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Nowadays, terms like ‘Data Analytics,’ ‘DataVisualization,’ and ‘Big Data’ have become quite popular. In this modern age, each business entity is driven by data. Data analytics are now very crucial whenever there is a decision-making process involved. The Role of Big Data. The Underlying Concept.
In this article, we will discuss the Decision Tree analysis method. 1) Classification Trees are used when the target variable is categorical and, as the name implies, are used to classify/divide the data into these predefined categories of a target variable. What is Decision Tree Analysis? Use Case – 2.
Basic knowledge of statistics is essential for data science. Statistics is broadly categorized into two types – Descriptive statistics – Descriptive statistics is describing the data. Visual graphs are the core of descriptive statistics. Publish Articles. This is an unexpected event and a red flag is raised.
In this article, we will discuss the Binary Logistic Regression Classification method of analysis, and how it can be used in business. Business Benefit: Once classes are assigned, the bank will have a loan applicant dataset with each applicant labeled as “likely/unlikely to default. What is Binary Logistic Regression Classification?
In this article, we will discuss the Binary Logistic Regression Classification method of analysis, and how it can be used in business. Business Benefit: Once classes are assigned, the bank will have a loan applicant dataset with each applicant labeled as “likely/unlikely to default. What is Binary Logistic Regression Classification?
In this article, we will discuss the Binary Logistic Regression Classification method of analysis, and how it can be used in business. Business Benefit: Once classes are assigned, the bank will have a loan applicant dataset with each applicant labeled as “likely/unlikely to default. What is Binary Logistic Regression Classification?
This article discusses the analytical method of Hierarchical Clustering and how it can be used within an organization for analytical purposes. Business Benefit: Once the segments are identified, the bank will have a loan applicants’ dataset with each applicant labeled as high/medium/low risk. What is Hierarchical Clustering?
This article discusses the analytical method of Hierarchical Clustering and how it can be used within an organization for analytical purposes. Business Benefit: Once the segments are identified, the bank will have a loan applicants’ dataset with each applicant labeled as high/medium/low risk. What is Hierarchical Clustering?
This article discusses the analytical method of Hierarchical Clustering and how it can be used within an organization for analytical purposes. Business Benefit: Once the segments are identified, the bank will have a loan applicants’ dataset with each applicant labeled as high/medium/low risk. What is Hierarchical Clustering?
In this article, we will discuss the KNN Classification method of analysis. The KNN (K Nearest Neighbors) algorithm analyzes all available data points and classifies this data, then classifies new cases based on these established categories. KNN Classification analysis can be useful in evaluating many types of data.
In this article, we will discuss the KNN Classification method of analysis. The KNN (K Nearest Neighbors) algorithm analyzes all available data points and classifies this data, then classifies new cases based on these established categories. KNN Classification analysis can be useful in evaluating many types of data.
In this article, we will discuss the KNN Classification method of analysis. The KNN (K Nearest Neighbors) algorithm analyzes all available data points and classifies this data, then classifies new cases based on these established categories. KNN Classification analysis can be useful in evaluating many types of data.
In this article, we will discuss the Decision Tree analysis method. 1) Classification Trees are used when the target variable is categorical and, as the name implies, are used to classify/divide the data into these predefined categories of a target variable. What is Decision Tree Analysis?
In this article, we will discuss the Decision Tree analysis method. 1) Classification Trees are used when the target variable is categorical and, as the name implies, are used to classify/divide the data into these predefined categories of a target variable. What is Decision Tree Analysis?
This article provides a brief explanation of the FP Growth technique of Frequent Pattern Mining. The FP Growth analytical technique finds frequent patterns, associations, or causal structures from data sets in various kinds of databases such as relational databases, transactional databases, and other forms of data repositories.
This article provides a brief overview of isotonic regression technique. About Smarten The Smarten approach to augmented analytics and modern business intelligence focuses on the business user and provides tools for Advanced Data Discovery so users can perform early prototyping and test hypotheses without the skills of a data scientist.
This article provides a brief overview of isotonic regression technique. The Smarten approach to augmented analytics and modern business intelligence focuses on the business user and provides tools for Advanced Data Discovery so users can perform early prototyping and test hypotheses without the skills of a data scientist.
This article provides a brief overview of isotonic regression technique. The Smarten approach to augmented analytics and modern business intelligence focuses on the business user and provides tools for Advanced Data Discovery so users can perform early prototyping and test hypotheses without the skills of a data scientist.
This article provides a brief explanation of the FP Growth technique of Frequent Pattern Mining. The FP Growth analytical technique finds frequent patterns, associations, or causal structures from data sets in various kinds of databases such as relational databases, transactional databases, and other forms of data repositories.
This article provides a brief explanation of the SVM Classification method of analytics. The goal is to choose a hyperplane with the greatest possible margin between the hyper-plane and any point within the training set, giving a greater chance of new data being classified correctly. What is SVM Classification Analysis?
This article provides a brief explanation of the FP Growth technique of Frequent Pattern Mining. The FP Growth analytical technique finds frequent patterns, associations, or causal structures from data sets in various kinds of databases such as relational databases, transactional databases, and other forms of data repositories.
