Update app.py

app.py

@@ -1,8 +1,8 @@
 import streamlit as st
 import pandas as pd
-from sklearn.linear_model import LinearRegression, LogisticRegression
+from sklearn.ensemble import RandomForestRegressor, RandomForestClassifier
 from sklearn.impute import SimpleImputer
-from sklearn.preprocessing import LabelEncoder
+from sklearn.preprocessing import LabelEncoder, OneHotEncoder
 from sklearn.metrics import r2_score, accuracy_score
 
 # Streamlit başlığı
@@ -39,18 +39,18 @@ if train_file and test_file:
             return train_df, test_df, None
 
         if model_type == "numeric":
-            model = LinearRegression()
+            model = RandomForestRegressor()
             X_train = not_null_train.drop(columns=[column])
             y_train = not_null_train[column]
             X_test = is_null_train.drop(columns=[column])
         else:
-            model = LogisticRegression(max_iter=1000)
+            model = RandomForestClassifier()
             le = LabelEncoder()
             y_train = le.fit_transform(not_null_train[column].astype(str))
             X_train = not_null_train.drop(columns=[column])
             X_test = is_null_train.drop(columns=[column])
 
-        # Kategorik veriler için dummy değişkenler oluşturma
+        # Kategorik veriler için One-Hot Encoding
         X_train = pd.get_dummies(X_train, drop_first=True)
         X_test = pd.get_dummies(X_test, drop_first=True)