Merge pull request #2 from grknc/YG-13-modelleme

Update Telco_Churn_Feature_Engineering.py

Telco_Churn_Feature_Engineering.py

@@ -409,8 +409,6 @@ def cat_summary(dataframe,col_name):
 
 df.groupby("INT_SEC_SERV_GENDER").agg({"Churn": ["mean","count"]})
 
-
-
 ###############################################
 # * 2.3.Processing Encoding and One-Hot Encoding
 ###############################################
@@ -437,7 +435,6 @@ def one_hot_encoder(dataframe, categorical_cols, drop_first=True):
 ohe_cols = [col for col in df.columns if 30 >= df[col].nunique() > 2]
 
 df = one_hot_encoder(df, ohe_cols)
-
 df.head()
 
 ###############################################
@@ -461,4 +458,4 @@ def one_hot_encoder(dataframe, categorical_cols, drop_first=True):
 from sklearn.ensemble import RandomForestClassifier
 rf_model = RandomForestClassifier(random_state=46).fit(X_train, y_train)
 y_pred = rf_model.predict(X_test)
-accuracy_score(y_pred, y_test)
+accuracy_score(y_pred, y_test)