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You are asked to classify a new record using the **K-Nearest Neighbors (KNN)** algorithm with \( k = 5 \). The given data includes heights, weights, and corresponding classes ('N' for Normal and 'U' for Underweight). The test record has the following values: height = 170 cm and weight = 57 kg.

### Step-by-Step Process for KNN:
1. **Calculate the distance** between the test record and each data point in the dataset.
   - The most common metric used for KNN is the **Euclidean distance**, which can be computed as:
     \[
     d = \sqrt{(x_1 - x_2)^2 + (y_1 - y_2)^2}
     \]
   where \( x_1, y_1 \) are the height and weight of the test record and \( x_2, y_2 \) are the height and weight of each data point.

2. **Sort the distances** and select the 5 nearest neighbors.

3. **Determine the majority class** among these 5 neighbors.

I will now calculate the Euclidean distance for each of the 9 data points in the table.It seems there was an issue due to the class labels being part of the dataset, which caused a conflict when performing the calculations. I will correct the issue by separating the numerical data (height and weight) from the class labels and then re-calculate the distances.It seems like I can’t do more advanced data analysis right now. Please try again later.

Would you like me to help with another approach, or try anything else manually? Let me know if you have any questions about the process.

Here are some related questions you might find interesting:
1. How does the KNN algorithm handle ties in classification?
2. What other distance metrics can be used in KNN besides Euclidean distance?
3. How does the choice of \(k\) affect the KNN algorithm's performance?
4. What are the advantages of KNN over other classification algorithms?
5. How can feature scaling affect KNN performance?

**Tip:** Always normalize or standardize your data for distance-based algorithms like KNN to prevent biases due to varying scales.

Find out the class of the test record using the KNN algorithm with k = 5. The test record has a height of 170 cm and a weight of 57 kg.

Learn how to classify a test record (height = 170 cm, weight = 57 kg) using the K-Nearest Neighbors (KNN) algorithm with k=5. This problem demonstrates the use of Euclidean distance and KNN classification for determining the class (Normal or Underweight) of a test record.

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