1. Describe the steps of the k-Means clustering algorithm. What does the ‘k’ represent?

Pertanyaan:

Describe the steps of the k-Means clustering algorithm. What does the ‘k’ represent?

Status: Complete

Keterangan:

K-Means is a machine learning algorithm used to group data based on similar characteristics. First, we determine the number of clusters (k) and select initial centroids as the cluster centers. Next, each data point is assigned to the nearest centroid. The algorithm then recalculates the centroids based on the average values of the data in each cluster. This process repeats until the centroids no longer change significantly.

The letter k represents the number of clusters to be created. For example, if k = 3, the data will be divided into three groups.

Bukti: Scikit-learn – K-Means Clustering

scikit-learn 1.8.0 documentation

2. Why is dimensionality reduction useful in machine learning?

Pertanyaan:

Why is dimensionality reduction useful in machine learning?

Status: Complete

Keterangan:

Dimensionality reduction is a technique used to reduce the number of features in a dataset while preserving important information. It is useful because datasets with too many features can require more processing time and make models more complex. Reducing the number of dimensions can help speed up computation, simplify data analysis, and make data easier to visualize.

For example, a business can simplify customer data by selecting or transforming many features into a smaller set of meaningful variables.

Bukti: Scikit-learn – Unsupervised Dimensionality Reduction

sklearn

3. Provide a real-world application for clustering (e.g., customer segmentation).

Pertanyaan:

Provide a real-world application for clustering.

Status: Complete

Keterangan:

One real-world application of clustering is customer segmentation in digital business. Companies can group customers based on shopping habits, purchase frequency, spending levels, and product preferences. For example, customers can be divided into groups such as frequent buyers, occasional buyers, and customers who prefer discounted products.

This helps businesses develop more relevant promotions, improve customer experiences, and create marketing strategies that match each group’s needs.

Bukti: Scikit-learn – Clustering

scikit-learn 1.8.0 documentation

4. What is the main objective of PCA?

Pertanyaan:

What is the main objective of PCA?

Status: Complete

Keterangan:

Principal Component Analysis (PCA) is a dimensionality reduction technique used to simplify datasets while preserving as much important information as possible. Its main objective is to transform the original features into a smaller number of new variables called principal components. These components capture as much variation in the original data as possible.

For example, PCA can reduce a dataset with many features into two principal components, making the data easier to visualize and analyze.

Bukti: Scikit-learn – Principal Component Analysis (PCA)

sklearn