Predictive Analytics with TensorFlow 5.1: Using K-means for Predictive Analytics

The video tutorial introduces unsupervised learning, focusing on clustering techniques like K-Means. It explains how these techniques group data without labels, using real-world examples such as organizing music files. The K-Means algorithm is detailed, including its iterative process and mathematical operations. A practical example using the Saratoga homes dataset demonstrates clustering for neighborhood prediction. The tutorial concludes with optimizing K-Means using the elbow method to determine the optimal number of clusters.

What is the primary goal of unsupervised learning?

• To label data points

• To find hidden patterns in unlabeled data

• To predict future data points

• To increase data storage

Which of the following is NOT a use of clustering?

• Anomaly detection

• Data labeling

• Social network analysis

• Real estate data analysis

What is the main objective of the K-Means algorithm?

• To label data points

• To minimize the distance between data points and their cluster centroids

• To increase the number of clusters

• To maximize the distance between data points

In the K-Means algorithm, what is updated in each iteration?

• The labels of the data points

• The centroids of the clusters

• The data points

• The number of clusters

What is the purpose of using the Saratoga, NY homes dataset in the video?

• To label the houses based on their features

• To predict future house prices

• To show an example of clustering using K-Means

• To demonstrate supervised learning

Which Python library is NOT mentioned as required for the K-Means implementation?

• Scikit-learn

• NumPy

• Pandas

• Matplotlib

Which of the following is a feature of the Saratoga dataset used in the video?

• Lot size

• Number of cars

• School district

• Crime rate

What is the elbow method used for in K-Means clustering?

• To increase the number of iterations

• To label the data points

• To calculate the distance between data points

• To determine the optimal number of clusters

What does a big drop in the elbow method graph indicate?

• The optimal number of clusters

• The need for more data

• An error in the algorithm

• The end of the clustering process

What is the main challenge when clustering data without labels?

• Increasing data storage

• Finding the right labels

• Ensuring data privacy

• Determining the number of clusters