Poetry
Region: PK
Tuesday 27 October 2026 15:20:16 GMT
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Sajjad Baloch :
Khatam Ho gay Rabte Un se Bhi Jin Se MiL KE Lagta Tha Ke. Zindagi Bhar Saath Dein Gey 💔
2026-10-27 20:06:36
1
عاطف منڈان :
جو آپ سے تنگ ہوں اُنہیں چھوڑ دیا کریں، ان کو خود چھوڑ جانے کی زحمت نہ دیں، بوجھ بننے سے یاد بن جانا بہتر ہے، اپنی ناقدری کرنے والوں کو اپنا سایہ بھی میسر نہ ہونے دیں،K💔 — فی امان اللہ 🫸
2026-10-27 21:00:24
3
Zeeshan Gujjar :
hyy🖤
2026-10-30 14:14:51
1
ذوقِ انتخاب،،💞💞 :
hyeeeeeeeeeee yar
2026-10-27 15:28:19
1
برباد لڑکا :
🥰🥰🥰
2026-10-28 01:46:21
1
shaid nonari :
💕💕
2026-10-27 15:23:36
1
maju chandia 💔 :
😳😳😳
2026-10-27 16:20:54
1
AliKhan DG Khan🥷 :
🥰🥰🥰
2026-11-01 10:27:06
0
💔 Meer 💔 :
🥰🥰🥰
2026-10-29 07:13:01
0
Sharyar siyal Adv :
🥰🥰🥰
2026-10-29 01:34:20
0
wasay Ali :
[Red heart][Red heart][Red heart]
2026-10-29 21:04:21
0
kaka Khan chidru khal 💔 :
🥰🥰🥰
2026-10-28 06:09:43
0
A🫀R💖 :
🥺🥺🥺
2026-10-27 16:52:23
1
Sharyar siyal Adv :
❤️❤️❤️
2026-10-29 01:34:18
0
To see more videos from user @poetrylines204, please go to the Tikwm
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![🤖 BUILD YOUR FIRST MACHINE LEARNING MODEL: PREDICT STUDENT SCORES! 📚 Want to build your first Machine Learning project but don’t know where to start? Let’s build something simple and practical: A Student Score Prediction Model! 🚀 Imagine predicting how many marks a student might score based on the number of hours they study. 🎯 Step 1: Understand the Problem Our goal is to predict a student’s exam score using their study hours. * Input (X): Hours studied * Output (Y): Exam score This is a Supervised Learning problem. 📊 Step 2: Create a Dataset Hours Studied Exam Score 1 35 2 40 3 50 4 55 5 65 6 70 7 80 8 90 🧹 Step 3: Prepare Your Data Use Python libraries: * Pandas: Load and organize data. * NumPy: Work with numerical values. * Scikit-learn: Build your ML model. 📈 Step 4: Choose Your Algorithm Start with Simple Linear Regression. The model learns the relationship between study hours and exam scores. Mathematical formula: [ y = mx + b ] Where: * y = Predicted score * x = Study hours * m = Slope * b = Intercept 🧠 Step 5: Train Your Model Split your dataset into training and testing data. Then use Scikit-learn: * LinearRegression() * model.fit(X_train, y_train) * model.predict(X_test) 🔮 Step 6: Make Your First Prediction What if a student studies for 9 hours? Your trained model can estimate their expected score! Remember: This is a prediction, not a guarantee. Real exam performance depends on many other factors. 📏 Step 7: Evaluate Your Model Learn these evaluation metrics: * MAE: Mean Absolute Error * MSE: Mean Squared Error * RMSE: Root Mean Squared Error * R² Score: How well the model explains score variation 🛠️ Your Tech Stack Python | Pandas | NumPy | Matplotlib | Scikit-learn | Google Colab Your first Machine Learning model doesn’t need to be complicated. Understand the problem, prepare the data, train the model, and evaluate its predictions. That’s how your ML journey begins! 🔥 💬 Challenge: Can you build this model and predict a student’s score after 9 hours of study? #MachineLearning #Python #DataScience #creatorsearchinsights #datascience](/video/cover/7702811090983456020.webp)
