 0.16\nsamples = 354\nvalue = [323, 31]", fillcolor="#e78d4c"] ;
|
||||
57 -> 117 ;
|
||||
118 [label="gini = 0.0\nsamples = 1\nvalue = [0, 1]", fillcolor="#399de5"] ;
|
||||
117 -> 118 ;
|
||||
119 [label="smoking_status_never smoked <= 0.5\ngini = 0.156\nsamples = 353\nvalue = [323, 30]", fillcolor="#e78d4b"] ;
|
||||
117 -> 119 ;
|
||||
120 [label="bmi <= 45.85\ngini = 0.213\nsamples = 214\nvalue = [188, 26]", fillcolor="#e99254"] ;
|
||||
119 -> 120 ;
|
||||
121 [label="avg_glucose_level <= 222.245\ngini = 0.207\nsamples = 213\nvalue = [188, 25]", fillcolor="#e89253"] ;
|
||||
120 -> 121 ;
|
||||
122 [label="avg_glucose_level <= 221.86\ngini = 0.236\nsamples = 183\nvalue = [158, 25]", fillcolor="#e99558"] ;
|
||||
121 -> 122 ;
|
||||
123 [label="age <= 55.5\ngini = 0.229\nsamples = 182\nvalue = [158, 24]", fillcolor="#e99457"] ;
|
||||
122 -> 123 ;
|
||||
124 [label="avg_glucose_level <= 105.11\ngini = 0.145\nsamples = 89\nvalue = [82, 7]", fillcolor="#e78c4a"] ;
|
||||
123 -> 124 ;
|
||||
125 [label="avg_glucose_level <= 104.375\ngini = 0.426\nsamples = 13\nvalue = [9, 4]", fillcolor="#f1b991"] ;
|
||||
124 -> 125 ;
|
||||
126 [label="age <= 51.5\ngini = 0.298\nsamples = 11\nvalue = [9, 2]", fillcolor="#eb9d65"] ;
|
||||
125 -> 126 ;
|
||||
127 [label="Residence_type_Urban <= 0.5\ngini = 0.48\nsamples = 5\nvalue = [3, 2]", fillcolor="#f6d5bd"] ;
|
||||
126 -> 127 ;
|
||||
128 [label="gender <= 0.5\ngini = 0.444\nsamples = 3\nvalue = [1, 2]", fillcolor="#9ccef2"] ;
|
||||
127 -> 128 ;
|
||||
129 [label="gini = 0.0\nsamples = 1\nvalue = [1, 0]", fillcolor="#e58139"] ;
|
||||
128 -> 129 ;
|
||||
130 [label="gini = 0.0\nsamples = 2\nvalue = [0, 2]", fillcolor="#399de5"] ;
|
||||
128 -> 130 ;
|
||||
131 [label="gini = 0.0\nsamples = 2\nvalue = [2, 0]", fillcolor="#e58139"] ;
|
||||
127 -> 131 ;
|
||||
132 [label="gini = 0.0\nsamples = 6\nvalue = [6, 0]", fillcolor="#e58139"] ;
|
||||
126 -> 132 ;
|
||||
133 [label="gini = 0.0\nsamples = 2\nvalue = [0, 2]", fillcolor="#399de5"] ;
|
||||
125 -> 133 ;
|
||||
134 [label="bmi <= 40.3\ngini = 0.076\nsamples = 76\nvalue = [73, 3]", fillcolor="#e68641"] ;
|
||||
124 -> 134 ;
|
||||
135 [label="age <= 49.5\ngini = 0.031\nsamples = 64\nvalue = [63, 1]", fillcolor="#e5833c"] ;
|
||||
134 -> 135 ;
|
||||
136 [label="avg_glucose_level <= 155.33\ngini = 0.219\nsamples = 8\nvalue = [7, 1]", fillcolor="#e99355"] ;
|
||||
135 -> 136 ;
|
||||
137 [label="gini = 0.0\nsamples = 7\nvalue = [7, 0]", fillcolor="#e58139"] ;
|
||||
136 -> 137 ;
|
||||
138 [label="gini = 0.0\nsamples = 1\nvalue = [0, 1]", fillcolor="#399de5"] ;
|
||||
136 -> 138 ;
|
||||
139 [label="gini = 0.0\nsamples = 56\nvalue = [56, 0]", fillcolor="#e58139"] ;
|
||||
135 -> 139 ;
|
||||
140 [label="bmi <= 42.7\ngini = 0.278\nsamples = 12\nvalue = [10, 2]", fillcolor="#ea9a61"] ;
|
||||
134 -> 140 ;
|
||||
141 [label="smoking_status_smokes <= 0.5\ngini = 0.444\nsamples = 6\nvalue = [4, 2]", fillcolor="#f2c09c"] ;
|
||||
140 -> 141 ;
|
||||
142 [label="bmi <= 42.3\ngini = 0.32\nsamples = 5\nvalue = [4, 1]", fillcolor="#eca06a"] ;
|
||||
141 -> 142 ;
|
||||
143 [label="gini = 0.0\nsamples = 4\nvalue = [4, 0]", fillcolor="#e58139"] ;
|
||||
142 -> 143 ;
|
||||
144 [label="gini = 0.0\nsamples = 1\nvalue = [0, 1]", fillcolor="#399de5"] ;
|
||||
142 -> 144 ;
|
||||
145 [label="gini = 0.0\nsamples = 1\nvalue = [0, 1]", fillcolor="#399de5"] ;
|
||||
141 -> 145 ;
|
||||
146 [label="gini = 0.0\nsamples = 6\nvalue = [6, 0]", fillcolor="#e58139"] ;
|
||||
140 -> 146 ;
|
||||
147 [label="age <= 59.5\ngini = 0.299\nsamples = 93\nvalue = [76, 17]", fillcolor="#eb9d65"] ;
|
||||
123 -> 147 ;
|
||||
148 [label="work_type_Private <= 0.5\ngini = 0.42\nsamples = 40\nvalue = [28, 12]", fillcolor="#f0b78e"] ;
|
||||
147 -> 148 ;
|
||||
149 [label="bmi <= 29.4\ngini = 0.208\nsamples = 17\nvalue = [15, 2]", fillcolor="#e89253"] ;
|
||||
148 -> 149 ;
|
||||
150 [label="bmi <= 26.5\ngini = 0.5\nsamples = 4\nvalue = [2, 2]", fillcolor="#ffffff"] ;
|
||||
149 -> 150 ;
|
||||
151 [label="gini = 0.0\nsamples = 2\nvalue = [2, 0]", fillcolor="#e58139"] ;
|
||||
150 -> 151 ;
|
||||
152 [label="gini = 0.0\nsamples = 2\nvalue = [0, 2]", fillcolor="#399de5"] ;
|
||||
150 -> 152 ;
|
||||
153 [label="gini = 0.0\nsamples = 13\nvalue = [13, 0]", fillcolor="#e58139"] ;
|
||||
149 -> 153 ;
|
||||
154 [label="bmi <= 30.85\ngini = 0.491\nsamples = 23\nvalue = [13, 10]", fillcolor="#f9e2d1"] ;
|
||||
148 -> 154 ;
|
||||
155 [label="bmi <= 24.55\ngini = 0.165\nsamples = 11\nvalue = [10, 1]", fillcolor="#e88e4d"] ;
|
||||
154 -> 155 ;
|
||||
156 [label="age <= 58.0\ngini = 0.5\nsamples = 2\nvalue = [1, 1]", fillcolor="#ffffff"] ;
|
||||
155 -> 156 ;
|
||||
157 [label="gini = 0.0\nsamples = 1\nvalue = [1, 0]", fillcolor="#e58139"] ;
|
||||
156 -> 157 ;
|
||||
158 [label="gini = 0.0\nsamples = 1\nvalue = [0, 1]", fillcolor="#399de5"] ;
|
||||
156 -> 158 ;
|
||||
159 [label="gini = 0.0\nsamples = 9\nvalue = [9, 0]", fillcolor="#e58139"] ;
|
||||
155 -> 159 ;
|
||||
160 [label="bmi <= 35.15\ngini = 0.375\nsamples = 12\nvalue = [3, 9]", fillcolor="#7bbeee"] ;
|
||||
154 -> 160 ;
|
||||
161 [label="gini = 0.0\nsamples = 5\nvalue = [0, 5]", fillcolor="#399de5"] ;
|
||||
160 -> 161 ;
|
||||
162 [label="avg_glucose_level <= 198.86\ngini = 0.49\nsamples = 7\nvalue = [3, 4]", fillcolor="#cee6f8"] ;
|
||||
160 -> 162 ;
|
||||
163 [label="gender <= 0.5\ngini = 0.48\nsamples = 5\nvalue = [3, 2]", fillcolor="#f6d5bd"] ;
|
||||
162 -> 163 ;
|
||||
164 [label="gini = 0.0\nsamples = 2\nvalue = [2, 0]", fillcolor="#e58139"] ;
|
||||
163 -> 164 ;
|
||||
165 [label="smoking_status_smokes <= 0.5\ngini = 0.444\nsamples = 3\nvalue = [1, 2]", fillcolor="#9ccef2"] ;
|
||||
163 -> 165 ;
|
||||
166 [label="gini = 0.0\nsamples = 2\nvalue = [0, 2]", fillcolor="#399de5"] ;
|
||||
165 -> 166 ;
|
||||
167 [label="gini = 0.0\nsamples = 1\nvalue = [1, 0]", fillcolor="#e58139"] ;
|
||||
165 -> 167 ;
|
||||
168 [label="gini = 0.0\nsamples = 2\nvalue = [0, 2]", fillcolor="#399de5"] ;
|
||||
162 -> 168 ;
|
||||
169 [label="bmi <= 21.2\ngini = 0.171\nsamples = 53\nvalue = [48, 5]", fillcolor="#e88e4e"] ;
|
||||
147 -> 169 ;
|
||||
170 [label="gini = 0.0\nsamples = 1\nvalue = [0, 1]", fillcolor="#399de5"] ;
|
||||
169 -> 170 ;
|
||||
171 [label="heart_disease <= 0.5\ngini = 0.142\nsamples = 52\nvalue = [48, 4]", fillcolor="#e78c49"] ;
|
||||
169 -> 171 ;
|
||||
172 [label="age <= 62.5\ngini = 0.083\nsamples = 46\nvalue = [44, 2]", fillcolor="#e68742"] ;
|
||||
171 -> 172 ;
|
||||
173 [label="gini = 0.0\nsamples = 32\nvalue = [32, 0]", fillcolor="#e58139"] ;
|
||||
172 -> 173 ;
|
||||
174 [label="bmi <= 24.0\ngini = 0.245\nsamples = 14\nvalue = [12, 2]", fillcolor="#e9965a"] ;
|
||||
172 -> 174 ;
|
||||
175 [label="gini = 0.0\nsamples = 1\nvalue = [0, 1]", fillcolor="#399de5"] ;
|
||||
174 -> 175 ;
|
||||
176 [label="work_type_Self-employed <= 0.5\ngini = 0.142\nsamples = 13\nvalue = [12, 1]", fillcolor="#e78c49"] ;
