Text Mining Techniques

Linear Regression

Statistical method modeling relationship between variables with a best-fitting line.

Naive Bayes

Probabilistic algorithm for classifying data based on conditional independence.

k-means Clustering

Unsupervised algorithm grouping data points into k clusters based on centroids.

Principal Component Analysis (PCA)

Dimensionality reduction preserving data variance by identifying principal components.

Random Forest

Ensemble method combining decision trees to enhance predictive performance.

Sentiment Analysis

NLP technique classifying text sentiment as positive, negative, or neutral.

Association Rules

Data mining technique discovering relationships between items in datasets.

Bubble Chart

Data visualization showing data points as bubbles on a scatter plot.

Decision Trees

Predictive modeling technique using tree-like structures for classification and regression.

Clustering

Unsupervised technique grouping similar data points into clusters.

Tree Map

Visualization technique displaying hierarchical data as nested rectangles.

Heat Map

Graphical representation of data using colors to show values in a matrix.

Time series

Sequence of data points collected at regular time intervals for trend analysis.

Regression analysis

Statistical method modeling relationships between variables for predictions.

Text mining

Extracting insights from unstructured text data using techniques like sentiment analysis.