diff --git a/test_script.py b/test_script.py
deleted file mode 100644
index de7e833..0000000
--- a/test_script.py
+++ /dev/null
@@ -1,133 +0,0 @@
-
-
-#### INITIALIZE
-
-import numpy as np
-import pandas as pd
-from sklearn import set_config
-set_config(transform_output="pandas") # dataframe supremacy
-
-# load data
-# appid,name,release_date,required_age,price,dlc_count,detailed_description,about_the_game,short_description,reviews,header_image,website,support_url,support_email,windows,mac,linux,metacritic_score,metacritic_url,achievements,recommendations,notes,supported_languages,full_audio_languages,packages,developers,publishers,categories,genres,screenshots,movies,user_score,score_rank,positive,negative,estimated_owners,average_playtime_forever,average_playtime_2weeks,median_playtime_forever,median_playtime_2weeks,discount,peak_ccu,tags,pct_pos_total,num_reviews_total,pct_pos_recent,num_reviews_recent
-dataset = pd.read_csv("./games_march2025_cleaned_2k.csv",sep=",")
-print(dataset.head())
-
-
-
-
-#### DROP UNIQUES
-print("DROP")
-
-#TODO: wird eh unten beim transformer deleted
-
-# appid,name,release_date,required_age,price,dlc_count,detailed_description,about_the_game,short_description,reviews,header_image,website,support_url,support_email,windows,mac,linux,metacritic_score,metacritic_url,achievements,recommendations,notes,supported_languages,full_audio_languages,packages,developers,publishers,categories,genres,screenshots,movies,user_score,score_rank,positive,negative,estimated_owners,average_playtime_forever,average_playtime_2weeks,median_playtime_forever,median_playtime_2weeks,discount,peak_ccu,tags,pct_pos_total,num_reviews_total,pct_pos_recent,num_reviews_recent
-#dataset.drop(['appid', 'name', 'release_date', 'reviews', 'header_image', 'website', 'support_url', 'support_email',
-#              'metacritic_url', 'notes', 'developers', 'publishers', 'screenshots', 'movies', 'estimated_owners'],
-#              axis=1, inplace=True)
-#print(dataset.head())
-
-#### STRUCTURIZE AND STANDARDIZE
-print("STRUCTURE")
-
-from sklearn.compose import ColumnTransformer
-from sklearn.preprocessing import FunctionTransformer
-
-
-# desc, genres, tags
-column_transformer = ColumnTransformer([
-        # merge all descriptions
-        ('desc', FunctionTransformer(lambda X: X.fillna('').agg(' '.join, axis=1).to_frame(name="desc")),
-            ['detailed_description', 'about_the_game', 'short_description']),
-        # genre -> actual genre, but very coarse
-        # tags -> user defined tags; title num list
-        #TODO: decide whether we drop tags
-        ('pass', 'passthrough', ['genres']),#, 'tags'
-    ],
-    verbose_feature_names_out=False
-)
-dataset = column_transformer.fit_transform(dataset)
-print(dataset)
-
-
-
-#### SET MISSING VALUES
-print("SETMISS")
-
-
-# Setting missing numeric values to the mean
-dataset.fillna(dataset.mean(numeric_only=True), inplace=True)
-# Setting missing text values to 'Unknown'
-dataset.fillna('', inplace=True)
-# Setting missing values in other columns to NaN
-dataset.dropna(inplace=True)
-
-
-
-
-##### STRUCTURIZE GENRES to onehot
-from sklearn.preprocessing import MultiLabelBinarizer
-import ast
-#serialize array
-dataset['genres'] = dataset['genres'].map(lambda s: ast.literal_eval(s)) 
-print(dataset['genres']) # in py but not yet onehotenc
-
-# MultiLabelBinarizer does onehotenc for arrays
-mlb_genres = MultiLabelBinarizer()
-genres_encoded = mlb_genres.fit_transform(dataset.pop('genres'))
-genres_count = len(mlb_genres.classes_) # for multi-label classifiction later
-
-genres_df = pd.DataFrame(genres_encoded, columns=mlb_genres.classes_)
-print(genres_df)
-#dataset = pd.concat([dataset, genres_df], axis=1)
-#print(dataset)
-
-
-#### convert text to bag of words
-
-## Count vs Tfidf vectorizer
-from sklearn.feature_extraction.text import TfidfVectorizer
-vectorizer = TfidfVectorizer()
-tfidf_matrix = vectorizer.fit_transform(dataset['desc']) # matrix
-tfidf_df = pd.DataFrame(tfidf_matrix.toarray(), columns=vectorizer.get_feature_names_out())
-print(tfidf_df)
-
-
-##### MODEL
-print("MODEL")
-
-from sklearn.linear_model import LogisticRegression
-from sklearn.multioutput import MultiOutputClassifier
-from sklearn.metrics import classification_report
-
-
-X = tfidf_df
-y = genres_df
-
-
-# cleanup datapoints that dont have a target value (all target columns are 0)
-mask = y.sum(axis=1).map(lambda x: x > 0)
-#print((mask == False).sum()) #31 cases with all target columns 0
-X_clean = X[mask]
-y_clean = y[mask]
-
-# Split dataset
-from sklearn.model_selection import train_test_split
-X_train, X_test, y_train, y_test = train_test_split(X_clean, y_clean, random_state=0)
-
-
-# we want to have multiple possible outputs (multi-label-classficiation) -> multioutputclassifier
-# logi regression is our base system
-# n_jobs=1 since there seems to be some multithreading join issue in sklearn (or my pc is too bad)
-multi_target_clf = MultiOutputClassifier(LogisticRegression(max_iter=1337, random_state=0), n_jobs=1)
-
-# model training
-multi_target_clf.fit(X_train, y_train)
-
-# predict against test data
-y_pred = multi_target_clf.predict(X_test)
-
-# print prec, recall, f1 etc
-print(classification_report(y_test, y_pred, zero_division=0.0))
-
-
-#print(f"Trainingsdaten: {X_train.shape}, Testdaten: {X_test.shape}")