|
||||
174 -> 176 ;
|
||||
177 [label="gini = 0.0\nsamples = 9\nvalue = [9, 0]", fillcolor="#e58139"] ;
|
||||
176 -> 177 ;
|
||||
178 [label="bmi <= 30.25\ngini = 0.375\nsamples = 4\nvalue = [3, 1]", fillcolor="#eeab7b"] ;
|
||||
176 -> 178 ;
|
||||
179 [label="smoking_status_Unknown <= 0.5\ngini = 0.5\nsamples = 2\nvalue = [1, 1]", fillcolor="#ffffff"] ;
|
||||
178 -> 179 ;
|
||||
180 [label="gini = 0.0\nsamples = 1\nvalue = [0, 1]", fillcolor="#399de5"] ;
|
||||
179 -> 180 ;
|
||||
181 [label="gini = 0.0\nsamples = 1\nvalue = [1, 0]", fillcolor="#e58139"] ;
|
||||
179 -> 181 ;
|
||||
182 [label="gini = 0.0\nsamples = 2\nvalue = [2, 0]", fillcolor="#e58139"] ;
|
||||
178 -> 182 ;
|
||||
183 [label="smoking_status_smokes <= 0.5\ngini = 0.444\nsamples = 6\nvalue = [4, 2]", fillcolor="#f2c09c"] ;
|
||||
171 -> 183 ;
|
||||
184 [label="gini = 0.0\nsamples = 4\nvalue = [4, 0]", fillcolor="#e58139"] ;
|
||||
183 -> 184 ;
|
||||
185 [label="gini = 0.0\nsamples = 2\nvalue = [0, 2]", fillcolor="#399de5"] ;
|
||||
183 -> 185 ;
|
||||
186 [label="gini = 0.0\nsamples = 1\nvalue = [0, 1]", fillcolor="#399de5"] ;
|
||||
122 -> 186 ;
|
||||
187 [label="gini = 0.0\nsamples = 30\nvalue = [30, 0]", fillcolor="#e58139"] ;
|
||||
121 -> 187 ;
|
||||
188 [label="gini = 0.0\nsamples = 1\nvalue = [0, 1]", fillcolor="#399de5"] ;
|
||||
120 -> 188 ;
|
||||
189 [label="age <= 60.5\ngini = 0.056\nsamples = 139\nvalue = [135, 4]", fillcolor="#e6853f"] ;
|
||||
119 -> 189 ;
|
||||
190 [label="age <= 51.5\ngini = 0.02\nsamples = 100\nvalue = [99, 1]", fillcolor="#e5823b"] ;
|
||||
189 -> 190 ;
|
||||
191 [label="avg_glucose_level <= 158.28\ngini = 0.077\nsamples = 25\nvalue = [24, 1]", fillcolor="#e68641"] ;
|
||||
190 -> 191 ;
|
||||
192 [label="gini = 0.0\nsamples = 18\nvalue = [18, 0]", fillcolor="#e58139"] ;
|
||||
191 -> 192 ;
|
||||
193 [label="avg_glucose_level <= 170.825\ngini = 0.245\nsamples = 7\nvalue = [6, 1]", fillcolor="#e9965a"] ;
|
||||
191 -> 193 ;
|
||||
194 [label="gini = 0.0\nsamples = 1\nvalue = [0, 1]", fillcolor="#399de5"] ;
|
||||
193 -> 194 ;
|
||||
195 [label="gini = 0.0\nsamples = 6\nvalue = [6, 0]", fillcolor="#e58139"] ;
|
||||
193 -> 195 ;
|
||||
196 [label="gini = 0.0\nsamples = 75\nvalue = [75, 0]", fillcolor="#e58139"] ;
|
||||
190 -> 196 ;
|
||||
197 [label="avg_glucose_level <= 228.38\ngini = 0.142\nsamples = 39\nvalue = [36, 3]", fillcolor="#e78c49"] ;
|
||||
189 -> 197 ;
|
||||
198 [label="avg_glucose_level <= 197.175\ngini = 0.1\nsamples = 38\nvalue = [36, 2]", fillcolor="#e68844"] ;
|
||||
197 -> 198 ;
|
||||
199 [label="gini = 0.0\nsamples = 25\nvalue = [25, 0]", fillcolor="#e58139"] ;
|
||||
198 -> 199 ;
|
||||
200 [label="avg_glucose_level <= 202.985\ngini = 0.26\nsamples = 13\nvalue = [11, 2]", fillcolor="#ea985d"] ;
|
||||
198 -> 200 ;
|
||||
201 [label="gender <= 0.5\ngini = 0.444\nsamples = 3\nvalue = [1, 2]", fillcolor="#9ccef2"] ;
|
||||
200 -> 201 ;
|
||||
202 [label="gini = 0.0\nsamples = 1\nvalue = [1, 0]", fillcolor="#e58139"] ;
|
||||
201 -> 202 ;
|
||||
203 [label="gini = 0.0\nsamples = 2\nvalue = [0, 2]", fillcolor="#399de5"] ;
|
||||
201 -> 203 ;
|
||||
204 [label="gini = 0.0\nsamples = 10\nvalue = [10, 0]", fillcolor="#e58139"] ;
|
||||
200 -> 204 ;
|
||||
205 [label="gini = 0.0\nsamples = 1\nvalue = [0, 1]", fillcolor="#399de5"] ;
|
||||
197 -> 205 ;
|
||||
206 [label="bmi <= 29.35\ngini = 0.264\nsamples = 666\nvalue = [562, 104]", fillcolor="#ea985e"] ;
|
||||
0 -> 206 [labeldistance=2.5, labelangle=-45, headlabel="False"] ;
|
||||
207 [label="age <= 73.5\ngini = 0.316\nsamples = 366\nvalue = [294, 72]", fillcolor="#eba069"] ;
|
||||
206 -> 207 ;
|
||||
208 [label="bmi <= 26.65\ngini = 0.229\nsamples = 129\nvalue = [112, 17]", fillcolor="#e99457"] ;
|
||||
207 -> 208 ;
|
||||
209 [label="avg_glucose_level <= 238.14\ngini = 0.15\nsamples = 61\nvalue = [56, 5]", fillcolor="#e78c4b"] ;
|
||||
208 -> 209 ;
|
||||
210 [label="bmi <= 22.85\ngini = 0.128\nsamples = 58\nvalue = [54, 4]", fillcolor="#e78a48"] ;
|
||||
209 -> 210 ;
|
||||
211 [label="ever_married <= 0.5\ngini = 0.255\nsamples = 20\nvalue = [17, 3]", fillcolor="#ea975c"] ;
|
||||
210 -> 211 ;
|
||||
212 [label="gini = 0.0\nsamples = 1\nvalue = [0, 1]", fillcolor="#399de5"] ;
|
||||
211 -> 212 ;
|
||||
213 [label="Residence_type_Rural <= 0.5\ngini = 0.188\nsamples = 19\nvalue = [17, 2]", fillcolor="#e89050"] ;
|
||||
211 -> 213 ;
|
||||
214 [label="gini = 0.0\nsamples = 11\nvalue = [11, 0]", fillcolor="#e58139"] ;
|
||||
213 -> 214 ;
|
||||
215 [label="gender <= 0.5\ngini = 0.375\nsamples = 8\nvalue = [6, 2]", fillcolor="#eeab7b"] ;
|
||||
213 -> 215 ;
|
||||
216 [label="gini = 0.0\nsamples = 1\nvalue = [0, 1]", fillcolor="#399de5"] ;
|
||||
215 -> 216 ;
|
||||
217 [label="avg_glucose_level <= 156.08\ngini = 0.245\nsamples = 7\nvalue = [6, 1]", fillcolor="#e9965a"] ;
|
||||
215 -> 217 ;
|
||||
218 [label="gini = 0.0\nsamples = 5\nvalue = [5, 0]", fillcolor="#e58139"] ;
|
||||
217 -> 218 ;
|
||||
219 [label="work_type_Self-employed <= 0.5\ngini = 0.5\nsamples = 2\nvalue = [1, 1]", fillcolor="#ffffff"] ;
|
||||
217 -> 219 ;
|
||||
220 [label="gini = 0.0\nsamples = 1\nvalue = [0, 1]", fillcolor="#399de5"] ;
|
||||
219 -> 220 ;
|
||||
221 [label="gini = 0.0\nsamples = 1\nvalue = [1, 0]", fillcolor="#e58139"] ;
|
||||
219 -> 221 ;
|
||||
222 [label="avg_glucose_level <= 62.19\ngini = 0.051\nsamples = 38\nvalue = [37, 1]", fillcolor="#e6843e"] ;
|
||||
210 -> 222 ;
|
||||
223 [label="avg_glucose_level <= 61.65\ngini = 0.375\nsamples = 4\nvalue = [3, 1]", fillcolor="#eeab7b"] ;
|
||||
222 -> 223 ;
|
||||
224 [label="gini = 0.0\nsamples = 3\nvalue = [3, 0]", fillcolor="#e58139"] ;
|
||||
223 -> 224 ;
|
||||
225 [label="gini = 0.0\nsamples = 1\nvalue = [0, 1]", fillcolor="#399de5"] ;
|
||||
223 -> 225 ;
|
||||
226 [label="gini = 0.0\nsamples = 34\nvalue = [34, 0]", fillcolor="#e58139"] ;
|
||||
222 -> 226 ;
|
||||
227 [label="avg_glucose_level <= 246.835\ngini = 0.444\nsamples = 3\nvalue = [2, 1]", fillcolor="#f2c09c"] ;
|
||||
209 -> 227 ;
|
||||
228 [label="gini = 0.0\nsamples = 1\nvalue = [0, 1]", fillcolor="#399de5"] ;
|
||||
227 -> 228 ;
|
||||
229 [label="gini = 0.0\nsamples = 2\nvalue = [2, 0]", fillcolor="#e58139"] ;
|
||||
227 -> 229 ;
|
||||
230 [label="avg_glucose_level <= 76.885\ngini = 0.291\nsamples = 68\nvalue = [56, 12]", fillcolor="#eb9c63"] ;
|
||||
208 -> 230 ;
|
||||
231 [label="gini = 0.0\nsamples = 13\nvalue = [13, 0]", fillcolor="#e58139"] ;
|
||||
230 -> 231 ;
|
||||
232 [label="bmi <= 29.25\ngini = 0.341\nsamples = 55\nvalue = [43, 12]", fillcolor="#eca470"] ;
|
||||
230 -> 232 ;
|
||||
233 [label="avg_glucose_level <= 78.755\ngini = 0.324\nsamples = 54\nvalue = [43, 11]", fillcolor="#eca16c"] ;
|
||||
232 -> 233 ;
|
||||
234 [label="gini = 0.0\nsamples = 1\nvalue = [0, 1]", fillcolor="#399de5"] ;
|
||||
233 -> 234 ;
|
||||
235 [label="bmi <= 28.55\ngini = 0.306\nsamples = 53\nvalue = [43, 10]", fillcolor="#eb9e67"] ;
