In a real world dataset, there will always be some data missing. This mainly associates with how the data was collected. Missing data plays an important role creating a predictive model, because there are algorithms which does not perform very well with missing dataset.
Fancyimput
fancyimpute is a library for missing data imputation algorithms. Fancyimpute use machine learning algorithm to impute missing values. Fancyimpute uses all the column to impute the missing values. There are two ways missing data can be imputed using Fancyimpute
- KNN or K-Nearest Neighbor
- MICE or Multiple Imputation by Chained Equation
K-Nearest Neighbor
To fill out the missing values KNN finds out the similar data points among all the features. Then it took the average of all the points to fill in the missing values.
Python3
import pandas as pd import numpy as np # importing the KNN from fancyimpute library from fancyimpute import KNN df = pd.DataFrame([[np.nan, 2, np.nan, 0], [3, 4, np.nan, 1], [np.nan, np.nan, np.nan, 5], [np.nan, 3, np.nan, 4], [5, 7, 8, 2], [2, 5, 7, 9]], columns = list('ABCD')) # printing the dataframe print(df) # calling the KNN class knn_imputer = KNN() # imputing the missing value with knn imputer df = knn_imputer.fit_transform(df) # printing dataframe print(df) |
Output:
A B C D 0 NaN 2.0 NaN 0 1 3.0 4.0 NaN 1 2 NaN NaN NaN 5 3 NaN 3.0 NaN 4 4 5.0 7.0 8.0 2 5 2.0 5.0 7.0 9 Imputing row 1/6 with 2 missing, elapsed time: 0.001 [[3.23556938 2. 7.75630267 0.] [3. 4. 7.825 1.] [3.67647071 3.46386587 7.64000033 5.] [3.35514006 3. 7.59183674 4.] [5. 7. 8. 2.] [2. 5. 7. 9.]]
Multiple Imputation by Chained Equation:
MICE uses multiple imputation instead of single imputation which results in statistical uncertainty. MICE perform multiple regression over the sample data and take averages of them
Python3
import pandas as pd import numpy as np # importing the MICE from fancyimpute library from fancyimpute import IterativeImputer df = pd.DataFrame([[np.nan, 2, np.nan, 0], [3, 4, np.nan, 1], [np.nan, np.nan, np.nan, 5], [np.nan, 3, np.nan, 4], [5, 7, 8, 2], [2, 5, 7, 9]], columns = list('ABCD')) # printing the dataframe print(df) # calling the MICE class mice_imputer = IterativeImputer() # imputing the missing value with mice imputer df = mice_imputer.fit_transform(df) # printing dataframe print(df) |
Output
A B C D 0 NaN 2.0 NaN 0 1 3.0 4.0 NaN 1 2 NaN NaN NaN 5 3 NaN 3.0 NaN 4 4 5.0 7.0 8.0 2 5 2.0 5.0 7.0 9 [[3.27262261 2. 7.9809332 0 ] [3. 4. 7.9193547 1.] [2.91717117 4.35730239 7.47523962 5.] [2.77722048 3. 7.53760743 4.] [5. 7. 8. 2.] [2. 5. 7. 9.]]
