diff --git a/.gitignore b/.gitignore
index c6f9a44..b97b406 100644
--- a/.gitignore
+++ b/.gitignore
@@ -1 +1,3 @@
 .vscode/settings.json
+Hausarbeit/__pycache__
+Beispielcode*
diff --git a/Hausarbeit/Beispielcode von mir.md b/Hausarbeit/Beispielcode von mir.md
deleted file mode 100644
index 206cdf8..0000000
--- a/Hausarbeit/Beispielcode von mir.md	
+++ /dev/null
@@ -1,84 +0,0 @@
-```
-# https://towardsdatascience.com/five-regression-python-modules-that-every-data-scientist-must-know-a4e03a886853
-#   based on: https://machinelearningmastery.com/how-to-use-correlation-to-understand-the-relationship-between-variables/
-
-# pip3 install openpyxl
-import os
-import pandas as pd  # To read data
-import math as m
-import numpy as np
-import scipy as sp 
-
-from scipy import stats
-import matplotlib.pyplot as plt  # To visualize
-
-# location will help to open files in the same directory as the py-script
-__location__ = os.path.realpath(
-    os.path.join(os.getcwd(), os.path.dirname(__file__)))
-
-df = pd.read_excel(os.path.join(__location__,'Daten_Umfrage_SPSS_20211113.xlsx'))
-
-df = df.apply(pd.to_numeric, errors='coerce')          # convert non-numeric values to NaN, e.g. Header "row 1" "CodeXYZ" -> "row 1" "NaN"
-print("Dataframe (Zeilen, Spalten, ...) inkl. NaN:", df.shape)
-print(df.head(10))
-#        Code     SE01_01  SE01_02  SE02_01  SE02_02  SE03_01 
-# 0      NaN      NaN      NaN      NaN      NaN      NaN   
-# 1      NaN      6.0      4.0      7.0      4.0      5.0   
-# ...
-#df = df.dropna()                                   # CAUTION: drops every row that even contains single NaN !
-
-print(df.tail(10))
-#          Code     SE01_01  SE01_02  SE02_01  SE02_02  SE03_01  
-# 155      NaN      4.0      4.0      3.0      5.0      1.0  
-# 156      NaN      NaN      NaN      NaN      NaN      NaN 
-# (End of File)
-
-#print(df["HO_Score_Bewerbung_Gewichtet"][105:110])
-#for col in df.columns:
-        #print(col)
-
-# Calculate Mean, gew, inv
-mwHO01_Diff = df["HO01_Diff"][1:156]            # Limit to Column and row Amount
-mwHO01_Diff = mwHO01_Diff.mean(skipna=True)     # Columns arithm. mean, skipna to ignore NaN rows
-mwHO01_Diff = round(mwHO01_Diff, 2)
-normHO01_Diff = m.sqrt((mwHO01_Diff / 6)**2)    # Norm
-invHO01_Diff = 1 - normHO01_Diff                # invert
-# usw
-print("HO01_Diff Mittelwert:", mwHO01_Diff)
-print("HO01_Diff Normiert:", normHO01_Diff)
-print("HO01_Diff Invertiert:", invHO01_Diff)
-# usw
-
-# Choose Dataframe Columns and row Amount
-dfColumnX = df["SS_Score"][1:156]
-dfColumnY = df["HO_Score_Bewerbung_Gewichtet"][1:156]
-
-# Convert Dataframe Columns to Array containing the X- and Y- Values
-arrX = np.asarray(dfColumnX)                         # convert that dataframe column to numpy array
-arrY = np.asarray(dfColumnY)                         
-
-# Prepare Plot Image
-plt.xlabel('SS_Score', color='black')
-plt.ylabel('HO_Score_Bewerbung_Gewichtet', color='black')
-plt.xlim([0,50])                                     # set x-Axis View Range,[from,to]
-plt.scatter(arrX, arrY)
-
-arrX, arrY = zip(*sorted(zip(arrX,arrY)))            # sort 2 arrays in sync 
-
-# Convert again, as sorting seemed to break the numpy array data format
-arrX = np.asarray(arrX)                              # before: "1   16.0" after: "[16. 18. 21. ...]"
-arrY = np.asarray(arrY)
-
-# Use least Square Linear Regression from SciPy Stats
-regr_results = sp.stats.linregress(arrX, arrY)   
-
-steigung = regr_results.slope
-yAchsAbschn = regr_results.intercept
-arrYpredicted = steigung * arrX + yAchsAbschn        # using y = m*x + n, calculate every single Y-Value fitting the regression Lines X-Values
-
-print("y =", steigung, "* x +", yAchsAbschn)
-
-# Plot Linear Regression Line
-plt.plot(arrX, arrYpredicted, label='Lin Regr', color='red', linestyle='solid') # https://scriptverse.academy/tutorials/python-matplotlib-plot-straight-line.html
-plt.show()
-```
\ No newline at end of file
diff --git a/Hausarbeit/__pycache__/mobile_device_data.cpython-37.pyc b/Hausarbeit/__pycache__/mobile_device_data.cpython-37.pyc
deleted file mode 100644
index 1b6bad8..0000000
Binary files a/Hausarbeit/__pycache__/mobile_device_data.cpython-37.pyc and /dev/null differ