Content-Based Car Recommendation System using K-Nearest Neighbors

This project is a simple content-based recommender system that suggests cars to a customer based on their preferences for features like mileage (mpg), displacement, horsepower, and weight.

🔍 Goal

To recommend cars that are most similar to a given car using the K-Nearest Neighbors algorithm.

📊 Dataset

The dataset used is mtcars.csv, which contains specifications of various car models.

🛠 Features Used

• Miles per Gallon (mpg)
• Displacement
• Horsepower
• Weight

🤖 Algorithm

• NearestNeighbors from scikit-learn
• Finds the most similar cars using Euclidean distance between feature vectors.

📚 Reference

This project is based on the following course: LinkedIn Learning - Building a Recommendation System with Python Machine Learning and AI

✅ Output

The notebook predicts cars similar to a given input and prints the top 3 most similar cars as recommendations.

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Feel free to explore and improve!