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@ctm25471ehz: Click, scrub, toss. No more disgusting toilet brush sitting in dirty water.#cleaninghacks #Toiletbrush #sopami #tiktok #deepclean

aurora
aurora
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Region: US
Saturday 14 March 2026 13:12:02 GMT
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Comments

thebrownbearclan
brownbearclan :
Why...the red, just, why. 🤦‍♂️🧐🤷‍♂️
2026-03-25 12:14:34
9
originallusalegrand
user2781260862097 :
Isn’t this a product made by Clorox , a dozen or so years ago? What am I missing?? lol!
2026-03-31 14:22:43
0
phatj38
Phat-J38 :
I have it already
2026-03-15 14:55:54
2
bubba5340
Becky5340 :
Not as blue.
2026-03-15 02:48:48
1
xiaotuantuan0018
Xiao Frank :
Nice
2026-03-27 14:27:50
0
connoversmcgee
connovers :
wow
2026-03-16 08:32:24
0
lifesunhfuyh
Everyday -Haven :
Nice
2026-03-17 02:34:49
0
dorma.anneman
Dorma Anneman :
😂😂😂
2026-03-24 13:33:53
1
jamal1234750
jamal1234750 :
🥰🥰🥰
2026-03-19 12:09:53
0
noriejean4
Norie Jean :
😁😁😁
2026-03-15 03:13:40
0
user8937589205723
user8937589205723 :
😳😳😳
2026-03-30 17:57:47
0
To see more videos from user @ctm25471ehz, please go to the Tikwm homepage.

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Inside the Mind of a Data Scientist 🧠 I didn’t start by picking a CatBoost model right away - I started by understanding the customer data, the class imbalance, and what the business actually needs from the model. Because more customers stay than leave, accuracy alone could be misleading. The real goal is to catch as many likely churners as possible while keeping false alarms manageable, which is why I focused on PR-AUC, precision, recall, and the prediction threshold. CatBoost was a strong fit because the dataset mixes numerical features like tenure and monthly charges with categorical features like contract type, internet service, and payment method. Instead of making a simple yes-or-no decision, the model produces a churn probability that can be used to prioritize which customers may need support first. Full code: github.com/lerabyte/customer-churn-catboost #ml #machinelearning #datascience #ai #STEM
Inside the Mind of a Data Scientist 🧠 I didn’t start by picking a CatBoost model right away - I started by understanding the customer data, the class imbalance, and what the business actually needs from the model. Because more customers stay than leave, accuracy alone could be misleading. The real goal is to catch as many likely churners as possible while keeping false alarms manageable, which is why I focused on PR-AUC, precision, recall, and the prediction threshold. CatBoost was a strong fit because the dataset mixes numerical features like tenure and monthly charges with categorical features like contract type, internet service, and payment method. Instead of making a simple yes-or-no decision, the model produces a churn probability that can be used to prioritize which customers may need support first. Full code: github.com/lerabyte/customer-churn-catboost #ml #machinelearning #datascience #ai #STEM
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