https://padlet.com/gayathrichandran177/9pup52jhk3o4quf0 en-us 2023-02-21 03:33:04 UTC 2023-02-28 14:40:26 UTC hello@padlet.com ikashahidah https://padlet.com/gayathrichandran177/9pup52jhk3o4quf0/wish/2489342532 2023-02-21 07:21:13 UTC https://padlet.com/gayathrichandran177/9pup52jhk3o4quf0/wish/2489342532 ikashahidah https://padlet.com/gayathrichandran177/9pup52jhk3o4quf0/wish/2489407188 Prediction is the technique of forecasting future occurrences or patterns using historical data. The purpose of prediction is to find patterns in data that may be utilised to create accurate forecasts about future occurrences.
The effective evaluation data mining prediction approaches involves the application of statistical and machine learning algorithms to examine and model the data.]]> 2023-02-21 08:25:19 UTC https://padlet.com/gayathrichandran177/9pup52jhk3o4quf0/wish/2489407188 ikashahidah https://padlet.com/gayathrichandran177/9pup52jhk3o4quf0/wish/2489425738 Regression is a mathematical approach used in data mining to examine the connection between one or even more independent variables and a dependent variable. The purpose of regression analysis is to determine the connection in between independent variable and the dependent variables and to develop a predictive model which can be utilized to make future predictions. Fitting a mathematical model to the data for regression analysis, where the model describes the connection in between independent and dependent variables in the data. Relying on the kind of information being collected, the model might be linear or nonlinear.

In data mining, regression analysis is often used for prediction and forecasting. Regression analysis, for example, may be used in commercial applications to forecast future sales based on prior sales reports and other variables such as advertising expenditures, price, and promotional activities. Regression analysis is a valuable data mining approach because it helps analysts to uncover correlations between variables that are not always visible. It is frequently employed in a wide range of disciplines like as business, finance, healthcare, and social sciences.]]> 2023-02-21 08:44:16 UTC https://padlet.com/gayathrichandran177/9pup52jhk3o4quf0/wish/2489425738 ikashahidah https://padlet.com/gayathrichandran177/9pup52jhk3o4quf0/wish/2489426419 Clustering can arrange items so that things in one cluster are more similar to others. Shortest distance features, density of data points, graphs and other statistical distributions are used to create clusters. K-means, hierarchical clustering and density-based clustering are just a few of the various methods that can be used to analyze clusters. This clustering methodology helps in the collection of important data into clusters and the selection of appropriate results depending on various methods.]]> 2023-02-21 08:45:02 UTC https://padlet.com/gayathrichandran177/9pup52jhk3o4quf0/wish/2489426419 ikashahidah https://padlet.com/gayathrichandran177/9pup52jhk3o4quf0/wish/2496037707 Data cleaning is a crucial technique in data mining that involves identifying and correcting errors, inconsistencies, and discrepancies in data sets to improve their quality and accuracy. The theory behind data cleaning involves understanding the types of errors that can occur in data and the strategies used to detect and correct them.
data cleaning techniques typically involve a combination of automated and manual processes. Automated processes such as data profiling and data validation can help identify errors and inconsistencies in data sets. For example, data profiling can help identify missing values or outliers, while data validation can check for consistency between data sets.]]> 2023-02-27 09:07:39 UTC https://padlet.com/gayathrichandran177/9pup52jhk3o4quf0/wish/2496037707 ikashahidah https://padlet.com/gayathrichandran177/9pup52jhk3o4quf0/wish/2497651214 Decision tree is a powerful tool in data analytics that helps to identify relationships and patterns within a dataset. The theory behind decision trees is rooted in the field of machine learning, and involves the use of algorithms to build a model that can be used to predict outcomes or make decisions based on input data.]]> 2023-02-28 09:17:24 UTC https://padlet.com/gayathrichandran177/9pup52jhk3o4quf0/wish/2497651214 ikashahidah https://padlet.com/gayathrichandran177/9pup52jhk3o4quf0/wish/2498035962 The theory behind data analytics in data exploration involves using various techniques and methods to analyze and understand the structure and patterns within a dataset. The goal of data exploration is to gain a deeper understanding of the data, identify potential issues or anomalies, and extract insights that can inform subsequent analysis and modeling.]]> 2023-02-28 14:28:14 UTC https://padlet.com/gayathrichandran177/9pup52jhk3o4quf0/wish/2498035962 ikashahidah https://padlet.com/gayathrichandran177/9pup52jhk3o4quf0/wish/2498045501 Association rule mining is a data mining technique that involves identifying patterns or relationships between variables in a dataset. The goal of association rule mining is to find frequent itemsets or sets of items that occur together in a transactional database, and to use these frequent itemsets to generate association rules.

The theory behind data analytics in association rule mining involves several key concepts, including support, confidence, and lift. Support is the proportion of transactions in the dataset that contain a specific itemset, while confidence is the conditional probability that a transaction containing one itemset will also contain another itemset. Lift is a measure of the strength of the association between two itemsets, and is calculated as the ratio of the observed frequency of the two itemsets occurring together to the expected frequency of the two itemsets occurring together if they were statistically independent.]]> 2023-02-28 14:34:01 UTC https://padlet.com/gayathrichandran177/9pup52jhk3o4quf0/wish/2498045501