Publications by finnstats
Naive Bayes Classification in R
Naive Bayes Classification in R, In this tutorial, we are going to discuss the prediction model based on Naive Bayes classification. Naive Bayes is a classification technique based on Bayes’ Theorem with an assumption of independence among predictors. The Naive Bayes model is easy to build and particularly useful for very large data sets. When ...
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Deep Neural Network in R
Neural Network in R, Neural Network is just like a human nervous system, which is made up of interconnected neurons, in other words, a neural network is made up of interconnected information processing units. The neural network draws from the parallel processing of information, which is the strength of this method. A neural network helps us to ex...
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LSTM Network in R
LSTM network in R, In this tutorial, we are going to discuss Recurrent Neural Networks. Recurrent Neural Networks are very useful for solving sequence of numbers-related issues. The major applications involved in the sequence of numbers are text classification, time series prediction, frames in videos, DNA sequences Speech recognition problems, e...
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Linear optimization using R
Linear optimization using R, in this tutorial we are going to discuss the linear optimization problems in R. Optimization is everything nowadays. We all have finite resources and time and we want to make the maximum profit out of that. Companies want to makes maximum profits based on limited resources they have, yes optimization is the solution ...
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Random Forest in R
Random Forest in R, Random forest developed by an aggregating tree and this can be used for classification and regression. One of the major advantages is its avoids overfitting. The random forest can deal with a large number of features and it helps to identify the important attributes. The random forest contains two user-friendly parameters ntre...
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Basic Functions in R
Basic Functions in R, in this tutorial, we are going to discuss basic statistical or user-defined functions. Functions are very useful in R for faster and safe execution. Some will be inbuilt functions and others may be user-defined, here we are going to discuss very useful functions in our day-to-day life. Basic Functions in R Getting Data libra...
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Market Basket Analysis in R
Market Basket Analysis in R, Market Basket Analysis is very popular. In this tutorial, the main idea is to identify the purchase pattern of the products, “what goes with what”. Based on this information Data Scientist can make decisions for increasing business profit. Many examples are available, suppose if you are login into amazon prime, th...
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Gradient Boosting in R
Gradient Boosting in R, in this tutorial we are going to discuss extreme gradient boosting. Why is eXtreme Gradient Boosting in R? Popular in machine learning challenges. Fast and accurate Can handle missing values. Is it requires numeric inputs? Yes, eXtreme Gradient Boosting requires a numeric matrix for its input. Sample Size calculation formu...
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Decision Trees in R
Decision Trees in R, Decision trees are mainly classification and regression types. Classification means Y variable is factor and regression type means Y variable is numeric. Just look at one of the examples from each type, Classification example is detecting email spam data and regression tree example is from Boston housing data. Decision trees...
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Cluster Analysis in R
Cluster Analysis in R, when we do data analytics, there are two kinds of approaches one is supervised and another is unsupervised. Clustering is a method for finding subgroups of observations within a data set. When we are doing clustering, we need observations in the same group with similar patterns and observations in different groups to be dis...
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