Data.md

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 title: Data
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 ## Data Chapter
 
 ### 1. Data Source
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 #### 5.2. Impact of Fake Data
-- The addition of fake data was evaluated by comparing the performance of machine learning models with and without fake data. The performance drop or improvement was analyzed based on the **Mean Squared Error (MSE)** and **R²** scores.
+- The inclusion of fake data was carefully evaluated as it significantly influenced the model's accuracy. Adding more than 25% fake data caused the accuracy to drop drastically, making the model unreliable.
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+- To maintain balance and robustness, the fake data was limited to 25%. This ensured enough variety without heavily compromising model performance.
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+- The evaluation showed a decline in performance metrics such as **Mean Squared Error (MSE)** and **R²** scores when fake data exceeded this threshold, emphasizing the importance of minimizing synthetic data.
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 ### 6. Data Splitting
 - The dataset was split into **training** and **testing** sets using a **70:30** ratio for model evaluation.