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@odettei: mmf dibelakang ada yg tidur

🐇 : michieya !
🐇 : michieya !
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Region: ID
Monday 04 May 2026 05:37:07 GMT
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Comments

paqikoi
kholily. :
ka miciiii
2026-05-04 07:00:08
0
olly_azli
kaelar💀 :
gk²,keknya kemarin gk sekalem ini 😳🤔
2026-05-04 06:09:58
0
rizkypriat
Fariz :
2026-05-04 11:40:14
1
kreutzerna
Kreutzerna :
my bini tuh
2026-05-04 06:48:19
0
luckyadrn48
ユキ❄ :
Baru tau ada bidadari yang naik mobil 🥰🥰
2026-05-04 05:44:31
0
tianztif
Tianz Acuuuuu 🍬🐰🐟 :
cantik nyaaa🥰🥰🥰
2026-05-04 14:27:35
0
sy1st_
Bandot :
2026-05-04 06:53:07
0
hiraethhd
ril :
2026-05-04 06:37:51
0
rsykb26
muraak :
2026-05-04 05:51:19
0
To see more videos from user @odettei, please go to the Tikwm homepage.

Other Videos

One of the best beginner Machine Learning projects because it takes you through the complete ML workflow. 1️⃣ COLLECT DATA 📥 Gather historical house data: 📐 Area 🛏️ Bedrooms 🚿 Bathrooms 📍 Location 🏗️ Property age 🚗 Parking 💰 Sale price 2️⃣ EXPLORE THE DATA 🔍 Perform EDA to understand: 📊 Distributions 🔗 Feature relationships 🚨 Outliers ❌ Missing values 📈 Price patterns 3️⃣ CLEAN THE DATA 🧹 Handle: ❌ Missing values 🔁 Duplicates ⚠️ Outliers 🏷️ Categorical variables 📏 Numerical features 4️⃣ PREPARE FEATURES ⚙️ Select useful variables and transform them into a format the model can learn from. Example: Location + Area + Bedrooms + Bathrooms → Model Features 🎯 Target → House Price 5️⃣ TRAIN THE MODEL 🤖 Start with regression algorithms such as: 📈 Linear Regression 🌳 Decision Tree 🌲 Random Forest ⚡ Gradient Boosting 6️⃣ EVALUATE 📊 Don’t just ask: “Is my model accurate?” Use appropriate regression metrics: 📉 MAE 📊 MSE 📐 RMSE 📈 R² 7️⃣ MAKE A PREDICTION 🎯 Give the model a new house: 📐 2,000 sq ft 🛏️ 4 bedrooms 🚿 3 bathrooms 📍 Location X ⬇️ 🤖 Model ⬇️ 💰 Predicted House Price 🔄 THE COMPLETE FLOW Data ⬇️ EDA ⬇️ Cleaning ⬇️ Feature Engineering ⬇️ Train Model ⬇️ Evaluate ⬇️ Predict ⬇️ 🚀 Deploy the Application 🛠️ TECH STACK 🐍 Python 🐼 Pandas 🔢 NumPy 📊 Matplotlib / Seaborn 🤖 Scikit-learn 🌐 Streamlit or FastAPI 💡 The real lesson isn’t predicting house prices. It’s learning how to take a real-world problem and turn it into a complete Machine Learning pipeline. Problem → Data → Model → Prediction → Solution 📌 Save this project idea for your ML portfolio. #HousePricePrediction #MachineLearning #DataScience #creatorsearchinsights #datascience
One of the best beginner Machine Learning projects because it takes you through the complete ML workflow. 1️⃣ COLLECT DATA 📥 Gather historical house data: 📐 Area 🛏️ Bedrooms 🚿 Bathrooms 📍 Location 🏗️ Property age 🚗 Parking 💰 Sale price 2️⃣ EXPLORE THE DATA 🔍 Perform EDA to understand: 📊 Distributions 🔗 Feature relationships 🚨 Outliers ❌ Missing values 📈 Price patterns 3️⃣ CLEAN THE DATA 🧹 Handle: ❌ Missing values 🔁 Duplicates ⚠️ Outliers 🏷️ Categorical variables 📏 Numerical features 4️⃣ PREPARE FEATURES ⚙️ Select useful variables and transform them into a format the model can learn from. Example: Location + Area + Bedrooms + Bathrooms → Model Features 🎯 Target → House Price 5️⃣ TRAIN THE MODEL 🤖 Start with regression algorithms such as: 📈 Linear Regression 🌳 Decision Tree 🌲 Random Forest ⚡ Gradient Boosting 6️⃣ EVALUATE 📊 Don’t just ask: “Is my model accurate?” Use appropriate regression metrics: 📉 MAE 📊 MSE 📐 RMSE 📈 R² 7️⃣ MAKE A PREDICTION 🎯 Give the model a new house: 📐 2,000 sq ft 🛏️ 4 bedrooms 🚿 3 bathrooms 📍 Location X ⬇️ 🤖 Model ⬇️ 💰 Predicted House Price 🔄 THE COMPLETE FLOW Data ⬇️ EDA ⬇️ Cleaning ⬇️ Feature Engineering ⬇️ Train Model ⬇️ Evaluate ⬇️ Predict ⬇️ 🚀 Deploy the Application 🛠️ TECH STACK 🐍 Python 🐼 Pandas 🔢 NumPy 📊 Matplotlib / Seaborn 🤖 Scikit-learn 🌐 Streamlit or FastAPI 💡 The real lesson isn’t predicting house prices. It’s learning how to take a real-world problem and turn it into a complete Machine Learning pipeline. Problem → Data → Model → Prediction → Solution 📌 Save this project idea for your ML portfolio. #HousePricePrediction #MachineLearning #DataScience #creatorsearchinsights #datascience


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