diff --git a/src/app.py b/src/app.py
index 3ce4475..598f3bc 100644
--- a/src/app.py
+++ b/src/app.py
@@ -1,9 +1,8 @@
 from learning.data import Dataset
 from learning.supervised import LinearRegression
-from learning.ml import MLAlgorithm
-from plot import Plot
+from learning.ml import MLRegression
 
-def auto_mpg() -> MLAlgorithm:
+def auto_mpg() -> MLRegression:
     df = Dataset("datasets\\auto-mpg.csv", "MPG")
 
     df.to_numbers(["HP"])
@@ -12,29 +11,22 @@ def auto_mpg() -> MLAlgorithm:
 
     return LinearRegression(df, learning_rate=0.0001)
 
-def automobile() -> MLAlgorithm:
+def automobile() -> MLRegression:
     df = Dataset("datasets\\regression\\automobile.csv", "symboling")
 
     attributes_to_modify = ["fuel-system", "engine-type", "drive-wheels", "body-style", "make", "engine-location", "aspiration", "fuel-type", "num-of-cylinders", "num-of-doors"]
     df.factorize(attributes_to_modify)
-    df.to_numbers()
+    df.to_numbers(["normalized-losses", "bore", "stroke", "horsepower", "peak-rpm", "price"])
     df.handle_na()
     df.regularize(excepts=attributes_to_modify)
 
     return LinearRegression(df, learning_rate=0.001)
 
 
-
-
-epoch = 50000
-skip = 1000
-lr = automobile()
-
-train, test = lr.learn(epoch)
-
-plot = Plot("Error", "Time", "Mean Error")
-plot.line("training", "red", data=train[skip:])
-plot.line("test", "blue", data=test[skip:])
+epoch = 15000
+ml = automobile()
+ml.learn(epoch)
+ml.plot()
 
 """
 for _ in range(0, epoch):
@@ -45,7 +37,3 @@ for _ in range(0, epoch):
     plot.update("test", test_err)
     plot.update_limits()
 """
-
-plot.wait()
-
-
diff --git a/src/learning/data.py b/src/learning/data.py
index 6b6afb9..944fbfa 100644
--- a/src/learning/data.py
+++ b/src/learning/data.py
@@ -17,24 +17,25 @@ class Dataset:
         self.target = target
         self.classification = (data[target].dtype == object)
 
-    def regularize(self, excepts:list=[]) -> Self:
+    def regularize(self, excepts:list[str]=[]) -> Self:
         excepts.append(self.target)
         excepts.append("Bias")
         for col in self.data:
             if col not in excepts:
-                dt = self.data[col]
-                self.data[col] = (dt - dt.mean()) / dt.std()
+                datacol = self.data[col]
+                datacol = (datacol - datacol.mean()) / datacol.std()
+                self.data[col] = datacol
         return self
 
-    def factorize(self, columns:list=[]) -> Self:
+    def factorize(self, columns:list[str]=[]) -> Self:
         data = self.data
         for col in columns:
             data[col] = pd.factorize(data[col])[0]
         return self
 
-    def to_numbers(self, columns:list=[]) -> Self:
+    def to_numbers(self, columns:list[str]=[]) -> Self:
         data = self.data
-        for col in self.data.columns:
+        for col in columns:
             if data[col].dtype == object:
                 data[col] = pd.to_numeric(data[col], errors='coerce')
         return self
@@ -64,3 +65,13 @@ class PrincipalComponentAnalisys:
         if threshold <= 0 or threshold > 1:
             threshold = 1
 
+
+
+if __name__ == "__main__":
+    df = Dataset("datasets\\regression\\automobile.csv", "symboling")
+    attributes_to_modify = ["fuel-system", "engine-type", "drive-wheels", "body-style", "make", "engine-location", "aspiration", "fuel-type", "num-of-cylinders", "num-of-doors"]
+    df.factorize(attributes_to_modify)
+    df.to_numbers(["normalized-losses", "bore", "stroke", "horsepower", "peak-rpm", "price"])
+    df.handle_na()
+    df.regularize(excepts=attributes_to_modify)
+    print(df.data.dtypes)
diff --git a/src/learning/ml.py b/src/learning/ml.py
index f6f543c..38834d5 100644
--- a/src/learning/ml.py
+++ b/src/learning/ml.py
@@ -1,14 +1,18 @@
 from abc import ABC, abstractmethod
 from learning.data import Dataset
+from plot import Plot
 
 import numpy as np
 
 
 class MLAlgorithm(ABC):
+    """ Classe generica per gli algoritmi di Machine Learning """
 
     dataset: Dataset
     testset: np.ndarray
     learnset: np.ndarray
+    test_error: list[float]
+    train_error: list[float]
 
     def _set_dataset(self, dataset:Dataset, split:float=0.2):
         ndarray = dataset.shuffle().as_ndarray()
@@ -30,6 +34,9 @@ class MLAlgorithm(ABC):
         for _ in range(0, max(1, times)):
             train.append(self.learning_step())
             test.append(self.test_error())
+
+        self.train_error = train
+        self.test_error = test
         return (train, test)
 
     @abstractmethod
@@ -39,3 +46,15 @@ class MLAlgorithm(ABC):
     @abstractmethod
     def test_error(self) -> float:
         pass
+
+    @abstractmethod
+    def plot(self, skip:int=1000) -> None:
+        pass
+
+
+class MLRegression(MLAlgorithm):
+    def plot(self, skip:int=1000) -> None:
+        plot = Plot("Error", "Time", "Mean Error")
+        plot.line("training", "red", data=self.train_error[skip:])
+        plot.line("test", "blue", data=self.test_error[skip:])
+        plot.wait()
diff --git a/src/learning/supervised.py b/src/learning/supervised.py
index e451bce..e34c87f 100644
--- a/src/learning/supervised.py
+++ b/src/learning/supervised.py
@@ -1,10 +1,10 @@
 import math as math
 import numpy as np
 
-from ml import MLAlgorithm
+from learning.ml import MLRegression
 from learning.data import Dataset
 
-class LinearRegression(MLAlgorithm):
+class LinearRegression(MLRegression):
     def __init__(self, dataset:Dataset, learning_rate:float=0.1) -> None:
         self._set_dataset(dataset)