|
||||
233 -> 235 ;
|
||||
236 [label="gender <= 0.5\ngini = 0.361\nsamples = 38\nvalue = [29, 9]", fillcolor="#eda876"] ;
|
||||
235 -> 236 ;
|
||||
237 [label="smoking_status_never smoked <= 0.5\ngini = 0.236\nsamples = 22\nvalue = [19, 3]", fillcolor="#e99558"] ;
|
||||
236 -> 237 ;
|
||||
238 [label="bmi <= 27.15\ngini = 0.42\nsamples = 10\nvalue = [7, 3]", fillcolor="#f0b78e"] ;
|
||||
237 -> 238 ;
|
||||
239 [label="gini = 0.0\nsamples = 5\nvalue = [5, 0]", fillcolor="#e58139"] ;
|
||||
238 -> 239 ;
|
||||
240 [label="avg_glucose_level <= 90.19\ngini = 0.48\nsamples = 5\nvalue = [2, 3]", fillcolor="#bddef6"] ;
|
||||
238 -> 240 ;
|
||||
241 [label="gini = 0.0\nsamples = 2\nvalue = [2, 0]", fillcolor="#e58139"] ;
|
||||
240 -> 241 ;
|
||||
242 [label="gini = 0.0\nsamples = 3\nvalue = [0, 3]", fillcolor="#399de5"] ;
|
||||
240 -> 242 ;
|
||||
243 [label="gini = 0.0\nsamples = 12\nvalue = [12, 0]", fillcolor="#e58139"] ;
|
||||
237 -> 243 ;
|
||||
244 [label="avg_glucose_level <= 83.18\ngini = 0.469\nsamples = 16\nvalue = [10, 6]", fillcolor="#f5cdb0"] ;
|
||||
236 -> 244 ;
|
||||
245 [label="gini = 0.0\nsamples = 2\nvalue = [0, 2]", fillcolor="#399de5"] ;
|
||||
244 -> 245 ;
|
||||
246 [label="bmi <= 26.95\ngini = 0.408\nsamples = 14\nvalue = [10, 4]", fillcolor="#efb388"] ;
|
||||
244 -> 246 ;
|
||||
247 [label="gini = 0.0\nsamples = 2\nvalue = [0, 2]", fillcolor="#399de5"] ;
|
||||
246 -> 247 ;
|
||||
248 [label="age <= 71.5\ngini = 0.278\nsamples = 12\nvalue = [10, 2]", fillcolor="#ea9a61"] ;
|
||||
246 -> 248 ;
|
||||
249 [label="hypertension <= 0.5\ngini = 0.165\nsamples = 11\nvalue = [10, 1]", fillcolor="#e88e4d"] ;
|
||||
248 -> 249 ;
|
||||
250 [label="gini = 0.0\nsamples = 9\nvalue = [9, 0]", fillcolor="#e58139"] ;
|
||||
249 -> 250 ;
|
||||
251 [label="avg_glucose_level <= 166.86\ngini = 0.5\nsamples = 2\nvalue = [1, 1]", fillcolor="#ffffff"] ;
|
||||
249 -> 251 ;
|
||||
252 [label="gini = 0.0\nsamples = 1\nvalue = [1, 0]", fillcolor="#e58139"] ;
|
||||
251 -> 252 ;
|
||||
253 [label="gini = 0.0\nsamples = 1\nvalue = [0, 1]", fillcolor="#399de5"] ;
|
||||
251 -> 253 ;
|
||||
254 [label="gini = 0.0\nsamples = 1\nvalue = [0, 1]", fillcolor="#399de5"] ;
|
||||
248 -> 254 ;
|
||||
255 [label="smoking_status_smokes <= 0.5\ngini = 0.124\nsamples = 15\nvalue = [14, 1]", fillcolor="#e78a47"] ;
|
||||
235 -> 255 ;
|
||||
256 [label="gini = 0.0\nsamples = 13\nvalue = [13, 0]", fillcolor="#e58139"] ;
|
||||
255 -> 256 ;
|
||||
257 [label="avg_glucose_level <= 82.305\ngini = 0.5\nsamples = 2\nvalue = [1, 1]", fillcolor="#ffffff"] ;
|
||||
255 -> 257 ;
|
||||
258 [label="gini = 0.0\nsamples = 1\nvalue = [0, 1]", fillcolor="#399de5"] ;
|
||||
257 -> 258 ;
|
||||
259 [label="gini = 0.0\nsamples = 1\nvalue = [1, 0]", fillcolor="#e58139"] ;
|
||||
257 -> 259 ;
|
||||
260 [label="gini = 0.0\nsamples = 1\nvalue = [0, 1]", fillcolor="#399de5"] ;
|
||||
232 -> 260 ;
|
||||
261 [label="work_type_Private <= 0.5\ngini = 0.356\nsamples = 237\nvalue = [182, 55]", fillcolor="#eda775"] ;
|
||||
207 -> 261 ;
|
||||
262 [label="bmi <= 17.6\ngini = 0.296\nsamples = 133\nvalue = [109, 24]", fillcolor="#eb9d65"] ;
|
||||
261 -> 262 ;
|
||||
263 [label="gini = 0.0\nsamples = 1\nvalue = [0, 1]", fillcolor="#399de5"] ;
|
||||
262 -> 263 ;
|
||||
264 [label="bmi <= 29.25\ngini = 0.288\nsamples = 132\nvalue = [109, 23]", fillcolor="#ea9c63"] ;
|
||||
262 -> 264 ;
|
||||
265 [label="bmi <= 26.15\ngini = 0.279\nsamples = 131\nvalue = [109, 22]", fillcolor="#ea9a61"] ;
|
||||
264 -> 265 ;
|
||||
266 [label="age <= 79.5\ngini = 0.187\nsamples = 67\nvalue = [60, 7]", fillcolor="#e89050"] ;
|
||||
265 -> 266 ;
|
||||
267 [label="avg_glucose_level <= 132.52\ngini = 0.089\nsamples = 43\nvalue = [41, 2]", fillcolor="#e68743"] ;
|
||||
266 -> 267 ;
|
||||
268 [label="gini = 0.0\nsamples = 32\nvalue = [32, 0]", fillcolor="#e58139"] ;
|
||||
267 -> 268 ;
|
||||
269 [label="avg_glucose_level <= 182.725\ngini = 0.298\nsamples = 11\nvalue = [9, 2]", fillcolor="#eb9d65"] ;
|
||||
267 -> 269 ;
|
||||
270 [label="heart_disease <= 0.5\ngini = 0.444\nsamples = 3\nvalue = [1, 2]", fillcolor="#9ccef2"] ;
|
||||
269 -> 270 ;
|
||||
271 [label="gini = 0.0\nsamples = 2\nvalue = [0, 2]", fillcolor="#399de5"] ;
|
||||
270 -> 271 ;
|
||||
272 [label="gini = 0.0\nsamples = 1\nvalue = [1, 0]", fillcolor="#e58139"] ;
|
||||
270 -> 272 ;
|
||||
273 [label="gini = 0.0\nsamples = 8\nvalue = [8, 0]", fillcolor="#e58139"] ;
|
||||
269 -> 273 ;
|
||||
274 [label="smoking_status_never smoked <= 0.5\ngini = 0.33\nsamples = 24\nvalue = [19, 5]", fillcolor="#eca26d"] ;
|
||||
266 -> 274 ;
|
||||
275 [label="bmi <= 19.6\ngini = 0.153\nsamples = 12\nvalue = [11, 1]", fillcolor="#e78c4b"] ;
|
||||
274 -> 275 ;
|
||||
276 [label="smoking_status_Unknown <= 0.5\ngini = 0.5\nsamples = 2\nvalue = [1, 1]", fillcolor="#ffffff"] ;
|
||||
275 -> 276 ;
|
||||
277 [label="gini = 0.0\nsamples = 1\nvalue = [1, 0]", fillcolor="#e58139"] ;
|
||||
276 -> 277 ;
|
||||
278 [label="gini = 0.0\nsamples = 1\nvalue = [0, 1]", fillcolor="#399de5"] ;
|
||||
276 -> 278 ;
|
||||
279 [label="gini = 0.0\nsamples = 10\nvalue = [10, 0]", fillcolor="#e58139"] ;
|
||||
275 -> 279 ;
|
||||
280 [label="gender <= 0.5\ngini = 0.444\nsamples = 12\nvalue = [8, 4]", fillcolor="#f2c09c"] ;
|
||||
274 -> 280 ;
|
||||
281 [label="avg_glucose_level <= 80.215\ngini = 0.444\nsamples = 3\nvalue = [1, 2]", fillcolor="#9ccef2"] ;
|
||||
280 -> 281 ;
|
||||
282 [label="gini = 0.0\nsamples = 1\nvalue = [1, 0]", fillcolor="#e58139"] ;
|
||||
281 -> 282 ;
|
||||
283 [label="gini = 0.0\nsamples = 2\nvalue = [0, 2]", fillcolor="#399de5"] ;
|
||||
281 -> 283 ;
|
||||
284 [label="avg_glucose_level <= 99.72\ngini = 0.346\nsamples = 9\nvalue = [7, 2]", fillcolor="#eca572"] ;
|
||||
280 -> 284 ;
|
||||
285 [label="avg_glucose_level <= 89.025\ngini = 0.444\nsamples = 6\nvalue = [4, 2]", fillcolor="#f2c09c"] ;
|
||||
284 -> 285 ;
|
||||
286 [label="hypertension <= 0.5\ngini = 0.32\nsamples = 5\nvalue = [4, 1]", fillcolor="#eca06a"] ;
|
||||
285 -> 286 ;
|
||||
287 [label="gini = 0.0\nsamples = 4\nvalue = [4, 0]", fillcolor="#e58139"] ;
|
||||
286 -> 287 ;
|
||||
288 [label="gini = 0.0\nsamples = 1\nvalue = [0, 1]", fillcolor="#399de5"] ;
|
||||
286 -> 288 ;
|
||||
289 [label="gini = 0.0\nsamples = 1\nvalue = [0, 1]", fillcolor="#399de5"] ;
|
||||
285 -> 289 ;
|
||||
290 [label="gini = 0.0\nsamples = 3\nvalue = [3, 0]", fillcolor="#e58139"] ;
|
||||
284 -> 290 ;
|
||||
291 [label="bmi <= 27.15\ngini = 0.359\nsamples = 64\nvalue = [49, 15]", fillcolor="#eda876"] ;
|
||||
265 -> 291 ;
|
||||
292 [label="age <= 80.5\ngini = 0.488\nsamples = 19\nvalue = [11, 8]", fillcolor="#f8ddc9"] ;
|
||||
291 -> 292 ;
|
||||
293 [label="avg_glucose_level <= 79.015\ngini = 0.498\nsamples = 15\nvalue = [7, 8]", fillcolor="#e6f3fc"] ;
|
||||
292 -> 293 ;
|
||||
294 [label="gini = 0.0\nsamples = 3\nvalue = [0, 3]", fillcolor="#399de5"] ;