This article provides a brief explanation of the SVM Classification method of analytics. The goal is to choose a hyperplane with the greatest possible margin between the hyper-plane and any point within the training set, giving a greater chance of new data being classified correctly. What is SVM Classification Analysis?
This article provides a brief explanation of the SVM Classification method of analytics. The goal is to choose a hyperplane with the greatest possible margin between the hyper-plane and any point within the training set, giving a greater chance of new data being classified correctly. What is SVM Classification Analysis?
In this article, we discuss the analytical method known as frequent pattern mining, previously known as ‘association’ What is Frequent Pattern Mining? Use Case – 2 Business Problem: A bank-marketing manager wishes to analyze which products are frequently and sequentially bought together.
In this article, we discuss the analytical method known as frequent pattern mining, previously known as ‘association’ What is Frequent Pattern Mining? Use Case – 2 Business Problem: A bank-marketing manager wishes to analyze which products are frequently and sequentially bought together.
In this article, we discuss the analytical method known as frequent pattern mining, previously known as ‘association’ What is Frequent Pattern Mining? Business Problem: A bank-marketing manager wishes to analyze which products are frequently and sequentially bought together. Use Case – 2.
This article provides a brief overview of random forest classification technique. The data is a result of analysis to determine the quality of the red wine based upon chemicals it consists of. What is Random Forest Classification? Business Use Case 2 Business Problem: Predict quality of Red Wine.
This article provides a brief overview of random forest classification technique. Business Benefit: The predictive model will help us identify whether a customer fails to repay the loan depending on certain factors, which would lead to easier identification of risky customers and help the bank avert the risk delinquencies.
This article provides a brief overview of random forest classification technique. Business Benefit: The predictive model will help us identify whether a customer fails to repay the loan depending on certain factors, which would lead to easier identification of risky customers and help the bank avert the risk delinquencies.
But why Datavisualization? In this article, I am going to examine Why do Business Analysts need to learn Datavisualization skills? This report suggests that, in 2020, the job requirements for data science and analytics is projected to boom to by 364,000 openings to 2,720,000. ” The context.
This article explains the Karl Pearson Correlation method of analysis, and how it can be applied in business. Business Problem: A bank wants to find the correlation between income and credit card delinquency rate of credit card holders. What is the Karl Pearson Correlation Analytical Technique?
This article explains the Karl Pearson Correlation method of analysis, and how it can be applied in business. Business Problem: A bank wants to find the correlation between income and credit card delinquency rate of credit card holders. What is the Karl Pearson Correlation Analytical Technique?
This article explains the Karl Pearson Correlation method of analysis, and how it can be applied in business. Business Problem: A bank wants to find the correlation between income and credit card delinquency rate of credit card holders. What is the Karl Pearson Correlation Analytical Technique?
This article provides a brief explanation of the KMeans Clustering algorithm. KMeans Clustering is a grouping of similar things or data. How Does an Enterprise Use the KMeans Clustering Algorithm to Analyze Data? What is the KMeans Clustering algorithm?
This article provides a brief explanation of the KMeans Clustering algorithm. KMeans Clustering is a grouping of similar things or data. How Does an Enterprise Use the KMeans Clustering Algorithm to Analyze Data? What is the KMeans Clustering algorithm?
This article provides a brief explanation of the KMeans Clustering algorithm. KMeans Clustering is a grouping of similar things or data. How Does an Enterprise Use the KMeans Clustering Algorithm to Analyze Data? What is the KMeans Clustering algorithm?
This article will focus on the Naïve Bayes Classification method of analysis. Business Benefit: Once classes are assigned, the bank will have a loan applicant dataset with each applicant labeled as “likely/unlikely to default”. What is Naïve Bayes Classification? a business can predict the likelihood of fraud.
This article will focus on the Naïve Bayes Classification method of analysis. Business Benefit: Once classes are assigned, the bank will have a loan applicant dataset with each applicant labeled as “likely/unlikely to default”. What is Naïve Bayes Classification? a business can predict the likelihood of fraud.
This article will focus on the Naïve Bayes Classification method of analysis. Business Benefit: Once classes are assigned, the bank will have a loan applicant dataset with each applicant labeled as “likely/unlikely to default”. What is Naïve Bayes Classification? a business can predict the likelihood of fraud. Use Case – 1.
These are the roles that mainly focus on data interpretation, strategy, and decision-making. In this article, I have listed these roles, listed down their responsibilities and their core skills. DataVisualization Specialist/Designer These experts convey trends and insights through visualdata.
This article describes chi square test of association and hypothesis testing. This technique is used to determine if the relationship exists between any two business parameters that are of categorical data type. What is the Chi Square Test of Association Method of Hypothesis Testing?
This article describes chi square test of association and hypothesis testing. This technique is used to determine if the relationship exists between any two business parameters that are of categorical data type. What is the Chi Square Test of Association Method of Hypothesis Testing?
This article describes chi square test of association and hypothesis testing. This technique is used to determine if the relationship exists between any two business parameters that are of categorical data type. What is the Chi Square Test of Association Method of Hypothesis Testing?
There have been so many articles published about AI and its applications, you can find millions of articles from broad concepts to deep technical literature on the internet. You must be tired of continuously hearing quotes like, ‘data is the new oil’ and what not. Hope the article helped. AI in Finance.
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