The Great Ketchup Debate Solved: Using Machine Learning to Predict Condiment Preferences

Photo by Jessica Loaiza on Unsplash

Are you tired of the never-ending debate of whether to put ketchup or mustard on your hot dog? Well, with the power of Machine Learning and Python, we can finally settle this age-old argument once and for all!

Before we begin, make sure you have the following libraries installed: numpy, pandas, and scikit-learn. If you don’t have these libraries installed, you can install them by running !pip install numpy pandas scikit-learn in your command line.

We’ll be using a dataset of people and their condiment preferences, such as their favorite type of hot dog, their preferred bun toasting method, and their age. Here’s an example of how to import the data:

data = pd.read_csv("condiment_preferences.csv")
train_data = data.sample(frac=0.8, random_state=1)
test_data = data.drop(train_data.index)

Next, we’ll use the trusty DecisionTreeClassifier algorithm to create our model. This algorithm is perfect for this task because it can handle both categorical and numerical data, just like a good hot dog can handle both ketchup and mustard.

from sklearn.tree import DecisionTreeClassifier

# Create a decision tree classifier
clf = DecisionTreeClassifier()

# Train the classifier with our data…