diff --git a/DM_Gruppenarbeit-main/copyright.txt b/DM_Gruppenarbeit-main/copyright.txt
new file mode 100644
index 0000000000000000000000000000000000000000..72fe0e11b2ff9aa811a45b881ec491dd911a9343
--- /dev/null
+++ b/DM_Gruppenarbeit-main/copyright.txt
@@ -0,0 +1,5 @@
+Data mining group project between:
+
+- Wojcik, D.
+- Mucherla, S
+- Maurer, E.
\ No newline at end of file
diff --git a/DM_Gruppenarbeit-main/data/test.txt b/DM_Gruppenarbeit-main/data/test.txt
new file mode 100644
index 0000000000000000000000000000000000000000..8b137891791fe96927ad78e64b0aad7bded08bdc
--- /dev/null
+++ b/DM_Gruppenarbeit-main/data/test.txt
@@ -0,0 +1 @@
+
diff --git a/DM_Gruppenarbeit-main/datenvorverarbeitung/Correlation.py b/DM_Gruppenarbeit-main/datenvorverarbeitung/Correlation.py
new file mode 100644
index 0000000000000000000000000000000000000000..f7895a66d379f3124c007cba1fe7debb239b9b5c
--- /dev/null
+++ b/DM_Gruppenarbeit-main/datenvorverarbeitung/Correlation.py
@@ -0,0 +1,54 @@
+import pandas as pd
+import numpy as np
+import matplotlib.pyplot as plt
+
+# Load the data
+data_Series_Train = pd.read_csv('CsvDateien/SeriesTrain/10MinSeries.csv')
+data_train = pd.read_csv('cleaned_train.csv')
+
+
+# Merge the datasets on the common column 'id'
+data_combined = pd.merge(data_Series_Train, data_train, on='id', how='inner')
+
+# Select numeric columns
+numeric_data = data_combined.select_dtypes(include=[np.number])
+
+# Ensure PCIAT-PCIAT_Total is included
+if 'PCIAT-PCIAT_Total' not in numeric_data.columns:
+ raise ValueError("The column 'PCIAT-PCIAT_Total' is not present in the data.")
+
+# Calculate correlations with 'PCIAT-PCIAT_Total'
+corr_with_pciat = numeric_data.corr()[['PCIAT-PCIAT_Total']]
+
+# Plot the correlation matrix
+plt.figure(figsize=(6, len(corr_with_pciat) // 2))
+plt.title('Correlation with PCIAT-PCIAT_Total', fontsize=16)
+plt.barh(corr_with_pciat.index, corr_with_pciat['PCIAT-PCIAT_Total'], color='skyblue')
+plt.xlabel('Correlation Coefficient')
+plt.ylabel('Variables')
+plt.grid(axis='x', linestyle='--', alpha=0.7)
+plt.tight_layout()
+plt.show()
+
+
+# # Select numeric columns for correlation matrix
+# corr_Series_Train = data_Series_Train.select_dtypes(include=[np.number]).corr()
+# corr_train = data_train.select_dtypes(include=[np.number]).corr()
+
+# # Plot the correlation matrix for data_Series_Train
+# plt.figure(figsize=(12, 10))
+# plt.title('Correlation Matrix - Series Train Data', fontsize=16)
+# plt.imshow(corr_Series_Train, cmap='coolwarm', interpolation='nearest')
+# plt.colorbar()
+# plt.xticks(range(corr_Series_Train.shape[1]), corr_Series_Train.columns, rotation=90)
+# plt.yticks(range(corr_Series_Train.shape[0]), corr_Series_Train.columns)
+# plt.show()
+
+# # Plot the correlation matrix for data_train
+# plt.figure(figsize=(12, 10))
+# plt.title('Correlation Matrix - Train Data', fontsize=16)
+# plt.imshow(corr_train, cmap='coolwarm', interpolation='nearest')
+# plt.colorbar()
+# plt.xticks(range(corr_train.shape[1]), corr_train.columns, rotation=90)
+# plt.yticks(range(corr_train.shape[0]), corr_train.columns)
+# plt.show()
diff --git a/DM_Gruppenarbeit-main/datenvorverarbeitung/CsvBearbeiten.py b/DM_Gruppenarbeit-main/datenvorverarbeitung/CsvBearbeiten.py
new file mode 100644
index 0000000000000000000000000000000000000000..c82d77debf75edd2c83a191f1a5feae245adb0fa
--- /dev/null
+++ b/DM_Gruppenarbeit-main/datenvorverarbeitung/CsvBearbeiten.py
@@ -0,0 +1,51 @@
+import pandas as pd
+import os
+
+def change_csv():
+
+ path_to_test_data = f'CsvDateien/ParquettCleanCsv/TrainDaten'
+
+ csv_files = [f for f in os.listdir(path_to_test_data) if f.endswith('.csv')]
+
+ for file in csv_files:
+ print(file)
+
+ divide_by = 1000000000
+ df = pd.read_csv(f'CsvDateien/ParquettCleanCsv/TrainDaten/{file}')
+
+ #drops unnecessary columns
+ columns_to_remove = ["X", "Y", "Z", "anglez", "battery_voltage", "relative_date_PCIAT"]
+ df = df.drop(columns=columns_to_remove, errors='ignore')
+
+ print(type(df["time_of_day"]))
+
+ df["time_of_day"] = pd.to_datetime(df["time_of_day"] // divide_by, unit='s').dt.time
+
+ light_binary_value(df)
+
+ df.to_csv(f'CsvDateien/NutzerIdentifikationCsv/TrainDaten/{file}')
+
+def light_binary_value(df):
+ condition_inner_space = (
+ ((0 <= df["light"]) & (df["light"] <= 50)) |
+ ((80 <= df["light"]) & (df["light"] <= 100)) |
+ ((140 <= df["light"]) & (df["light"] <= 399))
+ )
+
+ # 1 Twilight
+ # 5 Minimal Street Lighting
+ # 10 Sunset
+ # 50 Family Living Room
+ # 80 Hallway
+ # 100 Very Dark Overcast Day
+ # 320 - 500 Office Lighting
+ # 400 Sunrise/Sunset
+ # 1,000 Overcast Day
+ # 10,000-25,000 Full Daylight
+ # 32,000-130,000 Direct Sunlight
+
+ df.loc[condition_inner_space, "light"] = 0
+ df.loc[~condition_inner_space, "light"] = 1
+
