Update README.md

README.md

@@ -131,21 +131,19 @@ print(f"RMSE: {rmse}")
 
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-## Example Notebooks
-### 🚀 Interactive Baseline: XGBoost for Resource Estimation
+### Example Notebooks
+#### 🚀 Baseline: XGBoost for Resource Estimation
 
-We provide a sample baseline implementation using **XGBoost** to demonstrate how to perform resource estimation (e.g., predicting `fit_time`) using the dataset's metadata.
+A sample baseline implementation using **XGBoost** is provided to demonstrate how to predict resource metrics such as `fit_time` using the dataset's metadata.
 
-You can interactively explore and run this notebook on Google Colab:
+📥 **Download the notebook** from the repository:
 
-[![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://huggingface.co/datasets/ICICLE-AI/ResourceEstimation_HLOGenCNN/blob/main/Baseline_XGBoost_Resource_Estimation.ipynb)
-**Baseline_XGBoost_Resource_Estimation.ipynb**
+[Baseline_XGBoost_Resource_Estimation.ipynb](https://huggingface.co/datasets/ICICLE-AI/ResourceEstimation_HLOGenCNN/blob/main/Baseline_XGBoost_Resource_Estimation.ipynb)
 
 This notebook covers:
 - Loading and preprocessing metadata from `dataset-new.csv`
 - Training an XGBoost regressor to predict training time
 - Evaluating model performance (e.g., RMSE)
-- Guidance for extending to advanced models (e.g., incorporating HLO graph features)
 
 > ⚡ **Note:** Make sure to adjust paths if cloning the dataset locally or integrating with Hugging Face `datasets` API.