Primer Markdown
BASE IRIS
## 'data.frame': 150 obs. of 5 variables:
## $ Sepal.Length: num 5.1 4.9 4.7 4.6 5 5.4 4.6 5 4.4 4.9 ...
## $ Sepal.Width : num 3.5 3 3.2 3.1 3.6 3.9 3.4 3.4 2.9 3.1 ...
## $ Petal.Length: num 1.4 1.4 1.3 1.5 1.4 1.7 1.4 1.5 1.4 1.5 ...
## $ Petal.Width : num 0.2 0.2 0.2 0.2 0.2 0.4 0.3 0.2 0.2 0.1 ...
## $ Species : Factor w/ 3 levels "setosa","versicolor",..: 1 1 1 1 1 1 1 1 1 1 ...## autoEDA | Setting color theme
## autoEDA | Removing constant features
## autoEDA | 0 constant features removed
## autoEDA | 0 zero spread features removed
## autoEDA | Removing features containing majority missing values
## autoEDA | 0 majority missing features removed
## autoEDA | Cleaning data
## autoEDA | Correcting sparse categorical feature levels
## autoEDA | Performing univariate analysis
## autoEDA | Visualizing data
## Feature Observations FeatureClass FeatureType PercentageMissing
## 1 Sepal.Length 150 numeric Continuous 0
## 2 Sepal.Width 150 numeric Continuous 0
## 3 Petal.Length 150 numeric Continuous 0
## 4 Petal.Width 150 numeric Continuous 0
## 5 Species 150 character Categorical 0
## PercentageUnique ConstantFeature ZeroSpreadFeature LowerOutliers
## 1 23.33 No No 0
## 2 15.33 No No 1
## 3 28.67 No No 0
## 4 14.67 No No 0
## 5 2.00 No No 0
## UpperOutliers ImputationValue MinValue FirstQuartile Median Mean Mode
## 1 0 5.8 4.3 5.1 5.80 5.84 5
## 2 3 3 2.0 2.8 3.00 3.06 3
## 3 0 4.35 1.0 1.6 4.35 3.76 1.4
## 4 0 1.3 0.1 0.3 1.30 1.20 0.2
## 5 0 SETOSA 0.0 0.0 0.00 0.00 SETOSA
## ThirdQuartile MaxValue LowerOutlierValue UpperOutlierValue
## 1 6.4 7.9 3.15 8.35
## 2 3.3 4.4 2.05 4.05
## 3 5.1 6.9 -3.65 10.35
## 4 1.8 2.5 -1.95 4.05
## 5 0.0 0.0 0.00 0.00BASE MIS_DATASETS
## autoEDA | Setting color theme
## autoEDA | Removing constant features
## autoEDA | 0 constant features removed
## autoEDA | 0 zero spread features removed
## autoEDA | Removing features containing majority missing values
## autoEDA | 0 majority missing features removed
## autoEDA | Cleaning data
## autoEDA | Correcting sparse categorical feature levels
## autoEDA | Performing univariate analysis
## autoEDA | Visualizing data
## Feature Observations FeatureClass FeatureType PercentageMissing
## 1 customerID 10 character Categorical 0
## 2 MonthlyCharges 10 character Categorical 30
## 3 TotalCharges 10 character Categorical 30
## 4 PaymentMethod 10 character Categorical 30
## 5 Churn 10 character Categorical 0
## PercentageUnique ConstantFeature ZeroSpreadFeature LowerOutliers
## 1 100 No No 0
## 2 80 No No 0
## 3 80 No No 0
## 4 50 No No 0
## 5 20 No No 0
## UpperOutliers ImputationValue MinValue FirstQuartile Median Mean
## 1 0 1452-KIOVK 0 0 0 0
## 2 0 MISSING 0 0 0 0
## 3 0 MISSING 0 0 0 0
## 4 0 MISSING 0 0 0 0
## 5 0 NO 0 0 0 0
## Mode ThirdQuartile MaxValue LowerOutlierValue UpperOutlierValue
## 1 1452-KIOVK 0 0 0 0
## 2 104.8 0 0 0 0
## 3 108.15 0 0 0 0
## 4 ELECTRONIC CHECK 0 0 0 0
## 5 NO 0 0 0 0