main.py works but I think it can be improved. But I don't know how...

main.py

@@ -15,7 +15,6 @@ FEATURES = ["points", "x", "y"]
 def load_dataframe():
     try:
         colum_list = FEATURES
-        #df = pd.read_csv("data/shots_dev.csv", usecols = colum_list).dropna()
         df = pd.read_csv("data/shots.csv", usecols = colum_list).dropna()
         return df
     except FileNotFoundError as error:
@@ -34,8 +33,16 @@ def calc_f1_score(y_true, y_pred):
     tn = np.sum(np.multiply([i==False for i in y_pred], [not(j) for j in y_true]))
     fp = np.sum(np.multiply([i==True for i in y_pred], [not(j) for j in y_true]))
     fn = np.sum(np.multiply([i==False for i in y_pred], y_true))
-    precision = calc_precision(tp, fp)
-    recall = calc_recall(tp, fn)
+    
+    if tp != 0 and fp != 0:
+        precision = calc_precision(tp, fp)
+    else:
+        precision = 0
+    
+    if tp != 0 and fn != 0:
+        recall = calc_recall(tp, fn)
+    else:
+        recall = 0
     
     if precision != 0 and recall != 0:
         f1 = (2 * precision * recall) / (precision + recall)
@@ -49,11 +56,20 @@ def calc_precision(tp, fp):
 def calc_recall(tp, fn):
     return tp / (tp + fn)
 
+def get_score_from_cli():
+    try:
+        culmen_depth = float(input("x: "))
+        culmen_length = float(input("y: "))
+        return np.array([culmen_depth, culmen_length]).reshape(1, -1)
+    except ValueError:
+        print("Invalid input. Please enter numeric values.")
+        return None
+
 def main():
     df = load_dataframe()
-    #print(df.head())
+    '''print(df.head())
 
-    '''sns.countplot(x = df["points"])
+    sns.countplot(x = df["points"])
     plt.show()
 
     sns.heatmap(df.corr(), annot=True, cmap='coolwarm')
@@ -65,6 +81,7 @@ def main():
     features = ["x", "y"]
 
     X = df[features]
+    
     y = pd.get_dummies(df['points'])
 
     X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.4, random_state=0)
@@ -86,10 +103,21 @@ def main():
         pred = model.predict(X_test.values)
 
         my_f1_macro_score = calc_f1_macro(y_test, pd.DataFrame(pred))
-        print(f'My F1 score of {name} is {my_f1_macro_score}')
+        print(f'My F1 score of {name} is {my_f1_macro_score}\n')
         
         f1_sklearn = f1_score(y_test.values, pred, average='macro')
-        print(f'Sklearn F1 score of {name} is {f1_sklearn}')
+        print(f'Sklearn F1 score of {name} is {f1_sklearn}\n')
+
+    score = get_score_from_cli()
+
+    label_encoder = LabelEncoder()
+    df["points"] = label_encoder.fit_transform(df["points"])
+
+    for name, model in models.items():
+        pred = model.predict(score)
+        points_number = pd.DataFrame(pred).idxmax(axis=1)
+        points = label_encoder.inverse_transform(points_number)[0]
+        print(f"{name}: {points} Punkte")
 
 
 if __name__ == "__main__":