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Feature Text Mining Text Analytics Definition Extracting insights from unstructured text using statistical and ML techniques. Analyzing text data for actionable insights. Focus Identifying patterns and trends in large text datasets. Deriving insights to make informed decisions. Techniques NLP, ML,…
Name Subject Marks John Math 75 Emily Math 73 Michael Math 99 Sophia Math 65 Daniel Math 68 Olivia Math 92 William Math 100 Ava Math 71 James Math 91 Isabella Math 87 John Science 75 Emily Science 86 Michael…
The steps for text analysis involve several stages to extract meaningful information from unstructured text data. Here’s an explanation of each step: Language Identification: Language identification involves determining the language in which the text is written. This step is important…
Why Tokenize Text? Tokenization is a crucial preprocessing step in natural language processing (NLP) that involves splitting text into smaller units, called tokens. Tokens can be words, phrases, or other meaningful units of text, depending on the specific task and…
In R programming language, there are various types of data visualizations that can be created using different packages such as ggplot2, plotly, lattice, and base R graphics. Below are some common types of data visualizations in R along with brief…
The Seven Practice Areas of Text Analytics Search and Information Retrieval (IR): This practice area focuses on developing techniques to efficiently search and retrieve relevant information from large volumes of text data. It involves methods for indexing, querying, and ranking…
Feature ARMA Model ARIMA Model Definition Models that combine autoregressive (AR) and moving average (MA) components without differencing the data. Models that combine autoregressive (AR), moving average (MA), and differencing components to account for non-stationarity. Components AR(p) + MA(q) AR(p)…
ADF (Augmented Dickey-Fuller) Test: The Augmented Dickey-Fuller (ADF) test is a statistical test used to determine whether a given time series is stationary or non-stationary. It is based on the null hypothesis that the series has a unit root, meaning…
Stationarity in Time Series Analysis Stationarity in time series analysis refers to a property of a series where statistical properties such as mean, variance, and autocorrelation structure remain constant over time. In simpler terms, it means that the series does…
Autocorrelation, also known as serial correlation, is a statistical concept that measures the degree of correlation between a time series and a lagged version of itself. In simpler terms, it assesses the relationship between the current observation and past observations…
