# Missing data imputation with fancyimpute

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

1. KNN or K-Nearest Neighbor
2. 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.]]
```

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