🐛 Set in the dataframe all fighters fight for beter accuracy

test.py

@@ -4,11 +4,7 @@ import numpy as np
 import seaborn as sns
 import matplotlib.pyplot as plt
 from sklearn.tree import export_graphviz
-from io import StringIO 
-from IPython.display import Image
 from sklearn.tree import plot_tree
-import pydotplus
-from IPython.display import Image
 from sklearn.pipeline import Pipeline
 from sklearn.ensemble import RandomForestClassifier
 from sklearn.metrics import accuracy_score
@@ -153,24 +149,31 @@ def build_df(df, fighters, i):
     return df_fights
     
 def build_df_all_but_last(df, fighters):    
-    
     cols = [col for col in df] 
     df_fights=pd.DataFrame(columns=cols)
     for f in range(len(fighters)):
-        for i in range(10000):
+        i=0
+        while True:
             fight_row = select_fight_row(df, fighters[f], i)
             if fight_row is None:
+                if not df_fights.empty:
+                    df_fights = df_fights.iloc[:-1]
                 break
             fight_row = list(fight_row) 
             dfTemp = pd.DataFrame(data=[fight_row], columns=cols)
-            df_fights = df_fights._append(dfTemp, ignore_index=True)
+            df_fights = df_fights.dropna(axis=1, how='all')
+            df_fights = pd.concat([df_fights, dfTemp], ignore_index=True)
+            i=i+1
     df_fights.drop_duplicates(inplace=True)
+    df_fights = df_fights[~df_fights.apply(lambda row: 'Open Stance' in row.values, axis=1)].reset_index(drop=True)
     df_fights['title_bout'] = df_fights['title_bout'].map({True: 1, False: 0})
     df_fights.drop(['R_fighter', 'B_fighter', 'date'], axis=1, inplace=True)
+
     return df_fights
 
+
 df_train = build_df_all_but_last(df, fighters)
-df_test = build_df(df, fighters,-1)
+df_test = build_df(df, fighters,0)
 
 preprocessor = make_column_transformer((OrdinalEncoder(), ['weight_class', 'B_Stance', 'R_Stance']), remainder='passthrough')