🔍 Career Path Predictor: Easy KNN Guide!

Building a Career Path Predictor with K-Nearest Neighbors

In this post, we’re diving into a fun and friendly exploration of a Python code snippet that predicts career paths based on your interests, skills, and goals using machine learning.

We'll break it down step-by-step so even beginners can follow along with ease!

🧠 Understanding the Data

The code starts with some sample data:

data = [

    ['technology', 'coding', 'problem-solving', 'Software Engineer'],

    ['art', 'creativity', 'design', 'Graphic Designer'],

    ['math', 'statistics', 'analysis', 'Data Scientist']

]

This data contains a list of career paths, each described by three features: interests, skills, and goals. The last item in each list is the career path.

For example, if you’re into technology, coding, and problem-solving, the model might suggest that you’d make a great Software Engineer.

🎛️ Encoding the Data

Machine learning algorithms like K-Nearest Neighbors (KNN) work best with numbers, but our data is made up of words!

That’s where LabelEncoder comes in. LabelEncoder transforms these words into numbers so the algorithm can process them.

from sklearn.preprocessing import LabelEncoder

# Initialize LabelEncoders for each feature column

encoders = [LabelEncoder() for _ in range(len(X[0]))]

# Transform each column in X

X_encoded = []

for col, encoder in zip(zip(*X), encoders):

    X_encoded.append(encoder.fit_transform(col))

# Transpose back to original shape

X_encoded = list(zip(*X_encoded))

Here's what's happening:

1. LabelEncoder is created for each feature (interests, skills, goals).

2. Each feature column is transformed from words into numbers.

3. The result is an encoded dataset that the model can work with.

🧑‍🔧 Training the Model

Now that we have our data in numeric form, it's time to train the KNN model.

from sklearn.neighbors import KNeighborsClassifier

# Train the model

model = KNeighborsClassifier(n_neighbors=1)

model.fit(X_encoded, y_encoded)

The KNN algorithm is like a helpful neighbor who gives advice based on what they’ve seen before.

If you tell them what you like, they’ll look at similar people (in this case, career paths) and make a suggestion.

In this example, we set n_neighbors=1, meaning the model will find the single closest match.

🔮 Making Predictions

Once the model is trained, you can predict a career path based on new input:

def predict_career(model, encoders, label_encoder, user_input):

    user_input_encoded = [encoder.transform([feature])[0] for feature, encoder in zip(user_input, encoders)]

    predicted_label = model.predict([user_input_encoded])[0]

    # Decode the predicted label back to the career path

    return label_encoder.inverse_transform([predicted_label])[0]

Here’s how it works:

1. User Input: You provide your interests, skills, and goals.

2. Encoding: The input is encoded using the same method as before.

3. Prediction: The model predicts a career path based on the input.

4. Decoding: Finally, the numerical prediction is converted back into a career path name.

💡 Example in Action

Let’s see it in action with some example input:

user_input = ['technology', 'coding', 'problem-solving']

predicted_career = predict_career(model, encoders, label_encoder, user_input)

print(predicted_career)

This would print "Software Engineer"—a career that matches your interests!

Final Thoughts

And there you have it! With this post, you've learned how to build a simple career path predictor using Python's KNeighborsClassifier and LabelEncoder.

It's a powerful way to explore how machine learning can make smart suggestions based on your input.

Keep experimenting and have fun with your new AI skills! 🎉

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