## Concept of Correlation in Statistics

Correlation is degree of relatedness of two or more variables. Correlation shows the linear relationship between two or more variables.

Correlation is degree of relatedness of two or more variables. Correlation shows the linear relationship between two or more variables.

A recommender system is a technology that uses machine learning and data analysis to match people with similar tastes and preferences. Have three types: Content-based Filtering, Collaborative Filtering, and Collaborative Filtering.

Data are the facts that are collected by direct observation, experiments, and surveys.

Introduction Distance is the separation of two or more points in space. Machine learning models use different types of distances such as Euclidean distance, Manhattan distance, Minkowski, cosine distance, etc. Machine learning algorithms like k-nearest neighbor, k-means clustering uses Euclidean, Manhattan, Minkowski methods for distance calculation. Cosine distance is used in recommendation algorithms. In this … Read more

Let’s discuss the random generation methods in this blog, especially there are two methods: Linear Congruential Method and Combined Linear Congruential Method

Difference between data mart and data warehouse. A data mart is the subset of a data warehouse that holds more summarized data whereas a data warehouse very detailed information.

Let’s discuss the difference between OLTP and OLAP. Online Transaction Processing(OLTP) is a transactional processing system whereas Online Analytical Processing(OLAP) is an analytical processing system that can handle any analytical process.

Deviations from exact or true values are called errors. There are different types of error in numerical methods such as inherent error, round-off error, truncation error, absolute error, relative error, percentage error, etc.

Web Mining is the application of data mining techniques to extract knowledge from web data i.e. web content, web structure, and web usage data. According to the type of data extraction, web mining is divided into three main types: Web content mining, Web structure mining, and Web usage mining.

Forward chaining in AI is a method in which inference rules are applied to existing data to extract additional data until an end goal is achieved. Backward chaining in AI is the logical process of inferring unknown truths from known conclusions by moving backward from a solution to determining the initial conditions.