Python Program to Handle Missing Values in Data

Handle Missing Values in Data using Machine Learning

Missing values are a common occurrence in datasets and it is important to handle them appropriately before using the data for machine learning tasks. In this blog post we will discuss various techniques for handling missing values in data using machine learning.

What are Missing Values?

Missing values are values that are not present in the dataset for certain variables. They can occur for a variety of reasons such as data collection errors or intentional data masking. Missing values can cause problems in machine learning tasks because they can lead to biased or inaccurate models.

Techniques for Handling Missing Values

There are several techniques for handling missing values in data using machine learning. Here are a few commonly used methods:

1. Deletion: This method involves simply deleting the rows or columns that contain missing values. While this method is easy to implement it can result in loss of valuable data.
2. Imputation: This method involves filling in the missing values with estimated values based on the available data. There are several imputation techniques including mean imputation, median imputation, and regression imputation.
3. Advanced Imputation: This method involves using advanced machine learning techniques to impute missing values. Examples include k-Nearest Neighbors (k-NN) imputation and Expectation-Maximization (EM) imputation.

Let’s look at an example of implementing these methods using Python.

Python Program to Handle Missing Values

We will use the scikit-learn library in Python to implement the above methods.

First, let’s import the necessary libraries:

import pandas as pd
import numpy as np
from sklearn.impute import SimpleImputer
from sklearn.neighbors import KNeighborsRegressor

Next, let’s create a sample dataset with missing values:

data = {'A': [1, 2, np.nan, 4, 5], 'B': [6, np.nan, 8, np.nan, 10], 'C': [11, 12, 13, 14, np.nan]}
df = pd.DataFrame(data)

Now, let’s implement the three methods we discussed earlier.

Deletion:

# Drop rows with missing values
df.dropna(inplace=True)

Imputation using mean:

# Fill missing values with mean
imputer = SimpleImputer(strategy='mean')
df_imputed = pd.DataFrame(imputer.fit_transform(df), columns=df.columns)

Advanced Imputation using k-NN:

# Fill missing values using k-NN
imputer = KNeighborsRegressor(n_neighbors=2)
df_imputed_knn = pd.DataFrame(imputer.fit_transform(df), columns=df.columns)

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Conclusion

Missing values are a common problem in datasets but they can be handled effectively using various techniques. In this blog post we discussed three commonly used methods for handling missing values in data using machine learning: deletion, imputation, and advanced imputation. We also provided an example of implementing these methods using Python and the scikit-learn library. By handling missing values appropriately we can ensure that our machine learning models are accurate and unbiased.

For More Information:

https://www.datacamp.com/tutorial/techniques-to-handle-missing-data-values