+
+change_csv()
\ No newline at end of file
diff --git a/DM_Gruppenarbeit-main/datenvorverarbeitung/CsvDateien/NutzerIdentifikationCsv/TestDaten/anker.txt b/DM_Gruppenarbeit-main/datenvorverarbeitung/CsvDateien/NutzerIdentifikationCsv/TestDaten/anker.txt
new file mode 100644
index 0000000000000000000000000000000000000000..8b137891791fe96927ad78e64b0aad7bded08bdc
--- /dev/null
+++ b/DM_Gruppenarbeit-main/datenvorverarbeitung/CsvDateien/NutzerIdentifikationCsv/TestDaten/anker.txt
@@ -0,0 +1 @@
+
diff --git a/DM_Gruppenarbeit-main/datenvorverarbeitung/CsvDateien/NutzerIdentifikationCsv/TrainDaten/anker.txt b/DM_Gruppenarbeit-main/datenvorverarbeitung/CsvDateien/NutzerIdentifikationCsv/TrainDaten/anker.txt
new file mode 100644
index 0000000000000000000000000000000000000000..8b137891791fe96927ad78e64b0aad7bded08bdc
--- /dev/null
+++ b/DM_Gruppenarbeit-main/datenvorverarbeitung/CsvDateien/NutzerIdentifikationCsv/TrainDaten/anker.txt
@@ -0,0 +1 @@
+
diff --git a/DM_Gruppenarbeit-main/datenvorverarbeitung/CsvDateien/NutzerIdentifikationCsv/test.txt b/DM_Gruppenarbeit-main/datenvorverarbeitung/CsvDateien/NutzerIdentifikationCsv/test.txt
new file mode 100644
index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391
diff --git a/DM_Gruppenarbeit-main/datenvorverarbeitung/CsvDateien/ParquettCleanCsv/TestDaten/test.txt b/DM_Gruppenarbeit-main/datenvorverarbeitung/CsvDateien/ParquettCleanCsv/TestDaten/test.txt
new file mode 100644
index 0000000000000000000000000000000000000000..8b137891791fe96927ad78e64b0aad7bded08bdc
--- /dev/null
+++ b/DM_Gruppenarbeit-main/datenvorverarbeitung/CsvDateien/ParquettCleanCsv/TestDaten/test.txt
@@ -0,0 +1 @@
+
diff --git a/DM_Gruppenarbeit-main/datenvorverarbeitung/CsvDateien/ParquettCleanCsv/TrainDaten/test.txt b/DM_Gruppenarbeit-main/datenvorverarbeitung/CsvDateien/ParquettCleanCsv/TrainDaten/test.txt
new file mode 100644
index 0000000000000000000000000000000000000000..8b137891791fe96927ad78e64b0aad7bded08bdc
--- /dev/null
+++ b/DM_Gruppenarbeit-main/datenvorverarbeitung/CsvDateien/ParquettCleanCsv/TrainDaten/test.txt
@@ -0,0 +1 @@
+
diff --git a/DM_Gruppenarbeit-main/datenvorverarbeitung/CsvDateien/ParquettCleanCsv/test.txt b/DM_Gruppenarbeit-main/datenvorverarbeitung/CsvDateien/ParquettCleanCsv/test.txt
new file mode 100644
index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391
diff --git a/DM_Gruppenarbeit-main/datenvorverarbeitung/CsvDateien/SeriesTest/anker.txt b/DM_Gruppenarbeit-main/datenvorverarbeitung/CsvDateien/SeriesTest/anker.txt
new file mode 100644
index 0000000000000000000000000000000000000000..8b137891791fe96927ad78e64b0aad7bded08bdc
--- /dev/null
+++ b/DM_Gruppenarbeit-main/datenvorverarbeitung/CsvDateien/SeriesTest/anker.txt
@@ -0,0 +1 @@
+
diff --git a/DM_Gruppenarbeit-main/datenvorverarbeitung/CsvDateien/SeriesTrain/30MinSeries.csv b/DM_Gruppenarbeit-main/datenvorverarbeitung/CsvDateien/SeriesTrain/30MinSeries.csv
new file mode 100644
index 0000000000000000000000000000000000000000..c7ed7eaf920e6be19552f48bccea9950a2cad204
Binary files /dev/null and b/DM_Gruppenarbeit-main/datenvorverarbeitung/CsvDateien/SeriesTrain/30MinSeries.csv differ
diff --git a/DM_Gruppenarbeit-main/datenvorverarbeitung/CsvDateien/Test/cleaned_test.csv b/DM_Gruppenarbeit-main/datenvorverarbeitung/CsvDateien/Test/cleaned_test.csv
new file mode 100644
index 0000000000000000000000000000000000000000..2906d8d5ba90e2f6fe1c4677891043de1aedfb2c
--- /dev/null
+++ b/DM_Gruppenarbeit-main/datenvorverarbeitung/CsvDateien/Test/cleaned_test.csv
@@ -0,0 +1,21 @@
+id,Basic_Demos-Age,Basic_Demos-Sex,CGAS-Season,CGAS-CGAS_Score,Physical-Season,Physical-BMI,Physical-Diastolic_BP,Physical-HeartRate,Physical-Systolic_BP,Fitness_Endurance-Season,Fitness_Endurance-Max_Stage,FGC-Season,FGC-FGC_CU_Zone,FGC-FGC_GSND_Zone,FGC-FGC_GSD_Zone,FGC-FGC_PU_Zone,FGC-FGC_SRL_Zone,FGC-FGC_SRR_Zone,FGC-FGC_TL_Zone,BIA-Season,BIA-BIA_Activity_Level_num,BIA-BIA_BMC,BIA-BIA_BMI,BIA-BIA_BMR,BIA-BIA_DEE,BIA-BIA_ECW,BIA-BIA_FFM,BIA-BIA_FFMI,BIA-BIA_FMI,BIA-BIA_Fat,BIA-BIA_Frame_num,BIA-BIA_ICW,BIA-BIA_LDM,BIA-BIA_LST,BIA-BIA_SMM,BIA-BIA_TBW,SDS-SDS_Total_T,PreInt_EduHx-Season,PreInt_EduHx-computerinternet_hoursday,Fitness_Endurance-Total_Time_Seconds,PAQ-Season,PAQ-PAQ_Total
+00008ff9,5,0,4.0,51.0,3.0,16.87731569,,,,,,3.0,0.0,,,0.0,0.0,0.0,1.0,3.0,2.0,2.66855,16.8792,932.498,1492.0,8.25598,41.5862,13.8177,3.06143,9.21377,1.0,24.4349,8.89536,38.9177,19.5413,32.6909,,3.0,3.0,0.0,,
+000fd460,9,0,,,3.0,14.03559028,75.0,70.0,122.0,,,3.0,0.0,,,0.0,1.0,1.0,0.0,4.0,2.0,2.57949,14.0371,936.656,1498.65,6.01993,42.0291,12.8254,1.21172,3.97085,1.0,21.0352,14.974,39.4497,15.4107,27.0552,64.0,2.0,0.0,0.0,3.0,2.34
+00105258,10,1,3.0,71.0,3.0,16.64869606,65.0,94.0,117.0,3.0,5.0,3.0,1.0,1.0,2.0,1.0,1.0,1.0,0.0,,,,,,,,,,,,,,,,,,54.0,2.0,2.0,453.0,2.0,2.17
