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@Aniruddh-2512
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  1. Correctness:

    • FriendRequests2.sql: The solution correctly identifies the person with the most friend requests by counting both requester and accepter IDs. This approach is correct for finding the most popular person.
    • LeagueStatistics.sql: The solution accurately calculates team statistics including points, goals for, goals against, and goal difference. The UNION ALL approach for home and away matches is correct.
    • NumberofSeniors.sql: The solution correctly calculates the number of senior and junior candidates that can be hired within a budget, using window functions to track cumulative sums.
    • SalesPerson.sql: The solution correctly identifies salespersons who didn't sell to company 'RED' using a NOT IN clause with a subquery.
  2. Time Complexity:

    • All solutions appear to have reasonable time complexity for their respective problems, primarily O(n) or O(n log n) due to sorting and grouping operations.
  3. Space Complexity:

    • The space complexity is generally efficient, with temporary tables (CTEs) being used appropriately to organize intermediate results.
  4. Code Quality:

    • The code is generally well-structured and readable.
    • Consistent use of CTEs improves readability.
    • Some queries could benefit from more descriptive column aliases (e.g., 'num' in FriendRequests2.sql could be 'friend_count').
    • The NumberofSeniors.sql query could be simplified by using a single CTE with conditional aggregation.
  5. Efficiency:

    • FriendRequests2.sql: The solution is efficient, but could potentially be optimized by using COUNT(*) instead of COUNT(id) since id is guaranteed to be non-null.
    • LeagueStatistics.sql: The solution is already quite efficient.
    • NumberofSeniors.sql: The solution could be made more efficient by combining the senior and junior calculations into a single pass.
    • SalesPerson.sql: The solution is efficient, but could be written using NOT EXISTS instead of NOT IN for potentially better performance with NULL values.

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