Neural Network Projects with Python

Imputing missing values

As discussed earlier, imputing missing values is an essential part of the machine learning workflow. Real-world datasets are messy and usually contain missing values. Most machine learning models such as neural networks are unable to work with missing data, and hence we have to preprocess the data before we feed the data into our models. pandas makes it easy to handle missing values.

Let's use the Iris dataset from earlier. The Iris dataset does not have any missing values by default. Therefore, we have to delete some values on purpose for the sake of this exercise. The following code randomly selects 10 rows in the dataset, and deletes the sepal_length values in these 10 rows:

import numpy as npimport pandas as pd ...