Topic9_10WthQuizFinLAB

Data cleaning

Data visualization

Data mining algorithm selection

Model deployment

The algorithm is trained on unlabeled data

The algorithm learns from labeled data

The model predicts future data

The data is unstructured

To find hidden patterns in data

To predict the output using labeled data

To cluster data points

To reduce data dimensionality

Decision trees

K-means clustering

Linear regression

Naive Bayes

Classification

Clustering

Regression

Dimensionality reduction

The number of clusters

The number of features

The number of outliers

The number of iterations

Grouping similar data into clusters

Predicting continuous values

Assigning labels to data points based on features

Reducing the dimensionality of data

Stock price prediction

Image recognition

Customer segmentation

Predicting weather patterns

Supervised learning method

Unsupervised learning method

Regression technique

Dimensionality reduction technique

The clusters may become too broad and mixed

The clusters become too small and less meaningful

The algorithm fails to converge

The data becomes overfitted