Interpolation is a numerical technique used to estimate unknown values within the range of known data points. It is essential in various fields like scientific computing, engineering, and data analysis. This virtual lab experiment explores interpolation methods for both evenly and unevenly spaced data points, focusing on techniques such as linear interpolation, polynomial interpolation (Lagrange and Newton’s divided differences), spline interpolation, and nearest-neighbor interpolation. Linear interpolation connects adjacent points with straight lines, while polynomial interpolation constructs a higher-degree polynomial to pass through all given points. Spline interpolation employs piecewise polynomials for smooth transitions between points, and nearest-neighbor interpolation assigns the value of the closest point to the unknown value. Evenly spaced points simplify certain algorithms like Newton’s forward and backward differences, whereas unevenly spaced points require more generalized methods. The experiment aims to implement and compare these techniques, analyzing their accuracy, computational efficiency, and suitability for various datasets. Applications include data estimation, image processing, and numerical modeling, providing students with practical insights into effective interpolation strategies.