+00115b9f,9,0,3.0,71.0,2.0,18.29234694,60.0,97.0,117.0,2.0,6.0,2.0,1.0,,,0.0,0.0,0.0,1.0,2.0,3.0,3.84191,18.2943,1131.43,1923.44,15.5925,62.7757,14.074,4.22033,18.8243,2.0,30.4041,16.779,58.9338,26.4798,45.9966,45.0,4.0,0.0,577.0,4.0,2.451
+0016bb22,18,1,2.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,2.0,1.04
+001f3379,13,1,4.0,50.0,2.0,22.27995198,60.0,73.0,102.0,,,2.0,0.0,2.0,2.0,0.0,1.0,1.0,0.0,2.0,2.0,4.33036,30.1865,1330.97,1996.45,30.2124,84.0285,16.6877,13.4988,67.9715,2.0,32.9141,20.902,79.6982,35.3804,63.1265,56.0,1.0,0.0,0.0,1.0,4.11
+0038ba98,10,0,,,3.0,19.66076033,123.0,83.0,163.0,,,3.0,1.0,,,0.0,1.0,1.0,1.0,3.0,2.0,3.78271,19.6629,1135.86,1817.38,16.3275,63.247,14.7,4.96291,21.353,2.0,30.8936,16.0259,59.4643,26.1957,47.2211,40.0,3.0,3.0,0.0,4.0,3.67
+0068a485,10,1,,,3.0,16.86128647,71.0,90.0,116.0,,,3.0,0.0,2.0,1.0,0.0,0.0,0.0,0.0,3.0,3.0,4.05726,16.8631,1180.04,1888.06,21.94,67.9527,13.6092,3.25395,16.2474,2.0,28.5367,17.476,63.8954,28.768,50.4767,,3.0,2.0,0.0,3.0,1.27
+0069fbed,15,0,,,1.0,,,,,,,1.0,,,,,,,,,,,,,,,,,,,,,,,,,,2.0,2.0,0.0,,
+0083e397,19,1,2.0,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,0.0,,
+0087dd65,11,1,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,1.0,,0.0,,
+00abe655,11,0,2.0,66.0,,,,,,,,4.0,,,,,,,,,,,,,,,,,,,,,,,,,59.0,3.0,0.0,0.0,4.0,1.1
+00ae59c9,13,0,,,4.0,21.07906523,63.0,79.0,150.0,,,4.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,4.0,5.0,5.08025,21.0814,1239.46,2974.71,19.9545,74.2823,15.6597,5.42165,25.7177,2.0,36.0572,18.2705,69.202,36.2232,56.0118,47.0,3.0,1.0,0.0,3.0,3.02
+00af6387,12,0,,,1.0,15.54411111,57.0,71.0,103.0,,,1.0,0.0,2.0,2.0,0.0,0.0,1.0,1.0,,,,,,,,,,,,,,,,,,,1.0,,0.0,1.0,1.22
+00bd4359,12,0,,,,,,,,,,1.0,,,,,,,,,,,,,,,,,,,,,,,,,,1.0,2.0,0.0,,
+00c0cd71,7,0,2.0,51.0,1.0,29.31577503,80.0,75.0,99.0,1.0,4.0,1.0,1.0,,,0.0,1.0,1.0,1.0,,,,,,,,,,,,,,,,,,50.0,4.0,2.0,332.0,,
+00d56d4b,5,1,2.0,80.0,1.0,17.28450413,61.0,76.0,109.0,1.0,,1.0,0.0,,,0.0,1.0,1.0,1.0,,,,,,,,,,,,,,,,,,53.0,1.0,0.0,0.0,,
+00d9913d,10,1,,,3.0,19.89315702,,81.0,,,,3.0,0.0,,,0.0,0.0,0.0,1.0,,,,,,,,,,,,,,,,,,,3.0,1.0,0.0,,
+00e6167c,6,0,1.0,60.0,4.0,30.09464889,61.0,91.0,95.0,,,4.0,1.0,,,0.0,0.0,0.0,1.0,4.0,2.0,2.75035,17.2738,1003.07,1504.61,15.1456,49.1034,14.0898,3.18407,11.0966,1.0,23.6182,10.3396,46.3531,19.8886,38.7638,55.0,4.0,3.0,0.0,,
+00ebc35d,10,0,,,,,,,,,,1.0,,,,,,,,,,,,,,,,,,,,,,,,,,4.0,2.0,0.0,,
diff --git a/DM_Gruppenarbeit-main/datenvorverarbeitung/CsvDateien/Train/cleaned_train.csv b/DM_Gruppenarbeit-main/datenvorverarbeitung/CsvDateien/Train/cleaned_train.csv
new file mode 100644
index 0000000000000000000000000000000000000000..5fd120448a4002267e7146524c30cca1dc7666ac
--- /dev/null
+++ b/DM_Gruppenarbeit-main/datenvorverarbeitung/CsvDateien/Train/cleaned_train.csv
@@ -0,0 +1,1551 @@
+id,Basic_Demos-Age,Basic_Demos-Sex,CGAS-Season,CGAS-CGAS_Score,Physical-Season,Physical-BMI,Physical-Diastolic_BP,Physical-HeartRate,Physical-Systolic_BP,Fitness_Endurance-Season,Fitness_Endurance-Max_Stage,FGC-Season,FGC-FGC_CU_Zone,FGC-FGC_GSND_Zone,FGC-FGC_GSD_Zone,FGC-FGC_PU_Zone,FGC-FGC_SRL_Zone,FGC-FGC_SRR_Zone,FGC-FGC_TL_Zone,BIA-Season,BIA-BIA_Activity_Level_num,BIA-BIA_BMC,BIA-BIA_BMI,BIA-BIA_BMR,BIA-BIA_DEE,BIA-BIA_ECW,BIA-BIA_FFM,BIA-BIA_FFMI,BIA-BIA_FMI,BIA-BIA_Fat,BIA-BIA_Frame_num,BIA-BIA_ICW,BIA-BIA_LDM,BIA-BIA_LST,BIA-BIA_SMM,BIA-BIA_TBW,SDS-SDS_Total_T,PreInt_EduHx-Season,PreInt_EduHx-computerinternet_hoursday,sii,Fitness_Endurance-Total_Time_Seconds,PAQ-Season,PAQ-PAQ_Total
+00008ff9,5,0,4.0,51.0,3.0,16.87731569,,,,,,3,0.0,,,0.0,0.0,0.0,1.0,3,2.0,2.66855,16.8792,932.498,1492.0,8.25598,41.5862,13.8177,3.06143,9.21377,1.0,24.4349,8.89536,38.9177,19.5413,32.6909,,3.0,3.0,2.0,0.0,,
+000fd460,9,0,,,3.0,14.03559028,75.0,70.0,122.0,,,3,0.0,,,0.0,1.0,1.0,0.0,4,2.0,2.57949,14.0371,936.656,1498.65,6.01993,42.0291,12.8254,1.21172,3.97085,1.0,21.0352,14.974,39.4497,15.4107,27.0552,64.0,2.0,0.0,0.0,0.0,3.0,2.34
+00115b9f,9,0,3.0,71.0,2.0,18.29234694,60.0,97.0,117.0,2.0,6.0,2,1.0,,,0.0,0.0,0.0,1.0,2,3.0,3.84191,18.2943,1131.43,1923.44,15.5925,62.7757,14.074,4.22033,18.8243,2.0,30.4041,16.779,58.9338,26.4798,45.9966,45.0,4.0,0.0,1.0,577.0,4.0,2.451
+001f3379,13,1,4.0,50.0,2.0,22.27995198,60.0,73.0,102.0,,,2,0.0,2.0,2.0,0.0,1.0,1.0,0.0,2,2.0,4.33036,30.1865,1330.97,1996.45,30.2124,84.0285,16.6877,13.4988,67.9715,2.0,32.9141,20.902,79.6982,35.3804,63.1265,56.0,1.0,0.0,1.0,0.0,1.0,4.11
+0038ba98,10,0,,,3.0,19.66076033,123.0,83.0,163.0,,,3,1.0,,,0.0,1.0,1.0,1.0,3,2.0,3.78271,19.6629,1135.86,1817.38,16.3275,63.247,14.7,4.96291,21.353,2.0,30.8936,16.0259,59.4643,26.1957,47.2211,40.0,3.0,3.0,0.0,0.0,4.0,3.67
