@datascibykashi: 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

Data Scientist | Kashi

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Friday 05 September 2025 03:43:30 GMT