|
||||
293 -> 294 ;
|
||||
295 [label="Residence_type_Urban <= 0.5\ngini = 0.486\nsamples = 12\nvalue = [7, 5]", fillcolor="#f8dbc6"] ;
|
||||
293 -> 295 ;
|
||||
296 [label="hypertension <= 0.5\ngini = 0.278\nsamples = 6\nvalue = [5, 1]", fillcolor="#ea9a61"] ;
|
||||
295 -> 296 ;
|
||||
297 [label="gini = 0.0\nsamples = 5\nvalue = [5, 0]", fillcolor="#e58139"] ;
|
||||
296 -> 297 ;
|
||||
298 [label="gini = 0.0\nsamples = 1\nvalue = [0, 1]", fillcolor="#399de5"] ;
|
||||
296 -> 298 ;
|
||||
299 [label="smoking_status_smokes <= 0.5\ngini = 0.444\nsamples = 6\nvalue = [2, 4]", fillcolor="#9ccef2"] ;
|
||||
295 -> 299 ;
|
||||
300 [label="gini = 0.0\nsamples = 4\nvalue = [0, 4]", fillcolor="#399de5"] ;
|
||||
299 -> 300 ;
|
||||
301 [label="gini = 0.0\nsamples = 2\nvalue = [2, 0]", fillcolor="#e58139"] ;
|
||||
299 -> 301 ;
|
||||
302 [label="gini = 0.0\nsamples = 4\nvalue = [4, 0]", fillcolor="#e58139"] ;
|
||||
292 -> 302 ;
|
||||
303 [label="avg_glucose_level <= 62.88\ngini = 0.263\nsamples = 45\nvalue = [38, 7]", fillcolor="#ea985d"] ;
|
||||
291 -> 303 ;
|
||||
304 [label="age <= 76.5\ngini = 0.5\nsamples = 6\nvalue = [3, 3]", fillcolor="#ffffff"] ;
|
||||
303 -> 304 ;
|
||||
305 [label="gini = 0.0\nsamples = 2\nvalue = [0, 2]", fillcolor="#399de5"] ;
|
||||
304 -> 305 ;
|
||||
306 [label="heart_disease <= 0.5\ngini = 0.375\nsamples = 4\nvalue = [3, 1]", fillcolor="#eeab7b"] ;
|
||||
304 -> 306 ;
|
||||
307 [label="gini = 0.0\nsamples = 3\nvalue = [3, 0]", fillcolor="#e58139"] ;
|
||||
306 -> 307 ;
|
||||
308 [label="gini = 0.0\nsamples = 1\nvalue = [0, 1]", fillcolor="#399de5"] ;
|
||||
306 -> 308 ;
|
||||
309 [label="avg_glucose_level <= 92.78\ngini = 0.184\nsamples = 39\nvalue = [35, 4]", fillcolor="#e88f50"] ;
|
||||
303 -> 309 ;
|
||||
310 [label="gini = 0.0\nsamples = 19\nvalue = [19, 0]", fillcolor="#e58139"] ;
|
||||
309 -> 310 ;
|
||||
311 [label="bmi <= 29.15\ngini = 0.32\nsamples = 20\nvalue = [16, 4]", fillcolor="#eca06a"] ;
|
||||
309 -> 311 ;
|
||||
312 [label="gender <= 0.5\ngini = 0.266\nsamples = 19\nvalue = [16, 3]", fillcolor="#ea995e"] ;
|
||||
311 -> 312 ;
|
||||
313 [label="gini = 0.0\nsamples = 10\nvalue = [10, 0]", fillcolor="#e58139"] ;
|
||||
312 -> 313 ;
|
||||
314 [label="smoking_status_formerly smoked <= 0.5\ngini = 0.444\nsamples = 9\nvalue = [6, 3]", fillcolor="#f2c09c"] ;
|
||||
312 -> 314 ;
|
||||
315 [label="age <= 75.5\ngini = 0.245\nsamples = 7\nvalue = [6, 1]", fillcolor="#e9965a"] ;
|
||||
314 -> 315 ;
|
||||
316 [label="heart_disease <= 0.5\ngini = 0.5\nsamples = 2\nvalue = [1, 1]", fillcolor="#ffffff"] ;
|
||||
315 -> 316 ;
|
||||
317 [label="gini = 0.0\nsamples = 1\nvalue = [1, 0]", fillcolor="#e58139"] ;
|
||||
316 -> 317 ;
|
||||
318 [label="gini = 0.0\nsamples = 1\nvalue = [0, 1]", fillcolor="#399de5"] ;
|
||||
316 -> 318 ;
|
||||
319 [label="gini = 0.0\nsamples = 5\nvalue = [5, 0]", fillcolor="#e58139"] ;
|
||||
315 -> 319 ;
|
||||
320 [label="gini = 0.0\nsamples = 2\nvalue = [0, 2]", fillcolor="#399de5"] ;
|
||||
314 -> 320 ;
|
||||
321 [label="gini = 0.0\nsamples = 1\nvalue = [0, 1]", fillcolor="#399de5"] ;
|
||||
311 -> 321 ;
|
||||
322 [label="gini = 0.0\nsamples = 1\nvalue = [0, 1]", fillcolor="#399de5"] ;
|
||||
264 -> 322 ;
|
||||
323 [label="smoking_status_Unknown <= 0.5\ngini = 0.418\nsamples = 104\nvalue = [73, 31]", fillcolor="#f0b78d"] ;
|
||||
261 -> 323 ;
|
||||
324 [label="hypertension <= 0.5\ngini = 0.378\nsamples = 79\nvalue = [59, 20]", fillcolor="#eeac7c"] ;
|
||||
323 -> 324 ;
|
||||
325 [label="age <= 78.5\ngini = 0.312\nsamples = 62\nvalue = [50, 12]", fillcolor="#eb9f69"] ;
|
||||
324 -> 325 ;
|
||||
326 [label="avg_glucose_level <= 61.6\ngini = 0.137\nsamples = 27\nvalue = [25, 2]", fillcolor="#e78b49"] ;
|
||||
325 -> 326 ;
|
||||
327 [label="gini = 0.0\nsamples = 1\nvalue = [0, 1]", fillcolor="#399de5"] ;
|
||||
326 -> 327 ;
|
||||
328 [label="bmi <= 24.15\ngini = 0.074\nsamples = 26\nvalue = [25, 1]", fillcolor="#e68641"] ;
|
||||
326 -> 328 ;
|
||||
329 [label="smoking_status_never smoked <= 0.5\ngini = 0.444\nsamples = 3\nvalue = [2, 1]", fillcolor="#f2c09c"] ;
|
||||
328 -> 329 ;
|
||||
330 [label="gini = 0.0\nsamples = 1\nvalue = [0, 1]", fillcolor="#399de5"] ;
|
||||
329 -> 330 ;
|
||||
331 [label="gini = 0.0\nsamples = 2\nvalue = [2, 0]", fillcolor="#e58139"] ;
|
||||
329 -> 331 ;
|
||||
332 [label="gini = 0.0\nsamples = 23\nvalue = [23, 0]", fillcolor="#e58139"] ;
|
||||
328 -> 332 ;
|
||||
333 [label="avg_glucose_level <= 107.125\ngini = 0.408\nsamples = 35\nvalue = [25, 10]", fillcolor="#efb388"] ;
|
||||
325 -> 333 ;
|
||||
334 [label="smoking_status_never smoked <= 0.5\ngini = 0.227\nsamples = 23\nvalue = [20, 3]", fillcolor="#e99457"] ;
|
||||
333 -> 334 ;
|
||||
335 [label="gini = 0.0\nsamples = 11\nvalue = [11, 0]", fillcolor="#e58139"] ;
|
||||
334 -> 335 ;
|
||||
336 [label="age <= 79.5\ngini = 0.375\nsamples = 12\nvalue = [9, 3]", fillcolor="#eeab7b"] ;
|
||||
334 -> 336 ;
|
||||
337 [label="heart_disease <= 0.5\ngini = 0.5\nsamples = 4\nvalue = [2, 2]", fillcolor="#ffffff"] ;
|
||||
336 -> 337 ;
|
||||
338 [label="avg_glucose_level <= 71.035\ngini = 0.444\nsamples = 3\nvalue = [1, 2]", fillcolor="#9ccef2"] ;
|
||||
337 -> 338 ;
|
||||
339 [label="gini = 0.0\nsamples = 1\nvalue = [1, 0]", fillcolor="#e58139"] ;
|
||||
338 -> 339 ;
|
||||
340 [label="gini = 0.0\nsamples = 2\nvalue = [0, 2]", fillcolor="#399de5"] ;
|
||||
338 -> 340 ;
|
||||
341 [label="gini = 0.0\nsamples = 1\nvalue = [1, 0]", fillcolor="#e58139"] ;
|
||||
337 -> 341 ;
|
||||
342 [label="bmi <= 26.5\ngini = 0.219\nsamples = 8\nvalue = [7, 1]", fillcolor="#e99355"] ;
|
||||
336 -> 342 ;
|
||||
343 [label="avg_glucose_level <= 82.885\ngini = 0.444\nsamples = 3\nvalue = [2, 1]", fillcolor="#f2c09c"] ;
|
||||
342 -> 343 ;
|
||||
344 [label="gini = 0.0\nsamples = 1\nvalue = [0, 1]", fillcolor="#399de5"] ;
|
||||
343 -> 344 ;
|
||||
345 [label="gini = 0.0\nsamples = 2\nvalue = [2, 0]", fillcolor="#e58139"] ;
|
||||
343 -> 345 ;
|
||||
346 [label="gini = 0.0\nsamples = 5\nvalue = [5, 0]", fillcolor="#e58139"] ;
|
||||
342 -> 346 ;
|
||||
347 [label="ever_married <= 0.5\ngini = 0.486\nsamples = 12\nvalue = [5, 7]", fillcolor="#c6e3f8"] ;
|
||||
333 -> 347 ;
|
||||
348 [label="gini = 0.0\nsamples = 3\nvalue = [3, 0]", fillcolor="#e58139"] ;
|
||||
347 -> 348 ;
|
||||
349 [label="smoking_status_never smoked <= 0.5\ngini = 0.346\nsamples = 9\nvalue = [2, 7]", fillcolor="#72b9ec"] ;
|
||||
347 -> 349 ;
|
||||
350 [label="gini = 0.0\nsamples = 4\nvalue = [0, 4]", fillcolor="#399de5"] ;
|
||||
349 -> 350 ;
|
||||
351 [label="heart_disease <= 0.5\ngini = 0.48\nsamples = 5\nvalue = [2, 3]", fillcolor="#bddef6"] ;
|
||||
349 -> 351 ;
|
||||
352 [label="age <= 80.5\ngini = 0.444\nsamples = 3\nvalue = [2, 1]", fillcolor="#f2c09c"] ;
|
||||
351 -> 352 ;
|
||||
353 [label="gini = 0.0\nsamples = 2\nvalue = [2, 0]", fillcolor="#e58139"] ;
|
||||
352 -> 353 ;