+00ae59c9,13,0,,,4.0,21.07906523,63.0,79.0,150.0,,,4,1.0,1.0,1.0,1.0,1.0,1.0,1.0,4,5.0,5.08025,21.0814,1239.46,2974.71,19.9545,74.2823,15.6597,5.42165,25.7177,2.0,36.0572,18.2705,69.202,36.2232,56.0118,47.0,3.0,1.0,1.0,0.0,3.0,3.02
+00e6167c,6,0,1.0,60.0,4.0,30.09464889,61.0,91.0,95.0,,,4,1.0,,,0.0,0.0,0.0,1.0,4,2.0,2.75035,17.2738,1003.07,1504.61,15.1456,49.1034,14.0898,3.18407,11.0966,1.0,23.6182,10.3396,46.3531,19.8886,38.7638,55.0,4.0,3.0,0.0,0.0,,
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diff --git a/DM_Gruppenarbeit-main/datenvorverarbeitung/DT/CompressTimesteps.py b/DM_Gruppenarbeit-main/datenvorverarbeitung/DT/CompressTimesteps.py
new file mode 100644
index 0000000000000000000000000000000000000000..38c202dacac43447f946b3a0d9e53447d55b0f87
--- /dev/null
+++ b/DM_Gruppenarbeit-main/datenvorverarbeitung/DT/CompressTimesteps.py
@@ -0,0 +1,67 @@
+import pandas as pd
+import os
+
+
+# Funktion zum Hinzufügen einer neuen Spalte (falls benötigt, hier aber nicht verwendet)
+def append_new_column(dataframe):
+ new_df = pd.DataFrame(columns=dataframe.columns)
+ return new_df
+
+
+# Pfad zu den Daten
+path_to_data = f'CsvDateien/NutzerIdentifikationCsv/TrainDaten/'
+
+# Liste der CSV-Dateien
+csv_files = [f for f in os.listdir(path_to_data) if f.endswith('.csv')]
+
+def compressCsv():
+
+ # Zeitintervalle in Minuten
+ time_minutes = [10, 30]
+
+ # Schleife für jeden Zeitstempel
+ for timestamp in time_minutes:
+
+ # Anzahl der Schritte für das Zeitintervall
+ time_steps = timestamp * 12
+
+ # Liste zum Speichern der aggregierten Daten aus allen CSV-Dateien
+ combined_data = []
+
+ # Numerische und andere Spalten identifizieren
+ numeric_cols = ['enmo', 'non-wear_flag', 'light']
+ first_cols = ['step', 'time_of_day', 'weekday', 'quarter', 'id']
+
+ # Schleife für jede CSV-Datei
+ for csv_f in csv_files:
+ print(f"Verarbeite Datei: {csv_f}")
+
+ # Datei lesen
+ df = pd.read_csv(os.path.join(path_to_data, csv_f))
+
+ # Daten in Gruppen von time_steps zusammenfassen
+ grouped_data = []
+ for i in range(0, len(df), time_steps):
+ group = df.iloc[i:i + time_steps]
+ # Durchschnitt für numerische Spalten
+ mean_values = group[numeric_cols].mean().to_list()
+ # Erster Wert für die restlichen Spalten
+ first_values = group[first_cols].iloc[0].to_list()
+ # Kombiniertes Ergebnis
+ grouped_data.append(mean_values + first_values)
+
+ # Daten aus der aktuellen Datei zur kombinierten Liste hinzufügen
+ combined_data.extend(grouped_data)
+
+ # Spaltennamen der neuen DataFrame
+ columns = numeric_cols + first_cols
+
+ # Kombinierte DataFrame erstellen
+ final_df = pd.DataFrame(combined_data, columns=columns)
+
+ # Neue CSV-Datei speichern
+ output_path = f"{timestamp}MinSeries.csv"
+ final_df.to_csv(output_path, index=False)
+ print(f"Datei gespeichert: {output_path}")
+
+compressCsv()
diff --git a/DM_Gruppenarbeit-main/datenvorverarbeitung/DT/DT_Training.py b/DM_Gruppenarbeit-main/datenvorverarbeitung/DT/DT_Training.py
new file mode 100644
index 0000000000000000000000000000000000000000..d1a177a7ac8a0fb262398945cc7f4d20112df841
--- /dev/null
+++ b/DM_Gruppenarbeit-main/datenvorverarbeitung/DT/DT_Training.py
@@ -0,0 +1,58 @@
+import pandas as pd
+from sklearn.model_selection import train_test_split
+from sklearn.tree import DecisionTreeClassifier
+from sklearn.metrics import classification_report, accuracy_score
+
+# Daten laden
+file_path = 'DT_Merged_Data.csv'
+data = pd.read_csv(file_path)
+
+# Zielvariable und Features auswählen
+features = data.drop(columns=['id', 'sii']) # 'id' und 'sii' entfernen
+target = data['sii']
+
+# Fehlende Werte behandeln: NaN-Werte durch den Mittelwert (für Features) und Modus (für Zielvariable) ersetzen
+features.fillna(features.mean(), inplace=True)
+target.fillna(target.mode()[0], inplace=True)
+
+# Daten in Trainings- und Testdatensatz aufteilen
+X_train, X_test, y_train, y_test = train_test_split(features, target, test_size=0.2, random_state=42)
+
+# Decision-Tree-Klassifikator trainieren
+model = DecisionTreeClassifier(random_state=42)
+model.fit(X_train, y_train)
+
+# Vorhersagen machen
+y_pred = model.predict(X_test)
+
+# Modellbewertung
+accuracy = accuracy_score(y_test, y_pred)
+report = classification_report(y_test, y_pred)
+
+# Ergebnisse ausgeben
+print(f"Accuracy: {accuracy}")
+print("Classification Report:")
+print(report)
+
+# Neue Datei laden und `sii` vorhersagen
+new_file_path = 'DT_Merged_Data_Test.csv' # Pfad zur neuen Datei
+new_data = pd.read_csv(new_file_path)
+
+# Features aus der neuen Datei extrahieren und fehlende Werte behandeln
+new_features = new_data.drop(columns=['id'], errors='ignore') # Falls `sii` fehlt, ignorieren
+new_features.fillna(features.mean(), inplace=True)
+
+# Vorhersagen für die neue Datei machen
+new_predictions = model.predict(new_features)
+
+
+# Ergebnisse vorbereiten: Nur `id` und die vorhergesagten `sii`-Werte
