Outlier Detection Using Euclidean Distance

1. 1Calculate Euclidean distance between data points using the formula: sqrt(sum((x2-x1)^2))
2. 2Identify the threshold value for outlier detection by analyzing the distribution of distances
3. 3Apply the threshold to detect outliers in the dataset
4. 4Visualize the results using a scatter plot to understand the distribution of outliers
5. 5Compare the results with other outlier detection methods to evaluate their effectiveness

Data scientists and analysts can benefit from this technique to identify anomalies in their datasets, while machine learning engineers can use it to improve model performance

💡 Euclidean distance can be used to detect outliers in multidimensional data by calculating the distance between each data point and identifying those that exceed a certain threshold

Detect outliers in multidimensional data using Euclidean distance!