1. Home
  2. Detail

@binodpariyar078: ##viral_video ##viral_video 😊😊😊😊😊

B❤️J
B❤️J
Open In TikTok:
Region: US
Tuesday 06 October 2026 12:59:42 GMT
139
36
10
0

Music

Download

No Watermark .mp4 (1.9MB) No Watermark(HD) .mp4 (1.9MB) Watermark .mp4 (0MB) Music .mp3

Comments

priya.chetry6
Priya Chetry :
🥰🥰🥰
2026-10-06 16:25:03
1
laxmi.pariyar.00
Laxmi pariyar 00 :
🥰🥰🥰
2026-10-06 16:34:45
1
kamalapokheral205
samjhana pokhrel :
[Heartwarming][Heartwarming][Heartwarming]
2026-10-06 13:53:53
1
sitapokhrel006
Hari kala Pokherl :
🥰🥰🥰
2026-10-06 13:48:50
1
paras4965
paras :
🥰🥰🥰
2026-10-06 18:37:24
1
asmita4..5
as^mi~^ta`...❤️😍 :
🥰🥰🥰
2026-10-06 13:41:25
1
basantibhattrai15
❣ मुना ❣ मदन ❣ :
💕💕💕
2026-10-06 17:25:22
1
gomapokhrel174
gomapokhrel :
[Heartwarming][Heartwarming][Heartwarming]
2026-10-07 05:39:20
1
binita.nepali784
...BÎÑÌTÃ...❤️‍🩹🫶 :
[Heartwarming][Heartwarming][Heartwarming]
2026-10-08 03:06:10
0
rabinanp20
Rabina Nepali :
👌👌👌
2026-10-07 14:31:03
1
To see more videos from user @binodpariyar078, please go to the Tikwm homepage.

Other Videos

this edit is for ms. ash 🥹💔 almost forgot to post #naottos #fyp #wetsandmanhwa #josephlandi #joian
this edit is for ms. ash 🥹💔 almost forgot to post #naottos #fyp #wetsandmanhwa #josephlandi #joian
Dates and times are special features ⏳ that carry a lot of hidden information. But ML models can’t use raw datetime values directly ❌ 👉 That’s why we extract useful features from them. 🔹 Common Transformations for Date/Time: 1️⃣ Split into Components • Year, Month, Day, Hour, Minute • Example: 2024-08-29 14:35 → Year=2024, Month=8, Hour=14 2️⃣ Create Derived Features • Day of Week (Mon–Sun) • Weekend vs Weekday • Season (Winter, Summer, etc.) 3️⃣ Time Differences • Calculate duration between events • Example: Order_Date → Delivery_Date = Shipping_Time 4️⃣ Cyclical Encoding (for repeating patterns ⏰) • Convert Month/Day/Hour into sine & cosine to handle cycles. • Example: Hours of a day → sin(hour), cos(hour) ⚡ Why it matters? ✅ Captures trends, seasonality & patterns ✅ Improves forecasting & time-sensitive predictions ✅ Makes datetime data ML-friendly ⚠️ Be careful with time zones & missing timestamps 💡 Key Takeaway: Date & Time features hide powerful patterns (seasonality, trends, durations). Extract & encode them properly → your ML model gets a huge boost 🚀 #️⃣ #DateTimeFeatures #FeatureEngineering #MachineLearning #DataScience #100DaysOfML #mljourney
Dates and times are special features ⏳ that carry a lot of hidden information. But ML models can’t use raw datetime values directly ❌ 👉 That’s why we extract useful features from them. 🔹 Common Transformations for Date/Time: 1️⃣ Split into Components • Year, Month, Day, Hour, Minute • Example: 2024-08-29 14:35 → Year=2024, Month=8, Hour=14 2️⃣ Create Derived Features • Day of Week (Mon–Sun) • Weekend vs Weekday • Season (Winter, Summer, etc.) 3️⃣ Time Differences • Calculate duration between events • Example: Order_Date → Delivery_Date = Shipping_Time 4️⃣ Cyclical Encoding (for repeating patterns ⏰) • Convert Month/Day/Hour into sine & cosine to handle cycles. • Example: Hours of a day → sin(hour), cos(hour) ⚡ Why it matters? ✅ Captures trends, seasonality & patterns ✅ Improves forecasting & time-sensitive predictions ✅ Makes datetime data ML-friendly ⚠️ Be careful with time zones & missing timestamps 💡 Key Takeaway: Date & Time features hide powerful patterns (seasonality, trends, durations). Extract & encode them properly → your ML model gets a huge boost 🚀 #️⃣ #DateTimeFeatures #FeatureEngineering #MachineLearning #DataScience #100DaysOfML #mljourney
Nghe đâu đó #xuhuonggg #fpy #vairal #tuanan04
Nghe đâu đó #xuhuonggg #fpy #vairal #tuanan04


Easy way to build a bank statement
Easy way to build a bank statement
#созданный_в_бездне #мемы #деньсамоуправления #MadeInAbyss #madeinabyssanime
#созданный_в_бездне #мемы #деньсамоуправления #MadeInAbyss #madeinabyssanime

About

Legal