|
||||
354 [label="gini = 0.0\nsamples = 1\nvalue = [0, 1]", fillcolor="#399de5"] ;
|
||||
352 -> 354 ;
|
||||
355 [label="gini = 0.0\nsamples = 2\nvalue = [0, 2]", fillcolor="#399de5"] ;
|
||||
351 -> 355 ;
|
||||
356 [label="avg_glucose_level <= 82.24\ngini = 0.498\nsamples = 17\nvalue = [9, 8]", fillcolor="#fcf1e9"] ;
|
||||
324 -> 356 ;
|
||||
357 [label="avg_glucose_level <= 64.685\ngini = 0.408\nsamples = 7\nvalue = [2, 5]", fillcolor="#88c4ef"] ;
|
||||
356 -> 357 ;
|
||||
358 [label="gini = 0.0\nsamples = 2\nvalue = [2, 0]", fillcolor="#e58139"] ;
|
||||
357 -> 358 ;
|
||||
359 [label="gini = 0.0\nsamples = 5\nvalue = [0, 5]", fillcolor="#399de5"] ;
|
||||
357 -> 359 ;
|
||||
360 [label="avg_glucose_level <= 237.3\ngini = 0.42\nsamples = 10\nvalue = [7, 3]", fillcolor="#f0b78e"] ;
|
||||
356 -> 360 ;
|
||||
361 [label="ever_married <= 0.5\ngini = 0.219\nsamples = 8\nvalue = [7, 1]", fillcolor="#e99355"] ;
|
||||
360 -> 361 ;
|
||||
362 [label="gini = 0.0\nsamples = 1\nvalue = [0, 1]", fillcolor="#399de5"] ;
|
||||
361 -> 362 ;
|
||||
363 [label="gini = 0.0\nsamples = 7\nvalue = [7, 0]", fillcolor="#e58139"] ;
|
||||
361 -> 363 ;
|
||||
364 [label="gini = 0.0\nsamples = 2\nvalue = [0, 2]", fillcolor="#399de5"] ;
|
||||
360 -> 364 ;
|
||||
365 [label="Residence_type_Urban <= 0.5\ngini = 0.493\nsamples = 25\nvalue = [14, 11]", fillcolor="#f9e4d5"] ;
|
||||
323 -> 365 ;
|
||||
366 [label="avg_glucose_level <= 151.4\ngini = 0.375\nsamples = 12\nvalue = [9, 3]", fillcolor="#eeab7b"] ;
|
||||
365 -> 366 ;
|
||||
367 [label="bmi <= 20.8\ngini = 0.18\nsamples = 10\nvalue = [9, 1]", fillcolor="#e88f4f"] ;
|
||||
366 -> 367 ;
|
||||
368 [label="avg_glucose_level <= 87.09\ngini = 0.5\nsamples = 2\nvalue = [1, 1]", fillcolor="#ffffff"] ;
|
||||
367 -> 368 ;
|
||||
369 [label="gini = 0.0\nsamples = 1\nvalue = [0, 1]", fillcolor="#399de5"] ;
|
||||
368 -> 369 ;
|
||||
370 [label="gini = 0.0\nsamples = 1\nvalue = [1, 0]", fillcolor="#e58139"] ;
|
||||
368 -> 370 ;
|
||||
371 [label="gini = 0.0\nsamples = 8\nvalue = [8, 0]", fillcolor="#e58139"] ;
|
||||
367 -> 371 ;
|
||||
372 [label="gini = 0.0\nsamples = 2\nvalue = [0, 2]", fillcolor="#399de5"] ;
|
||||
366 -> 372 ;
|
||||
373 [label="bmi <= 23.8\ngini = 0.473\nsamples = 13\nvalue = [5, 8]", fillcolor="#b5daf5"] ;
|
||||
365 -> 373 ;
|
||||
374 [label="gender <= 0.5\ngini = 0.375\nsamples = 4\nvalue = [3, 1]", fillcolor="#eeab7b"] ;
|
||||
373 -> 374 ;
|
||||
375 [label="gini = 0.0\nsamples = 1\nvalue = [0, 1]", fillcolor="#399de5"] ;
|
||||
374 -> 375 ;
|
||||
376 [label="gini = 0.0\nsamples = 3\nvalue = [3, 0]", fillcolor="#e58139"] ;
|
||||
374 -> 376 ;
|
||||
377 [label="heart_disease <= 0.5\ngini = 0.346\nsamples = 9\nvalue = [2, 7]", fillcolor="#72b9ec"] ;
|
||||
373 -> 377 ;
|
||||
378 [label="avg_glucose_level <= 66.515\ngini = 0.219\nsamples = 8\nvalue = [1, 7]", fillcolor="#55abe9"] ;
|
||||
377 -> 378 ;
|
||||
379 [label="avg_glucose_level <= 59.03\ngini = 0.5\nsamples = 2\nvalue = [1, 1]", fillcolor="#ffffff"] ;
|
||||
378 -> 379 ;
|
||||
380 [label="gini = 0.0\nsamples = 1\nvalue = [0, 1]", fillcolor="#399de5"] ;
|
||||
379 -> 380 ;
|
||||
381 [label="gini = 0.0\nsamples = 1\nvalue = [1, 0]", fillcolor="#e58139"] ;
|
||||
379 -> 381 ;
|
||||
382 [label="gini = 0.0\nsamples = 6\nvalue = [0, 6]", fillcolor="#399de5"] ;
|
||||
378 -> 382 ;
|
||||
383 [label="gini = 0.0\nsamples = 1\nvalue = [1, 0]", fillcolor="#e58139"] ;
|
||||
377 -> 383 ;
|
||||
384 [label="avg_glucose_level <= 219.865\ngini = 0.191\nsamples = 300\nvalue = [268, 32]", fillcolor="#e89051"] ;
|
||||
206 -> 384 ;
|
||||
385 [label="hypertension <= 0.5\ngini = 0.145\nsamples = 255\nvalue = [235, 20]", fillcolor="#e78c4a"] ;
|
||||
384 -> 385 ;
|
||||
386 [label="age <= 76.5\ngini = 0.099\nsamples = 192\nvalue = [182, 10]", fillcolor="#e68844"] ;
|
||||
385 -> 386 ;
|
||||
387 [label="smoking_status_smokes <= 0.5\ngini = 0.017\nsamples = 114\nvalue = [113, 1]", fillcolor="#e5823b"] ;
|
||||
386 -> 387 ;
|
||||
388 [label="gini = 0.0\nsamples = 95\nvalue = [95, 0]", fillcolor="#e58139"] ;
|
||||
387 -> 388 ;
|
||||
389 [label="age <= 73.0\ngini = 0.1\nsamples = 19\nvalue = [18, 1]", fillcolor="#e68844"] ;
|
||||
387 -> 389 ;
|
||||
390 [label="gini = 0.0\nsamples = 16\nvalue = [16, 0]", fillcolor="#e58139"] ;
|
||||
389 -> 390 ;
|
||||
391 [label="work_type_Private <= 0.5\ngini = 0.444\nsamples = 3\nvalue = [2, 1]", fillcolor="#f2c09c"] ;
|
||||
389 -> 391 ;
|
||||
392 [label="gini = 0.0\nsamples = 2\nvalue = [2, 0]", fillcolor="#e58139"] ;
|
||||
391 -> 392 ;
|
||||
393 [label="gini = 0.0\nsamples = 1\nvalue = [0, 1]", fillcolor="#399de5"] ;
|
||||
391 -> 393 ;
|
||||
394 [label="bmi <= 43.15\ngini = 0.204\nsamples = 78\nvalue = [69, 9]", fillcolor="#e89153"] ;
|
||||
386 -> 394 ;
|
||||
395 [label="bmi <= 34.15\ngini = 0.186\nsamples = 77\nvalue = [69, 8]", fillcolor="#e89050"] ;
|
||||
394 -> 395 ;
|
||||
396 [label="bmi <= 34.0\ngini = 0.245\nsamples = 56\nvalue = [48, 8]", fillcolor="#e9965a"] ;
|
||||
395 -> 396 ;
|
||||
397 [label="avg_glucose_level <= 85.775\ngini = 0.222\nsamples = 55\nvalue = [48, 7]", fillcolor="#e99356"] ;
|
||||
396 -> 397 ;
|
||||
398 [label="gini = 0.0\nsamples = 19\nvalue = [19, 0]", fillcolor="#e58139"] ;
|
||||
397 -> 398 ;
|
||||
399 [label="bmi <= 31.95\ngini = 0.313\nsamples = 36\nvalue = [29, 7]", fillcolor="#eb9f69"] ;
|
||||
397 -> 399 ;
|
||||
400 [label="avg_glucose_level <= 87.16\ngini = 0.147\nsamples = 25\nvalue = [23, 2]", fillcolor="#e78c4a"] ;
|
||||
399 -> 400 ;
|
||||
401 [label="gini = 0.0\nsamples = 1\nvalue = [0, 1]", fillcolor="#399de5"] ;
|
||||
400 -> 401 ;
|
||||
402 [label="age <= 78.5\ngini = 0.08\nsamples = 24\nvalue = [23, 1]", fillcolor="#e68642"] ;
|
||||
400 -> 402 ;
|
||||
403 [label="avg_glucose_level <= 106.92\ngini = 0.198\nsamples = 9\nvalue = [8, 1]", fillcolor="#e89152"] ;
|
||||
402 -> 403 ;
|
||||
404 [label="gini = 0.0\nsamples = 6\nvalue = [6, 0]", fillcolor="#e58139"] ;
|
||||
403 -> 404 ;
|
||||
405 [label="avg_glucose_level <= 155.525\ngini = 0.444\nsamples = 3\nvalue = [2, 1]", fillcolor="#f2c09c"] ;
|
||||
403 -> 405 ;
|
||||
406 [label="gini = 0.0\nsamples = 1\nvalue = [0, 1]", fillcolor="#399de5"] ;
|
||||
405 -> 406 ;
|
||||
407 [label="gini = 0.0\nsamples = 2\nvalue = [2, 0]", fillcolor="#e58139"] ;
|
||||
405 -> 407 ;
|
||||
408 [label="gini = 0.0\nsamples = 15\nvalue = [15, 0]", fillcolor="#e58139"] ;
|
||||
402 -> 408 ;
|
||||
409 [label="avg_glucose_level <= 111.015\ngini = 0.496\nsamples = 11\nvalue = [6, 5]", fillcolor="#fbeade"] ;
|
||||
399 -> 409 ;
|
||||
410 [label="avg_glucose_level <= 89.3\ngini = 0.32\nsamples = 5\nvalue = [1, 4]", fillcolor="#6ab6ec"] ;
|
||||
409 -> 410 ;
|
||||
411 [label="gini = 0.0\nsamples = 1\nvalue = [1, 0]", fillcolor="#e58139"] ;
|
||||
410 -> 411 ;
|
||||
412 [label="gini = 0.0\nsamples = 4\nvalue = [0, 4]", fillcolor="#399de5"] ;
|
||||
410 -> 412 ;
|
||||
