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Missing Value Imputation with Python and K-Nearest Neighbors

Missing value imputation isn’t that difficult of a task to do. Methods range from simple mean imputation and complete removing of the observation to more advanced techniques like MICE. Nowadays, the more challenging task is to choose which method to use. Today we’ll explore one simple but highly effective way to impute missing data – the KNN algorithm.

KNN stands for K-Nearest Neighbors, a simple algorithm that makes predictions based on a defined number of nearest neighbors. It calculates distances from an instance you want to classify to every other instance in the training set. You can learn how to implement it from scratch here:

Let’s Make a KNN Classifier from Scratch

We won’t use the algorithm for classification purposes but to fill missing values, as the title suggests. The article will use the housing prices dataset, a simple and well-known one with just over 500 entries. You can download it here.

The article is structured as follows:

  • Dataset loading and exploration
  • KNN imputation
  • Imputer optimization
  • Conclusion

Dataset loading and exploration

As mentioned previously, you can download the housing dataset from this link. Also, make sure you have both Numpy and Pandas imported. This is how the first couple of rows look:

First rows

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By default, the dataset is very low on missing values – only five of them in a single attribute:

Initial missing values

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Let’s change that. It’s not something you would typically do, but we need a bit more of missing values. To start, let’s create two arrays of random numbers, ranging from 1 to the length of the dataset. The first array has 35 elements, and the second has 20 (arbitrary choice):

i1 = np.random.choice(a=df.index, size=35)
i2 = np.random.choice(a=df.index, size=20)

Here’s how the first array looks like:

Random array

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Your array will be different because the randomization process is, well, random. Next, we will replace existing values at particular indices with NANs. Here’s how:

df.loc[i1, 'INDUS'] = np.nan
df.loc[i2, 'TAX'] = np.nan

Let’s now check again for missing values – this time, the count is different:

Added missing values

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That’s all we need to begin with imputation. Let’s do that in the next section.

KNN imputation

The entire imputation boils down to 4 lines of code – one of which is library import. We need KNNImputer from sklearn.impute and then make an instance of it in a well-known Scikit-Learn fashion. The class expects one mandatory parameter – n_neighbors. It tells the imputer what’s the size of the parameter K.

To start, let’s choose an arbitrary number of 3. We’ll optimize this parameter later, but 3 is good enough to start. Next, we can call the fit_transform method on our imputer to impute missing data.

Finally, we’ll convert the resulting array into a pandas.DataFrame object for easier interpretation. Here’s the code:

from sklearn.impute import KNNImputer

imputer = KNNImputer(n_neighbors=3)
imputed = imputer.fit_transform(df)
df_imputed = pd.DataFrame(imputed, columns=df.columns)

Wasn’t that easy? Let’s check for missing values now:


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As expected, there aren’t any. Still, one question remains – how do we pick the right value for K?

Imputer optimization

This housing dataset is aimed towards predictive modeling with regression algorithms, as the target variable is continuous (MEDV). It means we can train many predictive models where missing values are imputed with different values for K and see which one performs the best.

But first, the imports. We need a couple of things from Scikit-Learn – to split the dataset into training and testing subsets, train the model, and validate it. We’ve chosen the Random Forests algorithm for training, but the decision is once again arbitrary. RMSE was used for the validation:

from sklearn.model_selection import train_test_split
from sklearn.ensemble import RandomForestRegressor
from sklearn.metrics import mean_squared_error

rmse = lambda y, yhat: np.sqrt(mean_squared_error(y, yhat))

Here are the steps necessary to perform the optimization:

  1. Iterate over the possible range for K – all odd numbers between 1 and 20 will do
  2. Perform the imputation with the current K value
  3. Split the dataset into training and testing subsets
  4. Fit the Random Forests model
  5. Predict on the test set
  6. Evaluate using RMSE

It sounds like a lot, but it boils down to around 15 lines of code. Here’s the snippet:

def optimize_k(data, target):
    errors = []
    for k in range(1, 20, 2):
        imputer = KNNImputer(n_neighbors=k)
        imputed = imputer.fit_transform(data)
        df_imputed = pd.DataFrame(imputed, columns=df.columns)
        X = df_imputed.drop(target, axis=1)
        y = df_imputed[target]
        X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)

        model = RandomForestRegressor(), y_train)
        preds = model.predict(X_test)
        error = rmse(y_test, preds)
        errors.append({'K': k, 'RMSE': error})
    return errors

We can now call the optimize_k function with our modified dataset (missing values in 3 columns) and pass in the target variable (MEDV):

k_errors = optimize_k(data=df, target='MEDV')

And that’s it! The k_errors array looks like this:

RMSE values

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Or, represented visually:

K value chart

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It looks like K=15 is the optimal value in the given range, as it resulted in the smallest error. We won’t cover the interpretation of the error, as it’s beyond this article’s scope. Let’s wrap things up in the next section.

Parting words

Missing data imputation is easy, at least the coding part. It’s the reasoning that makes it hard – understanding which attributes should and which shouldn’t be imputed. For example, maybe some values are missing because a customer isn’t using that type of service, making no sense to perform an imputation.

Consulting with a domain expert and studying the domain is always a way to go. The actual coding is easy.

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Dario Radečić
Data scientist, blogger, and enthusiast. Passionate about deep learning, computer vision, and data-driven decision making.

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