First : Processing the dataset

We will not go deep in processing the dataset, all we want to do is getting the dataset ready to be fed into our models .

We will get rid of any features with missing values, then we will encode the categorical features, that’s it.

Load the dataset :

• Load train and test data into pandas DataFrames
• Combine train and test data to process them together

combined.describe()

let’s define a function to get the columns that don’t have any missing values

Get the columns that do not have any missing values .

Let’s see how many columns we got

[out]:Number of numerical columns with no nan values : 25 Number of nun-numerical columns with no nan values : 20

The correlation between the features

From the correlation heat map above, we see that about 15 features are highly correlated with the target.

One Hot Encode The Categorical Features :

We will encode the categorical features using one hot encoding.

[out]:There were 45 columns before encoding categorical features There are 149 columns after encoding categorical features

Now, split back combined dataFrame to training data and test data