413 [label="bmi <= 32.75\ngini = 0.278\nsamples = 6\nvalue = [5, 1]", fillcolor="#ea9a61"] ;
|
||||
409 -> 413 ;
|
||||
414 [label="gini = 0.0\nsamples = 1\nvalue = [0, 1]", fillcolor="#399de5"] ;
|
||||
413 -> 414 ;
|
||||
415 [label="gini = 0.0\nsamples = 5\nvalue = [5, 0]", fillcolor="#e58139"] ;
|
||||
413 -> 415 ;
|
||||
416 [label="gini = 0.0\nsamples = 1\nvalue = [0, 1]", fillcolor="#399de5"] ;
|
||||
396 -> 416 ;
|
||||
417 [label="gini = 0.0\nsamples = 21\nvalue = [21, 0]", fillcolor="#e58139"] ;
|
||||
395 -> 417 ;
|
||||
418 [label="gini = 0.0\nsamples = 1\nvalue = [0, 1]", fillcolor="#399de5"] ;
|
||||
394 -> 418 ;
|
||||
419 [label="ever_married <= 0.5\ngini = 0.267\nsamples = 63\nvalue = [53, 10]", fillcolor="#ea995e"] ;
|
||||
385 -> 419 ;
|
||||
420 [label="gender <= 0.5\ngini = 0.48\nsamples = 5\nvalue = [2, 3]", fillcolor="#bddef6"] ;
|
||||
419 -> 420 ;
|
||||
421 [label="gini = 0.0\nsamples = 2\nvalue = [2, 0]", fillcolor="#e58139"] ;
|
||||
420 -> 421 ;
|
||||
422 [label="gini = 0.0\nsamples = 3\nvalue = [0, 3]", fillcolor="#399de5"] ;
|
||||
420 -> 422 ;
|
||||
423 [label="avg_glucose_level <= 116.13\ngini = 0.212\nsamples = 58\nvalue = [51, 7]", fillcolor="#e99254"] ;
|
||||
419 -> 423 ;
|
||||
424 [label="age <= 79.5\ngini = 0.067\nsamples = 29\nvalue = [28, 1]", fillcolor="#e68640"] ;
|
||||
423 -> 424 ;
|
||||
425 [label="gini = 0.0\nsamples = 23\nvalue = [23, 0]", fillcolor="#e58139"] ;
|
||||
424 -> 425 ;
|
||||
426 [label="smoking_status_formerly smoked <= 0.5\ngini = 0.278\nsamples = 6\nvalue = [5, 1]", fillcolor="#ea9a61"] ;
|
||||
424 -> 426 ;
|
||||
427 [label="gini = 0.0\nsamples = 4\nvalue = [4, 0]", fillcolor="#e58139"] ;
|
||||
426 -> 427 ;
|
||||
428 [label="bmi <= 32.0\ngini = 0.5\nsamples = 2\nvalue = [1, 1]", fillcolor="#ffffff"] ;
|
||||
426 -> 428 ;
|
||||
429 [label="gini = 0.0\nsamples = 1\nvalue = [1, 0]", fillcolor="#e58139"] ;
|
||||
428 -> 429 ;
|
||||
430 [label="gini = 0.0\nsamples = 1\nvalue = [0, 1]", fillcolor="#399de5"] ;
|
||||
428 -> 430 ;
|
||||
431 [label="avg_glucose_level <= 199.87\ngini = 0.328\nsamples = 29\nvalue = [23, 6]", fillcolor="#eca26d"] ;
|
||||
423 -> 431 ;
|
||||
432 [label="work_type_Private <= 0.5\ngini = 0.49\nsamples = 14\nvalue = [8, 6]", fillcolor="#f8e0ce"] ;
|
||||
431 -> 432 ;
|
||||
433 [label="bmi <= 33.0\ngini = 0.444\nsamples = 6\nvalue = [2, 4]", fillcolor="#9ccef2"] ;
|
||||
432 -> 433 ;
|
||||
434 [label="gini = 0.0\nsamples = 3\nvalue = [0, 3]", fillcolor="#399de5"] ;
|
||||
433 -> 434 ;
|
||||
435 [label="gender <= 0.5\ngini = 0.444\nsamples = 3\nvalue = [2, 1]", fillcolor="#f2c09c"] ;
|
||||
433 -> 435 ;
|
||||
436 [label="gini = 0.0\nsamples = 1\nvalue = [0, 1]", fillcolor="#399de5"] ;
|
||||
435 -> 436 ;
|
||||
437 [label="gini = 0.0\nsamples = 2\nvalue = [2, 0]", fillcolor="#e58139"] ;
|
||||
435 -> 437 ;
|
||||
438 [label="avg_glucose_level <= 192.38\ngini = 0.375\nsamples = 8\nvalue = [6, 2]", fillcolor="#eeab7b"] ;
|
||||
432 -> 438 ;
|
||||
439 [label="avg_glucose_level <= 170.03\ngini = 0.5\nsamples = 4\nvalue = [2, 2]", fillcolor="#ffffff"] ;
|
||||
438 -> 439 ;
|
||||
440 [label="gini = 0.0\nsamples = 2\nvalue = [2, 0]", fillcolor="#e58139"] ;
|
||||
439 -> 440 ;
|
||||
441 [label="gini = 0.0\nsamples = 2\nvalue = [0, 2]", fillcolor="#399de5"] ;
|
||||
439 -> 441 ;
|
||||
442 [label="gini = 0.0\nsamples = 4\nvalue = [4, 0]", fillcolor="#e58139"] ;
|
||||
438 -> 442 ;
|
||||
443 [label="gini = 0.0\nsamples = 15\nvalue = [15, 0]", fillcolor="#e58139"] ;
|
||||
431 -> 443 ;
|
||||
444 [label="work_type_Private <= 0.5\ngini = 0.391\nsamples = 45\nvalue = [33, 12]", fillcolor="#eeaf81"] ;
|
||||
384 -> 444 ;
|
||||
445 [label="avg_glucose_level <= 252.09\ngini = 0.142\nsamples = 13\nvalue = [12, 1]", fillcolor="#e78c49"] ;
|
||||
444 -> 445 ;
|
||||
446 [label="gini = 0.0\nsamples = 12\nvalue = [12, 0]", fillcolor="#e58139"] ;
|
||||
445 -> 446 ;
|
||||
447 [label="gini = 0.0\nsamples = 1\nvalue = [0, 1]", fillcolor="#399de5"] ;
|
||||
445 -> 447 ;
|
||||
448 [label="bmi <= 31.65\ngini = 0.451\nsamples = 32\nvalue = [21, 11]", fillcolor="#f3c3a1"] ;
|
||||
444 -> 448 ;
|
||||
449 [label="gini = 0.0\nsamples = 6\nvalue = [6, 0]", fillcolor="#e58139"] ;
|
||||
448 -> 449 ;
|
||||
450 [label="bmi <= 32.4\ngini = 0.488\nsamples = 26\nvalue = [15, 11]", fillcolor="#f8ddca"] ;
|
||||
448 -> 450 ;
|
||||
451 [label="hypertension <= 0.5\ngini = 0.32\nsamples = 5\nvalue = [1, 4]", fillcolor="#6ab6ec"] ;
|
||||
450 -> 451 ;
|
||||
452 [label="gini = 0.0\nsamples = 4\nvalue = [0, 4]", fillcolor="#399de5"] ;
|
||||
451 -> 452 ;
|
||||
453 [label="gini = 0.0\nsamples = 1\nvalue = [1, 0]", fillcolor="#e58139"] ;
|
||||
451 -> 453 ;
|
||||
454 [label="bmi <= 33.55\ngini = 0.444\nsamples = 21\nvalue = [14, 7]", fillcolor="#f2c09c"] ;
|
||||
450 -> 454 ;
|
||||
455 [label="gini = 0.0\nsamples = 6\nvalue = [6, 0]", fillcolor="#e58139"] ;
|
||||
454 -> 455 ;
|
||||
456 [label="age <= 66.5\ngini = 0.498\nsamples = 15\nvalue = [8, 7]", fillcolor="#fcefe6"] ;
|
||||
454 -> 456 ;
|
||||
457 [label="gini = 0.0\nsamples = 2\nvalue = [2, 0]", fillcolor="#e58139"] ;
|
||||
456 -> 457 ;
|
||||
458 [label="age <= 77.5\ngini = 0.497\nsamples = 13\nvalue = [6, 7]", fillcolor="#e3f1fb"] ;
|
||||
456 -> 458 ;
|
||||
459 [label="heart_disease <= 0.5\ngini = 0.463\nsamples = 11\nvalue = [4, 7]", fillcolor="#aad5f4"] ;
|
||||
458 -> 459 ;
|
||||
460 [label="hypertension <= 0.5\ngini = 0.494\nsamples = 9\nvalue = [4, 5]", fillcolor="#d7ebfa"] ;
|
||||
459 -> 460 ;
|
||||
461 [label="age <= 74.5\ngini = 0.49\nsamples = 7\nvalue = [4, 3]", fillcolor="#f8e0ce"] ;
|
||||
460 -> 461 ;
|
||||
462 [label="age <= 69.0\ngini = 0.48\nsamples = 5\nvalue = [2, 3]", fillcolor="#bddef6"] ;
|
||||
461 -> 462 ;
|
||||
463 [label="age <= 67.5\ngini = 0.444\nsamples = 3\nvalue = [2, 1]", fillcolor="#f2c09c"] ;
|
||||
462 -> 463 ;
|
||||
464 [label="gini = 0.0\nsamples = 1\nvalue = [1, 0]", fillcolor="#e58139"] ;
|
||||
463 -> 464 ;
|
||||
465 [label="bmi <= 43.15\ngini = 0.5\nsamples = 2\nvalue = [1, 1]", fillcolor="#ffffff"] ;
|
||||
463 -> 465 ;
|
||||
466 [label="gini = 0.0\nsamples = 1\nvalue = [0, 1]", fillcolor="#399de5"] ;
|
||||
465 -> 466 ;
|
||||
467 [label="gini = 0.0\nsamples = 1\nvalue = [1, 0]", fillcolor="#e58139"] ;
|
||||
465 -> 467 ;
|
||||
468 [label="gini = 0.0\nsamples = 2\nvalue = [0, 2]", fillcolor="#399de5"] ;
|
||||
462 -> 468 ;
|
||||
469 [label="gini = 0.0\nsamples = 2\nvalue = [2, 0]", fillcolor="#e58139"] ;
|
||||
461 -> 469 ;
|
||||
470 [label="gini = 0.0\nsamples = 2\nvalue = [0, 2]", fillcolor="#399de5"] ;
|
||||
460 -> 470 ;
|
||||
471 [label="gini = 0.0\nsamples = 2\nvalue = [0, 2]", fillcolor="#399de5"] ;
|
||||
459 -> 471 ;
|
||||
472 [label="gini = 0.0\nsamples = 2\nvalue = [2, 0]", fillcolor="#e58139"] ;
|
||||
458 -> 472 ;
|
||||
}
|
||||
483
TestModel/testFile.ipynb
Normal file
483
TestModel/testFile.ipynb
Normal file
@@ -0,0 +1,483 @@
