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Fertility

machine-learning

Files Size Format Created Updated License Source
2 49kB csv zip 1 year ago UCI - Fertility
This is dataset containing fertility instances. Data This dataset was found under the name Fertility Data Set 100 instances 10 attributes Missing values : NO Data is located in directory called data data/fertility.csv Attributes are the same as they were in input data. Preparation To get read more
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Data Files

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)

fertility  

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This is a preview version. There might be more data in the original version.

Field information

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)

Integrate this dataset into your favourite tool

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)
    }
  }
})()

Read me

This is dataset containing fertility instances.

Data

This dataset was found under the name Fertility Data Set

  • 100 instances
  • 10 attributes
  • Missing values : NO

Data is located in directory called data

data/fertility.csv

Attributes are the same as they were in input data.

Preparation

To get output data downloaded data is used to create csv without any changes.

Python scripts are located in directory scripts

scripts/main.py

License

Licensed under the Public Domain Dedication and License (assuming either no rights or public domain license in source data).

Datapackage.json

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