+result_data = new_data[['id']].copy()
+result_data['sii'] = new_predictions
+
+# Datei speichern
+output_path = 'Data_with_SII_Predictions_DT.csv'
+result_data.to_csv(output_path, index=False)
+
+
+print(f"Die vorhergesagten Werte wurden in der Datei gespeichert: {output_path}")
\ No newline at end of file
diff --git a/DM_Gruppenarbeit-main/datenvorverarbeitung/DT/MergeData.py b/DM_Gruppenarbeit-main/datenvorverarbeitung/DT/MergeData.py
new file mode 100644
index 0000000000000000000000000000000000000000..505796bb9122e5684c0e5e1faee8d2f8b87caf65
--- /dev/null
+++ b/DM_Gruppenarbeit-main/datenvorverarbeitung/DT/MergeData.py
@@ -0,0 +1,49 @@
+# import os
+# import pandas as pd
+
+# series = pd.read_csv('DT_Summary_Series.csv')
+# train_df = pd.read_csv('cleaned_train.csv')
+
+# def merge_data_with_10min_timestamps(train_dataframe, parquet_train_csv):
+# """
+# Merges all CSV files in SeriesTrain folder with the given train_dataframe on the "id" column.
+# """
+# # Initialize an empty DataFrame to store the merged result
+# merged_df = parquet_train_csv.copy()
+
+# # Merge with the train dataframe
+# if "id" in merged_df.columns and "id" in train_dataframe.columns:
+# merged_df = pd.merge(merged_df, train_dataframe, on="id", how="outer")
+# else:
+# print(f"Skipping file {file} due to missing 'id' column.")
+
+
+# return merged_df
+# # Call the function
+
+# parquett_csv_10min = pd.read_csv(f'CsvDateien/SeriesTrain/10MinSeries.csv')
+# parquett_csv_30min = pd.read_csv(f'CsvDateien/SeriesTrain/30MinSeries.csv')
+
+# merged_dataframe_10min = merge_data_with_10min_timestamps(train_df, parquett_csv_10min)
+# merged_dataframe_30min = merge_data_with_10min_timestamps(train_df, parquett_csv_30min)
+
+
+import pandas as pd
+
+# Dateien laden
+file1_path = '/mnt/data/cleaned_train.csv'
+file2_path = '/mnt/data/DT_Summary_Series.csv'
+
+# Dateien als DataFrames laden
+df1 = pd.read_csv(file1_path)
+df2 = pd.read_csv(file2_path)
+
+# Gemeinsame Spalte für Merge bestimmen (hier 'ID' als Beispiel)
+# Ändern Sie 'ID' in den tatsächlichen Spaltennamen, der beiden Dateien gemeinsam ist
+merged_df = pd.merge(df1, df2, on='ID', how='inner') # Optionen für 'how': 'inner', 'outer', 'left', 'right'
+
+# Resultat speichern
+merged_file_path = '/mnt/data/merged_file.csv'
+merged_df.to_csv(merged_file_path, index=False)
+
+print(f"Dateien wurden erfolgreich gemerged und gespeichert unter: {merged_file_path}")
diff --git a/DM_Gruppenarbeit-main/datenvorverarbeitung/DT/MergeDataTest.py b/DM_Gruppenarbeit-main/datenvorverarbeitung/DT/MergeDataTest.py
new file mode 100644
index 0000000000000000000000000000000000000000..8d2a23970bd86a8d96fa48de3976fa13d98d1951
--- /dev/null
+++ b/DM_Gruppenarbeit-main/datenvorverarbeitung/DT/MergeDataTest.py
@@ -0,0 +1,25 @@
+import pandas as pd
+
+# Dateien laden
+main = 'cleaned_test.csv'
+secondary = 'DT_Summary_SeriesTest.csv'
+
+# Dateien als DataFrames laden
+df1 = pd.read_csv(main)
+df2 = pd.read_csv(secondary)
+
+# Gemeinsame Spalte für Merge bestimmen (hier 'ID' als Beispiel)
+# Ändern Sie 'ID' in den tatsächlichen Spaltennamen, der beiden Dateien gemeinsam ist
+merged_df = pd.merge(df1, df2, on='id', how='outer') # Optionen für 'how': 'inner', 'outer', 'left', 'right'
+
+# Spalte 'series' hinzufügen: 1, wenn die ID in Datei 2 ist, sonst 0
+merged_df['series'] = merged_df['id'].isin(df2['id']).astype(int)
+
+# Leere Felder auffüllen (optional, falls fehlende Werte entstehen)
+merged_df.fillna('', inplace=True)
+
+# Resultat speichern
+merged_file_path = 'DT_Merged_Data_Test.csv'
+merged_df.to_csv(merged_file_path, index=False)
+
+print(f"Dateien wurden erfolgreich gemerged und gespeichert unter: {merged_file_path}")
diff --git a/DM_Gruppenarbeit-main/datenvorverarbeitung/DT/MergeDataTrain.py b/DM_Gruppenarbeit-main/datenvorverarbeitung/DT/MergeDataTrain.py
new file mode 100644
index 0000000000000000000000000000000000000000..ca9272aa79861a01dbeb01f2a0d0e95285214b08
--- /dev/null
+++ b/DM_Gruppenarbeit-main/datenvorverarbeitung/DT/MergeDataTrain.py
@@ -0,0 +1,25 @@
+import pandas as pd
+
+# Dateien laden
+main = 'cleaned_train.csv'
+secondary = 'DT_Summary_Series.csv'
+
+# Dateien als DataFrames laden
+df1 = pd.read_csv(main)
+df2 = pd.read_csv(secondary)
+
+# Gemeinsame Spalte für Merge bestimmen (hier 'ID' als Beispiel)
+# Ändern Sie 'ID' in den tatsächlichen Spaltennamen, der beiden Dateien gemeinsam ist
+merged_df = pd.merge(df1, df2, on='id', how='outer') # Optionen für 'how': 'inner', 'outer', 'left', 'right'
+
+# Spalte 'series' hinzufügen: 1, wenn die ID in Datei 2 ist, sonst 0
+merged_df['series'] = merged_df['id'].isin(df2['id']).astype(int)
+
+# Leere Felder auffüllen (optional, falls fehlende Werte entstehen)
+merged_df.fillna('', inplace=True)
