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Try It Now!Files | Size | Format | Created | Updated | License | Source |
---|---|---|---|---|---|---|
2 | 49kB | csv zip | 5 years ago | UCI - Fertility |
Download files in this dataset
File | Description | Size | Last changed | Download |
---|---|---|---|---|
fertility | 3kB | csv (3kB) , json (17kB) | ||
fertility_zip | Compressed versions of dataset. Includes normalized CSV and JSON data with original data and datapackage.json. | 5kB | zip (5kB) |
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This is a preview version. There might be more data in the original version.
Field Name | Order | Type (Format) | Description |
---|---|---|---|
season | 1 | number (default) | Season in which the analysis was performed. 1) winter, 2) spring, 3) Summer, 4) fall. (-1, -0.33, 0.33, 1) |
age | 2 | number (default) | Age at the time of analysis. 18-36 (0, 1) |
childish-disease | 3 | integer (default) | Childish diseases (ie, chicken pox, measles, mumps, polio) 1) yes, 2) no. (0, 1) |
trauma | 4 | integer (default) | Accident or serious trauma 1) yes, 2) no. (0, 1) |
surgical-intervention | 5 | integer (default) | Surgical intervention 1) yes, 2) no. (0, 1) |
fevers | 6 | integer (default) | High fevers in the last year 1) less than three months ago, 2) more than three months ago, 3) no. (-1, 0, 1) |
alcoholic | 7 | number (default) | Frequency of alcohol consumption 1) several times a day, 2) every day, 3) several times a week, 4) once a week, 5) hardly ever or never (0, 1) |
smoking | 8 | integer (default) | Smoking habit 1) never, 2) occasional 3) daily. (-1, 0, 1) |
sitting | 9 | number (default) | Number of hours spent sitting per day ene-16 (0, 1) |
output | 10 | string (default) | Output: Diagnosis normal (N), altered (O) |
Use our data-cli tool designed for data wranglers:
data get https://datahub.io/machine-learning/fertility
data info machine-learning/fertility
tree machine-learning/fertility
# Get a list of dataset's resources
curl -L -s https://datahub.io/machine-learning/fertility/datapackage.json | grep path
# Get resources
curl -L https://datahub.io/machine-learning/fertility/r/0.csv
curl -L https://datahub.io/machine-learning/fertility/r/1.zip
If you are using R here's how to get the data you want quickly loaded:
install.packages("jsonlite", repos="https://cran.rstudio.com/")
library("jsonlite")
json_file <- 'https://datahub.io/machine-learning/fertility/datapackage.json'
json_data <- fromJSON(paste(readLines(json_file), collapse=""))
# get list of all resources:
print(json_data$resources$name)
# print all tabular data(if exists any)
for(i in 1:length(json_data$resources$datahub$type)){
if(json_data$resources$datahub$type[i]=='derived/csv'){
path_to_file = json_data$resources$path[i]
data <- read.csv(url(path_to_file))
print(data)
}
}
Note: You might need to run the script with root permissions if you are running on Linux machine
Install the Frictionless Data data package library and the pandas itself:
pip install datapackage
pip install pandas
Now you can use the datapackage in the Pandas:
import datapackage
import pandas as pd
data_url = 'https://datahub.io/machine-learning/fertility/datapackage.json'
# to load Data Package into storage
package = datapackage.Package(data_url)
# to load only tabular data
resources = package.resources
for resource in resources:
if resource.tabular:
data = pd.read_csv(resource.descriptor['path'])
print (data)
For Python, first install the `datapackage` library (all the datasets on DataHub are Data Packages):
pip install datapackage
To get Data Package into your Python environment, run following code:
from datapackage import Package
package = Package('https://datahub.io/machine-learning/fertility/datapackage.json')
# print list of all resources:
print(package.resource_names)
# print processed tabular data (if exists any)
for resource in package.resources:
if resource.descriptor['datahub']['type'] == 'derived/csv':
print(resource.read())
If you are using JavaScript, please, follow instructions below:
Install data.js
module using npm
:
$ npm install data.js
Once the package is installed, use the following code snippet:
const {Dataset} = require('data.js')
const path = 'https://datahub.io/machine-learning/fertility/datapackage.json'
// We're using self-invoking function here as we want to use async-await syntax:
;(async () => {
const dataset = await Dataset.load(path)
// get list of all resources:
for (const id in dataset.resources) {
console.log(dataset.resources[id]._descriptor.name)
}
// get all tabular data(if exists any)
for (const id in dataset.resources) {
if (dataset.resources[id]._descriptor.format === "csv") {
const file = dataset.resources[id]
// Get a raw stream
const stream = await file.stream()
// entire file as a buffer (be careful with large files!)
const buffer = await file.buffer
// print data
stream.pipe(process.stdout)
}
}
})()
This is dataset containing fertility instances.
This dataset was found under the name Fertility Data Set
Data is located in directory called data
data/fertility.csv
Attributes are the same as they were in input data.
To get output data downloaded data is used to create csv without any changes.
Python scripts are located in directory scripts
scripts/main.py
Licensed under the Public Domain Dedication and License (assuming either no rights or public domain license in source data).
Notifications of data updates and schema changes
Warranty / guaranteed updates
Workflow integration (e.g. Python packages, NPM packages)
Customized data (e.g. you need different or additional data)
Or suggest your own feature from the link below