diff --git a/notebooks/regression/svm_neuralnet_ready.csv b/notebooks/regression/svm_neuralnet_ready.csv
new file mode 100644
index 0000000000000000000000000000000000000000..be9ab250ff1ca0b6707785f80a0023fa8491ba00
--- /dev/null
+++ b/notebooks/regression/svm_neuralnet_ready.csv
@@ -0,0 +1,1198 @@
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diff --git a/notebooks/regression/tree_model_ready.csv b/notebooks/regression/tree_model_ready.csv
new file mode 100644
index 0000000000000000000000000000000000000000..84e91418da9d86fe452f101493cb3041e793bdae
--- /dev/null
+++ b/notebooks/regression/tree_model_ready.csv
@@ -0,0 +1,1198 @@
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+2015-03-11,7,0.65,3.9,1039.0,960,0,0,8.0,0.625625,501-1000,6.946975992135418,0.0,0.0,2,False,False,False,False,False,True,0,1
+2015-03-11,9,0.75,2.9,1039.0,1800,0,0,15.0,0.505888889,1001-2000,6.946975992135418,0.0,0.0,2,False,False,False,False,False,True,0,1
+2015-03-11,6,0.7,2.9,1039.0,720,0,0,6.0,0.394722222,501-1000,6.946975992135418,0.0,0.0,2,False,False,False,False,False,True,0,1
diff --git a/src/utils.py b/src/utils.py
new file mode 100644
index 0000000000000000000000000000000000000000..481a50f2ca223e3a020279773abeebdd05d68622
--- /dev/null
+++ b/src/utils.py
@@ -0,0 +1,30 @@
+from pathlib import Path
+import pandas as pd
+import os
+
+def load_dataset(filename, folder="raw", base_path=None):
+    """
+    Load a dataset from a specific folder path using pandas.
+
+    Parameters:
+        filename (str): The name of the file (e.g., "data.csv").
+        folder (str): The subfolder inside the data directory (default is "raw").
+        base_path (str or Path): Base path to the data directory. If None, defaults to two levels up from this script.
+
+    Returns:
+        pd.DataFrame: Loaded dataset.
+    """
+    if base_path is None:
+        base_path = Path(__file__).resolve().parents[2] / "data"
+
+    file_path = base_path / folder / filename
+
+    if not file_path.exists():
+        raise FileNotFoundError(f"❌ File not found: {file_path}")
+    
+    try:
+        df = pd.read_csv(file_path)
+        print(f"✅ Loaded dataset from: {file_path}")
+        return df
+    except Exception as e:
+        raise RuntimeError(f"⚠️ Failed to load dataset: {e}")