@arqf.f: #ملصقات #الحربي_كارمن #fyp #تلشعب_الصيني_ماله_حل😂😂 @آلـحــࢪبـي 🚸 @كــارمـــن 💔

جانو || الحربي

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Friday 28 August 2026 07:40:17 GMT

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جانو || الحربي :

دزولي ملصقات الحربي اريدها

2026-08-28 07:45:24

12

(للزمن؟ ) :

سوها ملصق

2026-08-28 15:42:35

1

رقيه ال كارمن (｡♡‿♡｡) 🔓 :

علمود تبوكيهن

2026-08-28 14:19:36

0

احمد رسول :

حاربي

2026-08-28 08:51:37

3

أشرف 🪽 :

شلون امسح الستوري انفضحت

2026-08-28 16:01:31

1

دٍعٌدٍشُه‍🚸 :

♥️

2026-08-28 08:42:01

4

امير _؟_الرعب @🧿 :

2026-08-28 09:11:21

2

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Can I build and test a real machine-learning model before a 10-minute timer runs out? Here’s the full breakdown of what I built 👇 The goal was to predict average house values across California using information about different neighborhoods. The dataset contained more than 20,000 examples, with clues including median income, house age, average number of rooms, population, and location. 1️⃣ First, I loaded and explored the data. Each row represented one area, each column contained a possible clue, and the house-value column contained the answer I wanted the model to predict. Before continuing, I guessed that median income would be the strongest individual clue, and it was!!! 2️⃣ Next, I separated the clues from the answers. In machine learning, the information given to the model is called the features, while the value we want it to predict is called the target. Keeping them separate is important because allowing the model to see the correct house value would be like giving someone the answers during a test. 3️⃣ Then, I split the data into two groups. The model received 80% of the neighborhoods for training, while the remaining 20% stayed completely hidden. That hidden group became the final test, allowing me to see whether the model could make predictions for examples it had never studied. 4️⃣ With the data ready, I created a linear-regression model. Linear regression looks for mathematical relationships between the clues and house values. It learns how strongly each feature should affect the final prediction while considering all the other features at the same time. 5️⃣ Then came the most important part: training. The model studied more than 16,000 examples and calculated the relationships that produced the smallest overall prediction errors. 6️⃣ After training, I gave it the hidden testing data. The model predicted a house value for every neighborhood without seeing the correct answers. Only after it finished did I compare its predictions with the real values. The final average error was approximately $53,000, and the R² score was around 0.576. That means this simple model explained roughly 57.6% of the variation in the test data. Finally, I graphed the predicted values against the actual values. Every dot represented one neighborhood, and the closer it appeared to the perfect-prediction line, the more accurate the model was. Is this ready to determine the price of someone’s house? Definitely not. But in ten minutes, we completed the entire machine-learning workflow!

Can I build and test a real machine-learning model before a 10-minute timer runs out? Here’s the full breakdown of what I built 👇 The goal was to predict average house values across California using information about different neighborhoods. The dataset contained more than 20,000 examples, with clues including median income, house age, average number of rooms, population, and location. 1️⃣ First, I loaded and explored the data. Each row represented one area, each column contained a possible clue, and the house-value column contained the answer I wanted the model to predict. Before continuing, I guessed that median income would be the strongest individual clue, and it was!!! 2️⃣ Next, I separated the clues from the answers. In machine learning, the information given to the model is called the features, while the value we want it to predict is called the target. Keeping them separate is important because allowing the model to see the correct house value would be like giving someone the answers during a test. 3️⃣ Then, I split the data into two groups. The model received 80% of the neighborhoods for training, while the remaining 20% stayed completely hidden. That hidden group became the final test, allowing me to see whether the model could make predictions for examples it had never studied. 4️⃣ With the data ready, I created a linear-regression model. Linear regression looks for mathematical relationships between the clues and house values. It learns how strongly each feature should affect the final prediction while considering all the other features at the same time. 5️⃣ Then came the most important part: training. The model studied more than 16,000 examples and calculated the relationships that produced the smallest overall prediction errors. 6️⃣ After training, I gave it the hidden testing data. The model predicted a house value for every neighborhood without seeing the correct answers. Only after it finished did I compare its predictions with the real values. The final average error was approximately $53,000, and the R² score was around 0.576. That means this simple model explained roughly 57.6% of the variation in the test data. Finally, I graphed the predicted values against the actual values. Every dot represented one neighborhood, and the closer it appeared to the perfect-prediction line, the more accurate the model was. Is this ready to determine the price of someone’s house? Definitely not. But in ten minutes, we completed the entire machine-learning workflow!

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