Demystifying Interpolation Search: Upgrade Your Searching Game! Prior to this article, I have written about Binary Search. Check it out if you haven’t seen it.
Prior to this article, I have written about Binary Search. Check it out if you haven’t seen it. In this article, we will be discussing Interpolation Search, which is an improvement of Binary Search when the values in the sorted array are uniformly distributed.
You might recall that Binary Search is already running in O(log n) time complexity. Can there be any other methods that perform even better? Ummm… partly yes. We will dive into it shortly.
Interpolation Search is another efficient searching technique. This method can outrun binary search in cases where the elements of the array are uniformly distributed. Interpolation Search estimates the position of the key value in the array. This can be achieved by taking into account the smallest and the largest element in the array and the length of the array.
It is based on the thinking that if the key value is larger (closer to the largest element), it’s position is likely located to the end of the array. The same thing goes for a key with a smaller value.
There are the 6 main steps in doing Interpolation Search, which are:
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The dataset also includes information on time and distance of flights which might also have an effect on delays. These columns can be analyzed with similar methods.