+
+# Resultat speichern
+merged_file_path = 'DT_Merged_Data.csv'
+merged_df.to_csv(merged_file_path, index=False)
+
+print(f"Dateien wurden erfolgreich gemerged und gespeichert unter: {merged_file_path}")
diff --git a/DM_Gruppenarbeit-main/datenvorverarbeitung/DT/SeriesForDT.py b/DM_Gruppenarbeit-main/datenvorverarbeitung/DT/SeriesForDT.py
new file mode 100644
index 0000000000000000000000000000000000000000..f5d2dab4413bb366dfc8fc99732037989386f47a
--- /dev/null
+++ b/DM_Gruppenarbeit-main/datenvorverarbeitung/DT/SeriesForDT.py
@@ -0,0 +1,21 @@
+import pandas as pd
+
+# Datei laden
+file_path = 'CsvDateien/SeriesTrain/' # Ersetzen Sie dies durch den tatsächlichen Dateipfad
+data = pd.read_csv('CsvDateien/SeriesTrain/10MinSeries.csv')
+
+# Durchschnitt und Standardabweichung für berechnen
+summary_stats = data.groupby('id').agg(
+ mean_enmo=('enmo', 'mean'),
+ std_enmo=('enmo', 'std'),
+ mean_light=('light', 'mean')
+).reset_index()
+
+# Spalten umbenennen
+summary_stats.rename(columns={'mean': 'mean_enmo', 'std': 'std_enmo'}, inplace=True)
+
+# Ergebnisse in eine neue CSV-Datei speichern
+output_path = 'DT_Summary_Series.csv' # Pfad, wo die Datei gespeichert werden soll
+summary_stats.to_csv(output_path, index=False)
+
+print(f"Die zusammengefassten Statistiken wurden in {output_path} gespeichert.")
diff --git a/DM_Gruppenarbeit-main/datenvorverarbeitung/DT/SeriesForDT_Test.py b/DM_Gruppenarbeit-main/datenvorverarbeitung/DT/SeriesForDT_Test.py
new file mode 100644
index 0000000000000000000000000000000000000000..7d3f23c945d306b9690fd410731e0fba5f2e8ecd
--- /dev/null
+++ b/DM_Gruppenarbeit-main/datenvorverarbeitung/DT/SeriesForDT_Test.py
@@ -0,0 +1,21 @@
+import pandas as pd
+
+# Datei laden
+file_path = 'CsvDateien/SeriesTest/' # Ersetzen Sie dies durch den tatsächlichen Dateipfad
+data = pd.read_csv('CsvDateien/SeriesTest/10MinSeries.csv')
+
+# Durchschnitt und Standardabweichung für berechnen
+summary_stats = data.groupby('id').agg(
+ mean_enmo=('enmo', 'mean'),
+ std_enmo=('enmo', 'std'),
+ mean_light=('light', 'mean')
+).reset_index()
+
+# Spalten umbenennen
+summary_stats.rename(columns={'mean': 'mean_enmo', 'std': 'std_enmo'}, inplace=True)
+
+# Ergebnisse in eine neue CSV-Datei speichern
+output_path = 'DT_Summary_SeriesTest.csv' # Pfad, wo die Datei gespeichert werden soll
+summary_stats.to_csv(output_path, index=False)
+
+print(f"Die zusammengefassten Statistiken wurden in {output_path} gespeichert.")
diff --git a/DM_Gruppenarbeit-main/datenvorverarbeitung/LSTM/LSTM_Training.py b/DM_Gruppenarbeit-main/datenvorverarbeitung/LSTM/LSTM_Training.py
new file mode 100644
index 0000000000000000000000000000000000000000..a31bcfe820cf5429ef26c0709ffb874df2788ed8
--- /dev/null
+++ b/DM_Gruppenarbeit-main/datenvorverarbeitung/LSTM/LSTM_Training.py
@@ -0,0 +1,152 @@
+import pandas as pd
+import numpy as np
+from sklearn.model_selection import train_test_split
+from sklearn.preprocessing import StandardScaler
+from tensorflow.keras.models import Sequential
+from tensorflow.keras.layers import LSTM, Dense, Dropout
+from tensorflow.keras.optimizers import Adam
+
+# Daten laden
+series_data = pd.read_csv('CsvDateien/SeriesTrain/30MinSeries.csv')
+static_data = pd.read_csv('cleaned_train.csv')
+test_series_data = pd.read_csv('CsvDateien/SeriesTest/30MinSeries.csv')
+test_static_data = pd.read_csv('cleaned_test.csv')
+
+# Debug: Spaltennamen ausgeben
+print("Train Series Data Columns:", series_data.columns)
+print("Train Static Data Columns:", static_data.columns)
+print("Test Series Data Columns:", test_series_data.columns)
+print("Test Static Data Columns:", test_static_data.columns)
+
+# Daten verknüpfen Train (alle IDs behalten)
+merged_data = series_data.merge(static_data, on='id', how='outer')
+
+# Fehlende Werte auffüllen (Platzhalter für fehlende Daten)
+merged_data['enmo'] = merged_data['enmo'].fillna(0)
+if 'sii' not in merged_data.columns:
+ merged_data['sii'] = 0
+else:
+ merged_data['sii'] = merged_data['sii'].fillna(0)
+merged_data['step'] = merged_data['step'].fillna(-1) # Negative Werte als Platzhalter für fehlende Schritte
+
+# Daten verknüpfen Test (alle IDs behalten)
+merged_data_test = test_series_data.merge(test_static_data, on='id', how='outer')
+merged_data_test['enmo'] = merged_data_test['enmo'].fillna(0)
+if 'sii' not in merged_data_test.columns:
+ merged_data_test['sii'] = 0
+else:
+ merged_data_test['sii'] = merged_data_test['sii'].fillna(0)
+merged_data_test['step'] = merged_data_test['step'].fillna(-1)
+
+# Zeitreihen in Fenster umwandeln
+def create_time_series(data, window_size, step_col, value_col):
+ if step_col not in data.columns or value_col not in data.columns:
+ raise KeyError(f"Columns '{step_col}' or '{value_col}' not found in data.")
