## Problem Given a list of purchase events for a single user, each as `(item_id, timestamp)`, determine the most frequent consecutive purchase pattern of length `k`. A pattern is a sequence of exactly `k` distinct items bought in consecutive transactions (no gaps). Return the pattern as a list of item IDs in the order they were purchased. If there is a tie, return the lexicographically smallest sequence. ```python def most_frequent_pattern(purchases: list[tuple[int, int]], k: int) -> list[int]: ... ``` **Example:** ``` purchases = [(1,100),(2,200),(3,300),(1,400),(2,500),(3,600),(1,700),(4,800)] k = 2 Output: [1, 2] # (1->2) appears twice; (2->3) appears twice -> tie -> lex smaller ``` **Constraints:** `1 <= k <= len(purchases)`, timestamps are strictly increasing. ## Follow-ups - How does your approach change if the same item can appear multiple times within a pattern? - Extend to find patterns across multiple users and return patterns seen by at least `m` users. - What is the time complexity? Can you do it in O(n) for k=2?

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Purchase Pattern Detection - Coding Interview

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Problem Given a list of purchase events for a single user, each as (item_id, timestamp), determine the most frequent consecutive purchase pattern of length k. A pattern is a sequence of exactly k distinct items bought in consecutive transactions (no gaps). Return the pattern as a list of item IDs in the order they were purchased. If there is a tie, return the lexicographically smallest sequence. Example: Constraints: 1 <= k <= len(purchases), timestamps are strictly increasing. Follow-ups How do…

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This is a reported interview question from a affirm interview during the phone round.

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Purchase Pattern Detection - Coding Interview

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