Data description

• peak wave height

  

  • Predict maximum significance wave height

  • This is the maximum significance wave height prediction problem using the observations of the Ocean Data Buoy report.
    The Ocean Data Buoy observes marine weather phenomena with various weather equipment at sea level. Depending on the shape, there are two types, ship type and disk type, and Pagoe and Paegui measure and analyze the moving acceleration of the buoyant body at sea level. For the Train data, it consists of daily data from 2014 to 2018, so you can study it to predict test data. The marine weatherman used the data from Ulleungdo Island.

  • Data summary
    point, date, average wind speed (m/s), average air pressure (hPa), average relative humidity (%), average temperature (°C), average water temperature (°C), average maximum wave height(m), average significant wave height (m), maximum wave height (m), average wave period (sec), max wave period (sec), max significant wave height(m)

  • significant wave height: The average of the crests observed during the arbitrary observation time, corresponding to one-third of the total, in the order in which the wave heights are high.
    Maximum wave height: The largest wave height observed during the arbitrary observation time.
    Mean wave height: Mean wave height of the observed wave height during the arbitrary observation time.
    

  • data source

  • source code

• sea ice extent

  

  • Predicts the magnitude of sea ice due to carbon dioxide emissions and unusual factors of carbon dioxide.

  • Every summer, let's look at the magnitude of sea ice due to the ongoing emissions of carbon dioxide and the unusual factors of carbon dioxide, the gases that contribute to the greenhouse effect. There are many factors in carbon dioxide emissions. We've talked about one of the interesting cases that. It is given from 1978 to 1919, when 2016 was omitted, and every year there is data from January to December.

  • Data summary
    year : (int64)
    month : (int64)
    car_reg : number of car registrations (float64)
    cow : the number of cow in the world (float64)
    gdp_ : average amount of GDP (float64)
    Carbon : Carbon emissions (remove number of linearity,seasonality) (float64)
    seaice_extent : sea ice size (float64)

  • data source

    • car_reg
    • cow
    • gdp
    • Carbon
    • seaice_extent

  • source code

• breast cancer diagnosis

  • Predict whether breast cancer is present based on the basis of test results.

  • Data summary
    *The results of the test
    diagnosis(Benign tomor = 0 / Malignant tumor = 1)
    

    *test results for diagnosing breast cancer
    radius_mean,texture_mean,perimeter_mean,area_mean,smoothness_mean,compactness_mean,concavity_mean,concave,points_mean,symmetry_mean,fractal_dimension_mean,radius_se,texture_se,perimeter_se,area_se,smoothness_se,compactness_se,concavity_se,concave points_se,symmetry_se,fractal_dimension_se,radius_worst,texture_worst,perimeter_worst,area_worst,smoothness_worst,compactness_worst,concavity_worst,concave points_worst,symmetry_worst,fractal_dimension_worst

  • source code

• Boston Housing Price

  • Estimate Boston house prices by various factors

  • Data summary
    (0) CRIM per capita crime rate by town (1) ZN proportion of residential land zoned for lots over 25,000 sq.ft. (2) INDUS proportion of non-retail business acres per town (3) CHAS Charles River dummy variable (= 1 if tract bounds river; 0 otherwise) (4) NOX nitric oxides concentration (parts per 10 million) (5) RM average number of rooms per dwelling (6) AGE proportion of owner-occupied units built prior to 1940 (7) DIS weighted distances to five Boston employment centres (8) RAD index of accessibility to radial highways (9) TAX full-value property-tax rate per $10,000 (10) PTRATIO pupil-teacher ratio by town (11) B 1000(Bk - 0.63)^2 where Bk is the proportion of blacks by town (12) LSTAT % lower status of the population (13) MEDV Median value of owner-occupied homes in $1000's (14) Price

  • source code

• Soil pollution

  • Predicting soil pollution levels
    If the levels of Cd, Cu, As, Hg, Pb, Zn and Ni, the major soil pollutants in industrial complexes and factory areas, are above their respective reference averages, the difference shall be considered as pollution levels and the total pollution levels in those areas will be predicted.

  • Data summary
    Area,Cd,Cu,As,Hg,Pb,Zn,Ni,pollution level

  • source code

• Danyang Water quality of sewage

  • Estimate the number of E. coli in water quality using discharge water data at Danyang Sewage Treatment Plant.
    

  • Data summary
    outflow (방류량), temp 수온(℃), 방류수질(pH(-)), 방류수질(BOD(㎎/L)), 방류수질(COD(㎎/L)), 방류수질(SS(㎎/L)), 방류수질(T-N(㎎/L)), 방류수질(T-P(㎎/L)), Escherichia coli(개/㎖)

  • data source

  • source code