+
+ time_series = []
+ labels = []
+ ids = data['id'].unique()
+
+ for person_id in ids:
+ person_data = data[data['id'] == person_id].sort_values(by=step_col)
+ values = person_data[value_col].values
+ target = person_data['sii'].iloc[0] if 'sii' in person_data else 0 # Assuming `sii` is static for each ID
+
+ if len(values) < window_size:
+ # Auffüllen von zu kurzen Zeitreihen mit Nullen
+ values = np.pad(values, (0, window_size - len(values)), 'constant', constant_values=0)
+
+ for i in range(len(values) - window_size + 1):
+ time_series.append(values[i:i + window_size])
+ labels.append(target)
+
+ # Für IDs ohne genügend Daten ein Fenster aus Nullen hinzufügen
+ if len(values) < window_size:
+ time_series.append(np.zeros(window_size))
+ labels.append(target)
+
+ return np.array(time_series), np.array(labels)
+
+# Parameter
+WINDOW_SIZE = 10
+
+# Features erstellen
+X, y = create_time_series(merged_data, WINDOW_SIZE, 'step', 'enmo')
+
+# Statische Merkmale hinzufügen
+static_features = static_data.drop(columns=['id', 'sii'], errors='ignore')
+scaler_static = StandardScaler()
+scaled_static = scaler_static.fit_transform(static_features)
+
+static_feature_dict = dict(zip(static_data['id'], scaled_static))
+
+# IDs in Zeitreihen-Daten und statischen Daten abgleichen
+all_ids = np.union1d(merged_data['id'].unique(), static_data['id'].unique())
+filtered_static_array = np.array([static_feature_dict.get(id, np.zeros(scaled_static.shape[1])) for id in all_ids])
+
+# Statische Merkmale zu Zeitfenstern hinzufügen
+X_static = np.repeat(filtered_static_array[:, np.newaxis, :], WINDOW_SIZE, axis=1)
+
+# Synchronisieren der Dimensionen von Zeitreihen- und statischen Daten
+if X.shape[0] != X_static.shape[0]:
+ # IDs, die nur in static_data existieren, hinzufügen
+ missing_ids = set(static_data['id'].unique()) - set(merged_data['id'].unique())
+ for _ in missing_ids:
+ X = np.vstack([X, np.zeros((WINDOW_SIZE, 1))])
+ y = np.append(y, 0)
+
+if X.shape[0] != X_static.shape[0]:
+ raise ValueError("Mismatch in number of samples between time series data and static features")
+
+X_combined = np.concatenate([X[:, :, np.newaxis], X_static], axis=2)
+
+# Testdaten vorbereiten
+X_test_data, y_test_data = create_time_series(merged_data_test, WINDOW_SIZE, 'step', 'enmo')
+
+# Test-Statische Merkmale
+test_static_features = test_static_data.drop(columns=['id', 'sii'], errors='ignore')
+test_scaled_static = scaler_static.transform(test_static_features)
+
+test_static_feature_dict = dict(zip(test_static_data['id'], test_scaled_static))
+
+# IDs in Test-Zeitreihen-Daten und Test-statischen Daten abgleichen
+all_test_ids = np.union1d(merged_data_test['id'].unique(), test_static_data['id'].unique())
+filtered_test_static_array = np.array([test_static_feature_dict.get(id, np.zeros(test_scaled_static.shape[1])) for id in all_test_ids])
+
+X_test_static = np.repeat(filtered_test_static_array[:, np.newaxis, :], WINDOW_SIZE, axis=1)
+
+# Synchronisieren der Dimensionen von Testzeitreihen- und statischen Daten
+if X_test_data.shape[0] != X_test_static.shape[0]:
+ # IDs, die nur in test_static_data existieren, hinzufügen
+ missing_test_ids = set(test_static_data['id'].unique()) - set(merged_data_test['id'].unique())
+ for _ in missing_test_ids:
+ X_test_data = np.vstack([X_test_data, np.zeros((WINDOW_SIZE, 1))])
+ y_test_data = np.append(y_test_data, 0)
+
+if X_test_data.shape[0] != X_test_static.shape[0]:
+ raise ValueError("Mismatch in number of samples between test time series data and static features")
+
+X_test_combined = np.concatenate([X_test_data[:, :, np.newaxis], X_test_static], axis=2)
+
+# Daten aufteilen
+X_train, X_val, y_train, y_val = train_test_split(X_combined, y, test_size=0.2, random_state=42)
+
+# LSTM-Modell erstellen
+model = Sequential([
+ LSTM(64, activation='relu', input_shape=(WINDOW_SIZE, X_combined.shape[2]), return_sequences=True),
+ Dropout(0.2),
+ LSTM(32, activation='relu', return_sequences=False),
+ Dropout(0.2),
+ Dense(16, activation='relu'),
+ Dense(1) # Regression output for sii
+])
+
+# Kompilieren
+model.compile(optimizer=Adam(learning_rate=0.001), loss='mse', metrics=['mae'])
+
+# Training
+history = model.fit(X_train, y_train, epochs=20, batch_size=32, validation_data=(X_val, y_val), verbose=1)
+
+# Evaluation auf Testdaten
+loss, mae = model.evaluate(X_test_combined, y_test_data)
+print(f"Test Loss: {loss}, Test MAE: {mae}")
diff --git a/DM_Gruppenarbeit-main/datenvorverarbeitung/LSTM/LSTN2.py b/DM_Gruppenarbeit-main/datenvorverarbeitung/LSTM/LSTN2.py
new file mode 100644
index 0000000000000000000000000000000000000000..6edf88605a954f5cde217810c50edc5d0e7668bd
--- /dev/null
+++ b/DM_Gruppenarbeit-main/datenvorverarbeitung/LSTM/LSTN2.py
@@ -0,0 +1,74 @@
+import numpy as np
+import pandas as pd
+from sklearn.preprocessing import MinMaxScaler, LabelEncoder
+from tensorflow.keras.models import Sequential
+from tensorflow.keras.layers import LSTM, Dense
+from sklearn.model_selection import train_test_split
+
+# Load the dataset
+data = pd.read_csv('/mnt/data/test.csv')
+
+# Relevant columns for time series and target
+time_series_cols = ['enmo', 'non-wear_flag', 'light', 'step', 'time_of_day', 'weekday', 'quarter']
+target_col = 'sii'
+
+# Drop rows with missing values in relevant columns
+data = data.dropna(subset=time_series_cols + [target_col])
+
+# Convert time_of_day to seconds (if necessary)
+data['time_of_day'] = pd.to_timedelta(data['time_of_day']).dt.total_seconds()
+
+# Normalize the time series data
+scaler = MinMaxScaler()
