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k-Nearest Neighbor Classification in KNIME

In this post, we shall see how to solve a classification problem using k-Nearest Neighbor (kNN) algorithm in KNIME. We shall use the Teaching Assistant Evaluation dataset from UCI repository. http://archive.ics.uci.edu/ml/datasets/Teaching+Assistant+Evaluation Data Set Information The data consist of evaluations of teaching performance over three regular semesters and two summer semesters …

Partitioning Data in KNIME

In a typical data mining project, it is a good practice to evaluate the performance of the model by applying it on a hold-out sample. Therefore, the available dataset needs to be partitioned. In this post, we shall see the multiple ways of partitioning the data in KNIME using different …

Sub Setting Data in KNIME

In this post, we shall see the multiple ways of sub setting data in KNIME. Reading auto_mpg.csv file Step-1: Add the CSV Reader node from Node Repository: IO > Read > CSV Reader Step-2: Right click on the node and select ‘Configure’ Step-3: In the Settings tab browse and choose …

Creating Dummy Variables with KNIME

Dummy variables are an effective way of utilizing categorical variables in data mining methods like K Nearest Neighbours (KNN) and in regression (like interaction effect). Therefore, there arises a need to convert the categorical variables into dummy variables. In this post, we shall see how to create dummy variables for …

Binning Numeric Data with KNIME

In many situations, we find it convenient if the variables are categorical in nature while doing data mining. Especially, some of the classification methods in data mining, like Naïve Bayes classification, requires that the variables be categorical in nature. In such situations, we need to convert the continuous numeric variables …

Normalizing Data with KNIME

Many data mining techniques involve distance computations. Therefore, it is important that the variables are standardized or else variables with higher values will influence the model. In this post, we shall see how to normalize or standardize the variables in a dataset using KNIME. Download the dataset from here Reading …

Handling Missing Value in KNIME

Often datasets come with varying levels of missing values. Therefore, it becomes important to handle those missing values before getting into any kind of analysis. In this post, we shall cover three basic ways of handling missing value using KNIME. Reading auto_mpg_missing.csv file Step-1: Add the CSV Reader node from …