|
||||
{
|
||||
"cells": [
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 1,
|
||||
"metadata": {},
|
||||
"outputs": [
|
||||
{
|
||||
"data": {
|
||||
"text/html": [
|
||||
"<div>\n",
|
||||
"<style scoped>\n",
|
||||
" .dataframe tbody tr th:only-of-type {\n",
|
||||
" vertical-align: middle;\n",
|
||||
" }\n",
|
||||
"\n",
|
||||
" .dataframe tbody tr th {\n",
|
||||
" vertical-align: top;\n",
|
||||
" }\n",
|
||||
"\n",
|
||||
" .dataframe thead th {\n",
|
||||
" text-align: right;\n",
|
||||
" }\n",
|
||||
"</style>\n",
|
||||
"<table border=\"1\" class=\"dataframe\">\n",
|
||||
" <thead>\n",
|
||||
" <tr style=\"text-align: right;\">\n",
|
||||
" <th></th>\n",
|
||||
" <th>Rank</th>\n",
|
||||
" <th>Name</th>\n",
|
||||
" <th>Platform</th>\n",
|
||||
" <th>Year</th>\n",
|
||||
" <th>Genre</th>\n",
|
||||
" <th>Publisher</th>\n",
|
||||
" <th>NA_Sales</th>\n",
|
||||
" <th>EU_Sales</th>\n",
|
||||
" <th>JP_Sales</th>\n",
|
||||
" <th>Other_Sales</th>\n",
|
||||
" <th>Global_Sales</th>\n",
|
||||
" </tr>\n",
|
||||
" </thead>\n",
|
||||
" <tbody>\n",
|
||||
" <tr>\n",
|
||||
" <th>0</th>\n",
|
||||
" <td>1</td>\n",
|
||||
" <td>Wii Sports</td>\n",
|
||||
" <td>Wii</td>\n",
|
||||
" <td>2006.0</td>\n",
|
||||
" <td>Sports</td>\n",
|
||||
" <td>Nintendo</td>\n",
|
||||
" <td>41.49</td>\n",
|
||||
" <td>29.02</td>\n",
|
||||
" <td>3.77</td>\n",
|
||||
" <td>8.46</td>\n",
|
||||
" <td>82.74</td>\n",
|
||||
" </tr>\n",
|
||||
" <tr>\n",
|
||||
" <th>1</th>\n",
|
||||
" <td>2</td>\n",
|
||||
" <td>Super Mario Bros.</td>\n",
|
||||
" <td>NES</td>\n",
|
||||
" <td>1985.0</td>\n",
|
||||
" <td>Platform</td>\n",
|
||||
" <td>Nintendo</td>\n",
|
||||
" <td>29.08</td>\n",
|
||||
" <td>3.58</td>\n",
|
||||
" <td>6.81</td>\n",
|
||||
" <td>0.77</td>\n",
|
||||
" <td>40.24</td>\n",
|
||||
" </tr>\n",
|
||||
" <tr>\n",
|
||||
" <th>2</th>\n",
|
||||
" <td>3</td>\n",
|
||||
" <td>Mario Kart Wii</td>\n",
|
||||
" <td>Wii</td>\n",
|
||||
" <td>2008.0</td>\n",
|
||||
" <td>Racing</td>\n",
|
||||
" <td>Nintendo</td>\n",
|
||||
" <td>15.85</td>\n",
|
||||
" <td>12.88</td>\n",
|
||||
" <td>3.79</td>\n",
|
||||
" <td>3.31</td>\n",
|
||||
" <td>35.82</td>\n",
|
||||
" </tr>\n",
|
||||
" <tr>\n",
|
||||
" <th>3</th>\n",
|
||||
" <td>4</td>\n",
|
||||
" <td>Wii Sports Resort</td>\n",
|
||||
" <td>Wii</td>\n",
|
||||
" <td>2009.0</td>\n",
|
||||
" <td>Sports</td>\n",
|
||||
" <td>Nintendo</td>\n",
|
||||
" <td>15.75</td>\n",
|
||||
" <td>11.01</td>\n",
|
||||
" <td>3.28</td>\n",
|
||||
" <td>2.96</td>\n",
|
||||
" <td>33.00</td>\n",
|
||||
" </tr>\n",
|
||||
" <tr>\n",
|
||||
" <th>4</th>\n",
|
||||
" <td>5</td>\n",
|
||||
" <td>Pokemon Red/Pokemon Blue</td>\n",
|
||||
" <td>GB</td>\n",
|
||||
" <td>1996.0</td>\n",
|
||||
" <td>Role-Playing</td>\n",
|
||||
" <td>Nintendo</td>\n",
|
||||
" <td>11.27</td>\n",
|
||||
" <td>8.89</td>\n",
|
||||
" <td>10.22</td>\n",
|
||||
" <td>1.00</td>\n",
|
||||
" <td>31.37</td>\n",
|
||||
" </tr>\n",
|
||||
" <tr>\n",
|
||||
" <th>...</th>\n",
|
||||
" <td>...</td>\n",
|
||||
" <td>...</td>\n",
|
||||
" <td>...</td>\n",
|
||||
" <td>...</td>\n",
|
||||
" <td>...</td>\n",
|
||||
" <td>...</td>\n",
|
||||
" <td>...</td>\n",
|
||||
" <td>...</td>\n",
|
||||
" <td>...</td>\n",
|
||||
" <td>...</td>\n",
|
||||
" <td>...</td>\n",
|
||||
" </tr>\n",
|
||||
" <tr>\n",
|
||||
" <th>16593</th>\n",
|
||||
" <td>16596</td>\n",
|
||||
" <td>Woody Woodpecker in Crazy Castle 5</td>\n",
|
||||
" <td>GBA</td>\n",
|
||||
" <td>2002.0</td>\n",
|
||||
" <td>Platform</td>\n",
|
||||
" <td>Kemco</td>\n",
|
||||
" <td>0.01</td>\n",
|
||||
" <td>0.00</td>\n",
|
||||
" <td>0.00</td>\n",
|
||||
" <td>0.00</td>\n",
|
||||
" <td>0.01</td>\n",
|
||||
" </tr>\n",
|
||||
" <tr>\n",
|
||||
" <th>16594</th>\n",
|
||||
" <td>16597</td>\n",
|
||||
" <td>Men in Black II: Alien Escape</td>\n",
|
||||
" <td>GC</td>\n",
|
||||
" <td>2003.0</td>\n",
|
||||
" <td>Shooter</td>\n",
|
||||
" <td>Infogrames</td>\n",
|
||||
" <td>0.01</td>\n",
|
||||
" <td>0.00</td>\n",
|
||||
" <td>0.00</td>\n",
|
||||
" <td>0.00</td>\n",
|
||||
" <td>0.01</td>\n",
|
||||
" </tr>\n",
|
||||
" <tr>\n",
|
||||
" <th>16595</th>\n",
|
||||
" <td>16598</td>\n",
|
||||
" <td>SCORE International Baja 1000: The Official Game</td>\n",
|
||||
" <td>PS2</td>\n",
|
||||
" <td>2008.0</td>\n",
|
||||
" <td>Racing</td>\n",
|
||||
" <td>Activision</td>\n",
|
||||
" <td>0.00</td>\n",
|
||||
" <td>0.00</td>\n",
|
||||
" <td>0.00</td>\n",
|
||||
" <td>0.00</td>\n",
|
||||
" <td>0.01</td>\n",
|
||||
" </tr>\n",
|
||||
" <tr>\n",
|
||||
" <th>16596</th>\n",
|
||||
" <td>16599</td>\n",
|
||||
" <td>Know How 2</td>\n",
|
||||
" <td>DS</td>\n",
|
||||
" <td>2010.0</td>\n",
|
||||
" <td>Puzzle</td>\n",
|
||||
" <td>7G//AMES</td>\n",
|
||||
" <td>0.00</td>\n",
|
||||
" <td>0.01</td>\n",
|
||||
" <td>0.00</td>\n",
|
||||
" <td>0.00</td>\n",
|
||||
" <td>0.01</td>\n",
|
||||
" </tr>\n",
|
||||
" <tr>\n",
|
||||
" <th>16597</th>\n",
|
||||
" <td>16600</td>\n",
|
||||
" <td>Spirits & Spells</td>\n",
|
||||
" <td>GBA</td>\n",
|
||||
" <td>2003.0</td>\n",
|
||||
" <td>Platform</td>\n",
|
||||
" <td>Wanadoo</td>\n",
|
||||
" <td>0.01</td>\n",
|
||||
" <td>0.00</td>\n",
|
||||
" <td>0.00</td>\n",
|
||||
" <td>0.00</td>\n",
|
||||
" <td>0.01</td>\n",
|
||||
" </tr>\n",
|
||||
" </tbody>\n",
|
||||
"</table>\n",
|
||||
"<p>16598 rows × 11 columns</p>\n",
|
||||
"</div>"
|
||||
],
|
||||
"text/plain": [
|
||||
" Rank Name Platform \\\n",
|
||||
"0 1 Wii Sports Wii \n",
|
||||
"1 2 Super Mario Bros. NES \n",
|
||||
"2 3 Mario Kart Wii Wii \n",
|
||||
"3 4 Wii Sports Resort Wii \n",
|
||||
"4 5 Pokemon Red/Pokemon Blue GB \n",
|
||||
"... ... ... ... \n",
|
||||
"16593 16596 Woody Woodpecker in Crazy Castle 5 GBA \n",
|
||||
"16594 16597 Men in Black II: Alien Escape GC \n",
|
||||
"16595 16598 SCORE International Baja 1000: The Official Game PS2 \n",
|
||||
"16596 16599 Know How 2 DS \n",
|
||||
"16597 16600 Spirits & Spells GBA \n",
|
||||
"\n",
|
||||
" Year Genre Publisher NA_Sales EU_Sales JP_Sales \\\n",
|
||||
"0 2006.0 Sports Nintendo 41.49 29.02 3.77 \n",
|
||||
"1 1985.0 Platform Nintendo 29.08 3.58 6.81 \n",
|
||||
"2 2008.0 Racing Nintendo 15.85 12.88 3.79 \n",
|
||||
"3 2009.0 Sports Nintendo 15.75 11.01 3.28 \n",
|
||||
"4 1996.0 Role-Playing Nintendo 11.27 8.89 10.22 \n",
|
||||
"... ... ... ... ... ... ... \n",
|
||||
"16593 2002.0 Platform Kemco 0.01 0.00 0.00 \n",
|
||||
"16594 2003.0 Shooter Infogrames 0.01 0.00 0.00 \n",
|
||||
"16595 2008.0 Racing Activision 0.00 0.00 0.00 \n",
|
||||
"16596 2010.0 Puzzle 7G//AMES 0.00 0.01 0.00 \n",
|
||||
"16597 2003.0 Platform Wanadoo 0.01 0.00 0.00 \n",
|
||||
"\n",
|
||||
" Other_Sales Global_Sales \n",
|
||||
"0 8.46 82.74 \n",
|
||||
"1 0.77 40.24 \n",
|
||||
"2 3.31 35.82 \n",
|
||||
"3 2.96 33.00 \n",
|
||||
"4 1.00 31.37 \n",
|
||||
"... ... ... \n",
|
||||
"16593 0.00 0.01 \n",
|
||||
"16594 0.00 0.01 \n",
|
||||
