Problem Overview

You are analyzing user engagement patterns across different Netflix content to understand viewing behavior. This type of analysis helps Netflix optimize content recommendations and understand completion rates.

Given a list of viewing sessions, you need to:

• Classify each session based on percentage watched

• Find each user's dominant engagement pattern

• Count how many users have each dominant pattern

Engagement Classification Rules

• Abandoned: Watched less than 25% of the content

• Sampled: Watched 25% to 75% of the content (inclusive on both ends)

• Completed: Watched more than 75% of the content

Example

` viewing_data = [ {"account_id": "U1", "title_id": "T1", "title_runtime_secs": 3600, "watched_secs": 3500, "day_nbr": 1}, {"account_id": "U1", "title_id": "T2", "title_runtime_secs": 5400, "watched_secs": 5000, "day_nbr": 3}, {"account_id": "U2", "title_id": "T1", "title_runtime_secs": 3600, "watched_secs": 800, "day_nbr": 5}, {"account_id": "U2", "title_id": "T2", "title_runtime_secs": 2700, "watched_secs": 500, "day_nbr": 9}, {"account_id": "U3", "title_id": "T1", "title_runtime_secs": 3600, "watched_secs": 1800, "day_nbr": 2} ]

Analysis:

U1: 3500/3600 = 97% (Completed), 5000/5400 = 93% (Completed) → dominant: Completed

U2: 800/3600 = 22% (Abandoned), 500/2700 = 19% (Abandoned) → dominant: Abandoned

U3: 1800/3600 = 50% (Sampled) → dominant: Sampled

analyzeEngagementPatterns(viewing_data)

Returns {"Completed": 1, "Abandoned": 1, "Sampled": 1}

`

Requirements

• Calculate percentage watched for each session

• Classify each session correctly based on percentage

• Handle ties (any tied pattern can be chosen as dominant)

• 1 <= viewing_data.length <= 10^5

• 1 <= title_runtime_secs <= 10^6

• 0 <= watched_secs <= title_runtime_secs

• All account_id and title_id values are non-empty strings