AB test for optimization towards the hit rate

Assignment Help Data Structure & Algorithms
Reference no: EM132631488

The task is focused around an AB test for optimization towards the hit rate (% of players who allow push notifications) and Player Retention.

You need to complete technical solutions coded in Python programming language and the solution description.

AB test

## Setup

Players (users) are randomly split in two groups:

- group1 - even loginIDs
- group2 - odd loginIDs

We are testing which is the best time / place to show popup to players asking them to allow push notifications:

- group1 - immediately after the installation (before the first tutorial battle)
- group2 - after the tutorial (which consists of 5 battles) We are optimizing towards:
- Hit rate (% of players who allow push notifications)
- Player Retention

Test was run for two weeks (14 days). Only players that participated in the test appear in the provided data.

## Data

There are two anonimized files:

- `notification_allowed.csv`
- `user_history.csv`

### notification_allowed.csv
Sent after request popup has been shown and player has answered (closing the app defaults to `false` as an answer).

```
login_id = int, starting from 1 time
allowed_notifications = true / false
date_id = int, starting from 0, measuring days since the start of the test
```

### user_history.csv
Generated at the end of each day. It is sent for every active user and for every payer (all time payer). When 21 days pass after the player has logged in for the last time, player no longer appears in this table. This is not true for payers, as they are eligible to appear in this table no matter how long has passed since the last log in.

```
login_id = int, starting from 1

registration_date_id = date_id when a player has joined the game registration_channel = Paid/Organic
registration_country = country of origin of the first session payer = player has spent money at any time in the past
dau = daily active player: 0 or 1 in this table sessions_count = daily count of sessions playtime = daily total playtime
last_login_day = date_id of the last login, up to this day days_active_last_7_days = number of days user was active,
including this day
cohort_size = 1 for all in this table
elo_rating = elo (trophie count) at the end of the day arena_level = arena level at the end of the day
gold = gold stash at the end of the day runes_stash_class1_tier1 = stash of various currencies runes_stash_class2_tier1
runes_stash_class3_tier1 runes_stash_tier2 spell_runes_stash hero_dust
item_dust
gold_gained_total = stats for various currencies gold_bought_total
gold_spent_total runes_gained_total_class1_tier1 runes_gained_total_class2_tier1 runes_gained_total_class3_tier1 runes_gained_total_tier_2 spell_runes_gained_total runes_spent_class1_tier1 runes_spent_class2_tier1 runes_spent_class3_tier1 runes_spent_total_tier2 spell_runes_spent_total hero_dust_spent_total hero_dust_gained_total
tokens tokens_gained_total
enter_queue_count = number of times player has entered PvP queue battles_played = daily battles played
battles_won = daily battles won
battles_played_total = battles played up to this day battles_won_total = battles won up to this day heroes_unlocked = heroes unlocked up to this day heroes_level = hero stats
hero_shards = hero stats
minion_upgrades = number of times user has upgraded minions free_chests_opened_total = various chest stats silver_chests_opened_total
golden_chests_opened_total magical_chests_opened_total victory_chests_opened_total pvp_event_small_chest_small pvp_event_small_chest_medium

pvp_event_small_chest_large pvp_event_big_chest_small pvp_event_big_chest_medium pvp_event_big_chest_large cards_gained_total = various card stats minion_cards_gained_common_total minion_cards_gained_rare_total minion_cards_gained_epic_total minion_cards_gained_legendary_total spell_cards_gained_common_total spell_cards_gained_rare_total spell_cards_gained_epic_total spell_cards_gained_legendary_total campaign_tickets = various campaign stats max_campaign_level
equipment_bonus campaign_battles campaign_battles_total
event_battles = various live event stats event_battles_total
event_tokens group_points lives_spent milestone_points
date_id = int, starting from 0, measuring days since the start of the test.

Attachment:- Task.rar

Reference no: EM132631488

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len2631488

9/14/2020 6:27:46 AM

The task is focused around an AB test for optimization towards the hit rate (% of players who allow push notifications) and Player Retention. I need complete technical solutions coded in Python programming language and the solution description. Please, inform me about the progress and send me a high-level solution and after that Python code daily.

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