diff --git a/tester.py b/tester.py
deleted file mode 100644
index f348237..0000000
--- a/tester.py
+++ /dev/null
@@ -1,69 +0,0 @@
-import missing_value as mv
-import dimensionality_reduction as dr
-import feature_selection as fs
-import balance_data as bd
-import outlier as out
-import pandas as pd
-from sklearn.model_selection import train_test_split, GridSearchCV
-from sklearn.ensemble import RandomForestClassifier
-from sklearn.metrics import accuracy_score, recall_score, precision_score, f1_score,classification_report, confusion_matrix
-from sklearn.linear_model import LogisticRegression
-import numpy as np
-from sklearn.model_selection import StratifiedKFold
-from sklearn.tree import DecisionTreeClassifier 
-from sklearn.ensemble import RandomForestRegressor, VotingRegressor, RandomForestClassifier, VotingClassifier
-from sklearn.ensemble import (ExtraTreesClassifier, 
-                              GradientBoostingClassifier, 
-                              HistGradientBoostingClassifier)
-from lightgbm import LGBMRegressor, LGBMClassifier
-from xgboost import XGBRegressor, XGBClassifier
-from catboost import CatBoostRegressor, CatBoostClassifier
-from sklearn.metrics import (balanced_accuracy_score as bas, 
-                             confusion_matrix)
-
-import Preprocessor as preprocess
-import OutlierHandler as outlierhandler
-import FeatureSelector as featureselector
-import DimensionReducer
-import BalanceData as balancedata
-import missing_value as mv
-import feature_selection as fs
-import Veda
-
-df = pd.read_csv('data\\train1.csv')
-# df2 = pd.read_csv('data\\test1.csv')
-
-# df = pd.concat([df, df2], ignore_index=True)
-
-X = df.drop(['Loan Status'],axis=1)
-y = df['Loan Status']
-
-print("Initial shape:  ", X.shape, " and ", y.shape, " and ", type(y))
-
-obj = Veda.Veda(classification=True)
-X, y, outlier, strategy, model =  obj.fit_transform(X, y)
-print(strategy)
-# preprocess_obj = preprocess.DataPreprocessor()
-# X, y = preprocess_obj.fit_transform(X, y)
-
-# out = outlierhandler.OutlierPreprocessor()
-# outlier, X, y  = out.fit_transform(X, y)
-
-# featuresel = featureselector.FeatureSelectionPipeline()
-# X, y = featuresel.fit_transform(X, y)
-
-
-# dimred = DimensionReducer.DimensionReducer()
-# X, y = dimred.fit_transform(X, y)
-
-# bdata = balancedata.AdaptiveBalancer(classification=True)
-# X, y, strategy, model = bdata.fit_transform(X, y)
-
-print("Final shape:  ", X.shape, " and ", y.shape)
-
-new_df = X.copy()  # Make a copy of X to avoid any unwanted changes
-new_df['target'] = y.values 
-
-new_df.to_csv('cleaned_data\\delloite1.csv', index=False)
-
-# new_df.to_csv('cleaned_data\\delloite.csv', index=False)
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