"16595 0.00 0.01 \n",
|
||||
"16596 0.00 0.01 \n",
|
||||
"16597 0.00 0.01 \n",
|
||||
"\n",
|
||||
"[16598 rows x 11 columns]"
|
||||
]
|
||||
},
|
||||
"execution_count": 1,
|
||||
"metadata": {},
|
||||
"output_type": "execute_result"
|
||||
}
|
||||
],
|
||||
"source": [
|
||||
"import pandas as pd\n",
|
||||
"df = pd.read_csv('vgsales.csv')\n",
|
||||
"df"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 2,
|
||||
"metadata": {},
|
||||
"outputs": [
|
||||
{
|
||||
"data": {
|
||||
"text/plain": [
|
||||
"(16598, 11)"
|
||||
]
|
||||
},
|
||||
"execution_count": 2,
|
||||
"metadata": {},
|
||||
"output_type": "execute_result"
|
||||
}
|
||||
],
|
||||
"source": [
|
||||
"df.shape"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 3,
|
||||
"metadata": {},
|
||||
"outputs": [
|
||||
{
|
||||
"data": {
|
||||
"text/html": [
|
||||
"<div>\n",
|
||||
"<style scoped>\n",
|
||||
" .dataframe tbody tr th:only-of-type {\n",
|
||||
" vertical-align: middle;\n",
|
||||
" }\n",
|
||||
"\n",
|
||||
" .dataframe tbody tr th {\n",
|
||||
" vertical-align: top;\n",
|
||||
" }\n",
|
||||
"\n",
|
||||
" .dataframe thead th {\n",
|
||||
" text-align: right;\n",
|
||||
" }\n",
|
||||
"</style>\n",
|
||||
"<table border=\"1\" class=\"dataframe\">\n",
|
||||
" <thead>\n",
|
||||
" <tr style=\"text-align: right;\">\n",
|
||||
" <th></th>\n",
|
||||
" <th>Rank</th>\n",
|
||||
" <th>Year</th>\n",
|
||||
" <th>NA_Sales</th>\n",
|
||||
" <th>EU_Sales</th>\n",
|
||||
" <th>JP_Sales</th>\n",
|
||||
" <th>Other_Sales</th>\n",
|
||||
" <th>Global_Sales</th>\n",
|
||||
" </tr>\n",
|
||||
" </thead>\n",
|
||||
" <tbody>\n",
|
||||
" <tr>\n",
|
||||
" <th>count</th>\n",
|
||||
" <td>16598.000000</td>\n",
|
||||
" <td>16327.000000</td>\n",
|
||||
" <td>16598.000000</td>\n",
|
||||
" <td>16598.000000</td>\n",
|
||||
" <td>16598.000000</td>\n",
|
||||
" <td>16598.000000</td>\n",
|
||||
" <td>16598.000000</td>\n",
|
||||
" </tr>\n",
|
||||
" <tr>\n",
|
||||
" <th>mean</th>\n",
|
||||
" <td>8300.605254</td>\n",
|
||||
" <td>2006.406443</td>\n",
|
||||
" <td>0.264667</td>\n",
|
||||
" <td>0.146652</td>\n",
|
||||
" <td>0.077782</td>\n",
|
||||
" <td>0.048063</td>\n",
|
||||
" <td>0.537441</td>\n",
|
||||
" </tr>\n",
|
||||
" <tr>\n",
|
||||
" <th>std</th>\n",
|
||||
" <td>4791.853933</td>\n",
|
||||
" <td>5.828981</td>\n",
|
||||
" <td>0.816683</td>\n",
|
||||
" <td>0.505351</td>\n",
|
||||
" <td>0.309291</td>\n",
|
||||
" <td>0.188588</td>\n",
|
||||
" <td>1.555028</td>\n",
|
||||
" </tr>\n",
|
||||
" <tr>\n",
|
||||
" <th>min</th>\n",
|
||||
" <td>1.000000</td>\n",
|
||||
" <td>1980.000000</td>\n",
|
||||
" <td>0.000000</td>\n",
|
||||
" <td>0.000000</td>\n",
|
||||
" <td>0.000000</td>\n",
|
||||
" <td>0.000000</td>\n",
|
||||
" <td>0.010000</td>\n",
|
||||
" </tr>\n",
|
||||
" <tr>\n",
|
||||
" <th>25%</th>\n",
|
||||
" <td>4151.250000</td>\n",
|
||||
" <td>2003.000000</td>\n",
|
||||
" <td>0.000000</td>\n",
|
||||
" <td>0.000000</td>\n",
|
||||
" <td>0.000000</td>\n",
|
||||
" <td>0.000000</td>\n",
|
||||
" <td>0.060000</td>\n",
|
||||
" </tr>\n",
|
||||
" <tr>\n",
|
||||
" <th>50%</th>\n",
|
||||
" <td>8300.500000</td>\n",
|
||||
" <td>2007.000000</td>\n",
|
||||
" <td>0.080000</td>\n",
|
||||
" <td>0.020000</td>\n",
|
||||
" <td>0.000000</td>\n",
|
||||
" <td>0.010000</td>\n",
|
||||
" <td>0.170000</td>\n",
|
||||
" </tr>\n",
|
||||
" <tr>\n",
|
||||
" <th>75%</th>\n",
|
||||
" <td>12449.750000</td>\n",
|
||||
" <td>2010.000000</td>\n",
|
||||
" <td>0.240000</td>\n",
|
||||
" <td>0.110000</td>\n",
|
||||
" <td>0.040000</td>\n",
|
||||
" <td>0.040000</td>\n",
|
||||
" <td>0.470000</td>\n",
|
||||
" </tr>\n",
|
||||
" <tr>\n",
|
||||
" <th>max</th>\n",
|
||||
" <td>16600.000000</td>\n",
|
||||
" <td>2020.000000</td>\n",
|
||||
" <td>41.490000</td>\n",
|
||||
" <td>29.020000</td>\n",
|
||||
" <td>10.220000</td>\n",
|
||||
" <td>10.570000</td>\n",
|
||||
" <td>82.740000</td>\n",
|
||||
" </tr>\n",
|
||||
" </tbody>\n",
|
||||
"</table>\n",
|
||||
"</div>"
|
||||
],
|
||||
"text/plain": [
|
||||
" Rank Year NA_Sales EU_Sales JP_Sales \\\n",
|
||||
"count 16598.000000 16327.000000 16598.000000 16598.000000 16598.000000 \n",
|
||||
"mean 8300.605254 2006.406443 0.264667 0.146652 0.077782 \n",
|
||||
"std 4791.853933 5.828981 0.816683 0.505351 0.309291 \n",
|
||||
"min 1.000000 1980.000000 0.000000 0.000000 0.000000 \n",
|
||||
"25% 4151.250000 2003.000000 0.000000 0.000000 0.000000 \n",
|
||||
"50% 8300.500000 2007.000000 0.080000 0.020000 0.000000 \n",
|
||||
"75% 12449.750000 2010.000000 0.240000 0.110000 0.040000 \n",
|
||||
"max 16600.000000 2020.000000 41.490000 29.020000 10.220000 \n",
|
||||
"\n",
|
||||
" Other_Sales Global_Sales \n",
|
||||
"count 16598.000000 16598.000000 \n",
|
||||
"mean 0.048063 0.537441 \n",
|
||||
"std 0.188588 1.555028 \n",
|
||||
"min 0.000000 0.010000 \n",
|
||||
"25% 0.000000 0.060000 \n",
|
||||
"50% 0.010000 0.170000 \n",
|
||||
"75% 0.040000 0.470000 \n",
|
||||
"max 10.570000 82.740000 "
|
||||
]
|
||||
},
|
||||
"execution_count": 3,
|
||||
"metadata": {},
|
||||
"output_type": "execute_result"
|
||||
}
|
||||
],
|
||||
"source": [
|
||||
"df.describe()"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 4,
|
||||
"metadata": {},
|
||||
"outputs": [
|
||||
{
|
||||
"data": {
|
||||
"text/plain": [
|
||||
"array([[1, 'Wii Sports', 'Wii', ..., 3.77, 8.46, 82.74],\n",
|
||||
" [2, 'Super Mario Bros.', 'NES', ..., 6.81, 0.77, 40.24],\n",
|
||||
" [3, 'Mario Kart Wii', 'Wii', ..., 3.79, 3.31, 35.82],\n",
|
||||
" ...,\n",
|
||||
" [16598, 'SCORE International Baja 1000: The Official Game', 'PS2',\n",
|
||||
" ..., 0.0, 0.0, 0.01],\n",
|
||||
" [16599, 'Know How 2', 'DS', ..., 0.0, 0.0, 0.01],\n",
|
||||
" [16600, 'Spirits & Spells', 'GBA', ..., 0.0, 0.0, 0.01]],\n",
|
||||
" dtype=object)"
|
||||
]
|
||||
},
|
||||
"execution_count": 4,
|
||||
"metadata": {},
|
||||
"output_type": "execute_result"
|
||||
}
|
||||
],
|
||||
"source": [
|
||||
"df.values"
|
||||
]
|
||||
}
|
||||
],
|
||||
"metadata": {
|
||||
"kernelspec": {
|
||||
"display_name": "Python 3.8.11 ('base')",
|
||||
"language": "python",
|
||||
"name": "python3"
|
||||
},
|
||||
"language_info": {
|
||||
"codemirror_mode": {
|
||||
"name": "ipython",
|
||||
"version": 3
|
||||
},
|
||||
"file_extension": ".py",
|
||||
"mimetype": "text/x-python",
|
||||
"name": "python",
|
||||
"nbconvert_exporter": "python",
|
||||
"pygments_lexer": "ipython3",
|
||||
"version": "3.8.13"
|
||||
},
|
||||
"orig_nbformat": 4,
|
||||
"vscode": {
|
||||
"interpreter": {
|
||||
"hash": "5819c1eaf6d552792a1bbc5e8998e6c2149ab26a1973a0d78107c0d9954e5ba0"
|
||||
}
|
||||
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|
||||
},
|
||||
"nbformat": 4,
|
||||
"nbformat_minor": 2
|
||||
}
|
||||
16599
TestModel/vgsales.csv
Normal file
16599
TestModel/vgsales.csv
Normal file
File diff suppressed because it is too large
Load Diff
Reference in New Issue
Block a user