training drug rating data
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| uniqueID,drugName,condition,rating,date,usefulCount,year | |
| 163740,Mirtazapine,Depression,10,28-Feb-12,22,2012 | |
| 206473,Mesalamine,"Crohn's Disease, Maintenance",8,17-May-09,17,2009 | |
| 159672,Bactrim,Urinary Tract Infection,9,29-Sep-17,3,2017 | |
| 39293,Contrave,Weight Loss,9,5-Mar-17,35,2017 | |
| 97768,Cyclafem 1 / 35,Birth Control,9,22-Oct-15,4,2015 | |
| 208087,Zyclara,Keratosis,4,3-Jul-14,13,2014 | |
| 215892,Copper,Birth Control,6,6-Jun-16,1,2016 | |
| 169852,Amitriptyline,Migraine Prevention,9,21-Apr-09,32,2009 | |
| 23295,Methadone,Opiate Withdrawal,7,18-Oct-16,21,2016 |
aishwarya8615
/ Minimum_Wage_Data.csv
Last active
September 18, 2019 22:21
US Minimum Wage by State from 1968 to 2017 and 2018 Equivalent Dollars
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| Year | State | HighValue | LowValue | CPIAverage | High2018 | Low2018 | |
|---|---|---|---|---|---|---|---|
| 1968 | Alabama | 0 | 0 | 34.78333333 | 0 | 0 | |
| 1968 | Alaska | 2.1 | 2.1 | 34.78333333 | 15.12 | 15.12 | |
| 1968 | Arizona | 0.66 | 0.468 | 34.78333333 | 4.75 | 3.37 | |
| 1968 | Arkansas | 0.15625 | 0.15625 | 34.78333333 | 1.12 | 1.12 | |
| 1968 | California | 1.65 | 1.65 | 34.78333333 | 11.88 | 11.88 | |
| 1968 | Colorado | 1.25 | 1 | 34.78333333 | 9 | 7.2 | |
| 1968 | Connecticut | 1.4 | 1.4 | 34.78333333 | 10.08 | 10.08 | |
| 1968 | Delaware | 1.25 | 1.25 | 34.78333333 | 9 | 9 | |
| 1968 | District of Columbia | 1.4 | 1.25 | 34.78333333 | 10.08 | 9 |
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| Country,Continent,Year,Status,Life_expectancy ,Adult_Mortality,infant_deaths,Alcohol,percentage_expenditure,Hepatitis_B,Measles , BMI ,under_five_deaths ,Polio,Total_expenditure,Diphtheria , HIV/AIDS,GDP,Population, thinness 1-19 years, thinness 5-9 years,Income_composition_of_resources,Schooling | |
| Afghanistan,Asia,2015,Developing,65,263,62,0.01,71.27962362,65,1154,19.1,83,6,8.16,65,0.1,584.25921,33736494,17.2,17.3,0.479,10.1 | |
| Afghanistan,Asia,2014,Developing,59.9,271,64,0.01,73.52358168,62,492,18.6,86,58,8.18,62,0.1,612.696514,327582,17.5,17.5,0.476,10 | |
| Afghanistan,Asia,2013,Developing,59.9,268,66,0.01,73.21924272,64,430,18.1,89,62,8.13,64,0.1,631.744976,31731688,17.7,17.7,0.47,9.9 | |
| Afghanistan,Asia,2012,Developing,59.5,272,69,0.01,78.1842153,67,2787,17.6,93,67,8.52,67,0.1,669.959,3696958,17.9,18,0.463,9.8 | |
| Afghanistan,Asia,2011,Developing,59.2,275,71,0.01,7.097108703,68,3013,17.2,97,68,7.87,68,0.1,63.537231,2978599,18.2,18.2,0.454,9.5 | |
| Afghanistan,Asia,2010,Developing,58.8,279,74,0.01,79.67936736,66,1989,16.7,102 |
Dataset Source: Healthcare Dataset Stroke Data from Kaggle.
This dataset is used to predict whether a patient is likely to get stroke based on the input parameters like gender, age, and various diseases and smoking status. A subset of the original train data is taken using the filtering method for Machine Learning and Data Visualization purposes.
About the Data: Each row in the data provides relavant information about a person , for instance; age, gender,smoking status, occurance of stroke in addition to other information Unknown in Smoking status means the information is unavailable. N/A in other input fields imply that it is not applicable.