Publications by finnstats
Social Network Analysis in R
Social Network Analysis in R, Social Network Analysis (SNA) is the process of exploring the social structure by using graph theory. It is mainly used for measuring and analyzing the structural properties of the network. It helps to measure social network relationships (Facebook, Twitter likes comments following etc..), Email connectivity, flows b...
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Handling missing values in R
Handling missing values in R, one of the common tasks in data analysis is handling missing values. In R, missing values are often represented by the symbol NA (not available) or some other value that represents missing values (i.e. 99). Impossible values (e.g., dividing by zero) are represented by the symbol NaN (not a number) Handling missing va...
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Regression analysis in R-Model Comparison
Regression analysis in R, just look at the Boston housing data and we can see a total of 506 observations and 14 variables. In this dataset, medv is the response variable, and the remaining are the predictors. We want to make a regression prediction model for medv based on other predictor variables. Most of the variables are numeric variables ex...
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Timeseries analysis in R
Timeseries analysis in R, in statistics time series, is one of the vast subjects, here we are going to analyze some basic functionalities with the help of R software. The idea here is to how to start time series analysis in R. In this tutorial will go through different areas like decomposition, forecasting, clustering, and classification. Cluster...
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Self Organizing Maps in R- Supervised Vs Unsupervised
Self-organizing maps are very useful for clustering and data visualization. Self-organizing maps (SOMs) are a form of neural network and a beautiful way to partition complex data. In this tutorial, we are using college admission data for clustering and visualization and we are covering unsupervised and supervised maps also. Self Organizing Maps T...
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Logistic Regression R- Tutorial
Logistic Regression R, In this tutorial we used the student application dataset for logistic regression analysis. Logistic regression is a statistical model that in its basic form uses a logistic function to model a binary dependent variable. In this tutorial, the target variable or dependent variable is Admit (0-No, 1-Yes) and the remaining vari...
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KNN Algorithm Machine Learning
knn algorithm machine learning, in this tutorial we are going to explain classification and regression problems. Machine learning is a subset of artificial intelligence which provides machines the ability to learn automatically and improve from previous experience without being explicitly programmed. The major part of machine learning is data. Fe...
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Customer Segmentation K Means Cluster
Customer segmentation is the process of separation of customers into groups based on common characteristics or patterns so companies can market their products to each group effectively and significantly. In business-to-consumer marketing, most of the companies often segment their customers into Age, Gender, Marital status, location (urban, suburb...
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Linear Discriminant Analysis in R
linear discriminant analysis, originally developed by R A Fisher in 1936 to classify subjects into one of the two clearly defined groups. It was later expanded to classify subjects into more than two groups. Linear Discriminant Analysis (LDA) is a dimensionality reduction technique. LDA used for dimensionality reduction to reduce the number of di...
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Random Forest Feature Selection
Random Forest feature selection, why we need feature selection? When we have too many features in the datasets and we want to develop a prediction model like a neural network will take a lot of time and reduces the accuracy of the prediction model. We need to make use of the Boruta algorithm and is based on random forest. How Boruta works? Suppos...
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