KNN ANALYSIS - K nearest neighbours #research #dataanalysis #spss #psychology

KNN Classification Using SPSS: A Beginner's Guide

What is KNN Classification?
K-Nearest Neighbors (KNN) is a simple, non-parametric classification algorithm used to assign labels to data points based on the labels of their closest neighbors in the dataset. The “K” in KNN refers to the number of nearest neighbors considered when making a classification decision. The algorithm is widely used for tasks like pattern recognition, customer segmentation, and predictive analytics.

How Does KNN Work?
Similarity-Based: KNN classifies a new data point by finding the “K” closest data points (neighbours) in the existing dataset and assigning the most common class among those neighbours to the new point.

Distance Metric: The closeness is usually measured using a distance metric such as Euclidean or Manhattan distance.

Non-parametric: KNN does not make assumptions about the underlying data distribution, making it flexible for different types of data.

Step-by-Step: Running KNN Classification in SPSS
1. Prepare Your Data
Ensure your dataset is loaded into SPSS. Your data should include predictor variables (features) and a categorical target variable (the class you want to predict).

Numeric variables are required for distance calculations.

2. Access the KNN Procedure
Go to the top menu and select:
Analyze - Classify - Nearest Neighbour.

3. Specify Analysis Settings
Dependent Variable: Select the categorical variable you want to predict.

Predictors: Choose the variables you want to use for classification.

Number of Neighbors (K): Choose the value of K (e.g., 3, 5). A common approach is to try several values and select the one that gives the best classification accuracy.

Distance Metric: Select a distance measure such as Euclidean or Manhattan, depending on your data and research question.

4. Run the Analysis
Click OK to run the KNN classification. SPSS will process the data and generate output tables and plots.

Tips for Beginners
Choosing K: A small K (like 1 or 3) can be sensitive to noise, while a large K can smooth out distinctions between classes. Try different values and use the one with the best accuracy.

Data Scaling: If your variables are on different scales, consider standardizing them so that no variable dominates the distance calculation.

Outliers: Too many outliers or noisy data can affect KNN performance, so clean your data before running the analysis.

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