Data preprocessing

Recall from the previous project that we had to preprocess the data by removing missing values and other data anomalies. In this project, we'll perform the same process. We'll also perform feature engineering to improve both the quality and quantity of the features before training our neural network on it.

Handling missing values and data anomalies

Let's do a check to see whether there are any missing values in our dataset:

print(df.isnull().sum())

We'll see the following output showing the number of missing values in each column:

We can see that there are only five rows (out of 500,000 rows) with missing data. With a missing data percentage of just 0.001%, it seems that we don&apos...