Encoding Categorical Variables with One-Hot Encoding in Python

One-hot encoding is a popular technique used for encoding categorical variables into numerical values that can be used by machine learning models. It is particularly useful when dealing with categorical variables that have a large number of unique values, as it creates a sparse matrix that is more memory-efficient than other encoding techniques. In this tutorial, we will walk through how to encode categorical variables with one-hot encoding in Python.

Step 1: Importing Libraries

To use one-hot encoding in Python, we need to import the necessary libraries. We will use the pandas library for data manipulation and the sklearn library for one-hot encoding.

import pandas as pd
from sklearn.preprocessing import OneHotEncoder

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Step 2: Loading Data

Next, we will load a sample dataset to work with. For this tutorial, we will use the titanic dataset from the seaborn library, which contains information about passengers on the Titanic ship. We will load the…