+data[time_series_cols] = scaler.fit_transform(data[time_series_cols])
+
+# Encode the target column (if necessary)
+label_encoder = LabelEncoder()
+data[target_col] = label_encoder.fit_transform(data[target_col]) # Encode classes as integers
+
+# Group data by 'id' for time series processing
+grouped = data.groupby('id')
+sequences = []
+targets = []
+
+# Create sequences and targets for each id
+sequence_length = 10
+for _, group in grouped:
+ group = group.sort_values('step') # Sort by time or step
+ values = group[time_series_cols].values
+ target_values = group[target_col].values
+
+ for i in range(len(group) - sequence_length):
+ seq = values[i:i + sequence_length]
+ target = target_values[i + sequence_length]
+ sequences.append(seq)
+ targets.append(target)
+
+# Convert to numpy arrays
+X = np.array(sequences)
+y = np.array(targets)
+
+# Train-test split
+X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)
+
+# Define the LSTM model
+num_classes = len(np.unique(y)) # Number of unique classes in `sii`
+model = Sequential([
+ LSTM(50, activation='relu', input_shape=(sequence_length, len(time_series_cols))),
+ Dense(num_classes, activation='softmax') # Output layer for classification
+])
+
+model.compile(optimizer='adam',
+ loss='sparse_categorical_crossentropy', # Loss for integer labels
+ metrics=['accuracy'])
+
+# Train the model
+model.fit(X_train, y_train, epochs=20, batch_size=32, validation_data=(X_test, y_test))
+
+# Evaluate the model
+loss, accuracy = model.evaluate(X_test, y_test)
+print(f"Test Loss: {loss}")
+print(f"Test Accuracy: {accuracy}")
+
+# Save the model
+model.save('lstm_sii_classification_model.h5')
diff --git a/DM_Gruppenarbeit-main/datenvorverarbeitung/LSTM/hyperparametertuning.py b/DM_Gruppenarbeit-main/datenvorverarbeitung/LSTM/hyperparametertuning.py
new file mode 100644
index 0000000000000000000000000000000000000000..939106d05c0176dacc6320903835513b66cf83e8
--- /dev/null
+++ b/DM_Gruppenarbeit-main/datenvorverarbeitung/LSTM/hyperparametertuning.py
@@ -0,0 +1,102 @@
+import numpy as np
+import pandas as pd
+from sklearn.preprocessing import MinMaxScaler, LabelEncoder
+from tensorflow.keras.models import Sequential
+from tensorflow.keras.layers import LSTM, Dense
+from tensorflow.keras.optimizers import Adam
+from sklearn.model_selection import train_test_split
+import keras_tuner as kt
+
+# Load the dataset
+data = pd.read_csv('30Min_Train_LSTM_Merged_Data.csv')
+
+# Relevant columns for time series and target
+time_series_cols = ['enmo', 'non-wear_flag', 'light', 'step', 'time_of_day', 'weekday', 'quarter']
+target_col = 'sii'
+
+# Drop rows with missing values in relevant columns
+data = data.dropna(subset=time_series_cols + [target_col])
+
+# Convert time_of_day to seconds (if necessary)
+data['time_of_day'] = pd.to_timedelta(data['time_of_day']).dt.total_seconds()
+
+# Normalize the time series data
+scaler = MinMaxScaler()
+data[time_series_cols] = scaler.fit_transform(data[time_series_cols])
+
+# Encode the target column (if necessary)
+label_encoder = LabelEncoder()
+data[target_col] = label_encoder.fit_transform(data[target_col]) # Encode classes as integers
+
+# Group data by 'id' for time series processing
+grouped = data.groupby('id')
+sequences = []
+targets = []
+
+# Create sequences and targets for each id
+sequence_length = 10
+for _, group in grouped:
+ group = group.sort_values('step') # Sort by time or step
+ values = group[time_series_cols].values
+ target_values = group[target_col].values
+
+ for i in range(len(group) - sequence_length):
+ seq = values[i:i + sequence_length]
+ target = target_values[i + sequence_length]
+ sequences.append(seq)
+ targets.append(target)
+
+# Convert to numpy arrays
+X = np.array(sequences)
+y = np.array(targets)
+
+# Train-test split
+X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)
+
+# Define a model builder function for Keras Tuner
+def build_model(hp):
+ model = Sequential()
+ model.add(
+ LSTM(
+ hp.Int('units', min_value=32, max_value=128, step=16),
+ activation='relu',
+ input_shape=(sequence_length, len(time_series_cols))
+ )
+ )
+ model.add(Dense(len(np.unique(y)), activation='softmax')) # Output layer for classification
+ model.compile(
+ optimizer=Adam(hp.Choice('learning_rate', values=[1e-3, 5e-3, 1e-2])),
+ loss='sparse_categorical_crossentropy',
+ metrics=['accuracy']
+ )
+ return model
+
+# Initialize the tuner
+tuner = kt.RandomSearch(
+ build_model,
+ objective='val_accuracy',
+ max_trials=10, # Number of different hyperparameter combinations to try
+ executions_per_trial=1, # Number of models to train for each combination
+ directory='my_dir',
+ project_name='lstm_hyperparameter_tuning'
+)
+
+# Run the hyperparameter search
+tuner.search(X_train, y_train, epochs=10, validation_data=(X_test, y_test), batch_size=32)
+
+# Get the best hyperparameters
+best_hps = tuner.get_best_hyperparameters(num_trials=1)[0]
+print(f"Best number of units: {best_hps.get('units')}")
+print(f"Best learning rate: {best_hps.get('learning_rate')}")
+
+# Build the best model and train it
+best_model = tuner.hypermodel.build(best_hps)
+history = best_model.fit(X_train, y_train, epochs=20, validation_data=(X_test, y_test))
+
+# Evaluate the best model
+loss, accuracy = best_model.evaluate(X_test, y_test)
+print(f"Test Loss: {loss}")
+print(f"Test Accuracy: {accuracy}")
+
+# Save the best model
+best_model.save('lstm_best_hyperparameter_model.h5')
diff --git a/DM_Gruppenarbeit-main/datenvorverarbeitung/ParquettClean.py b/DM_Gruppenarbeit-main/datenvorverarbeitung/ParquettClean.py
new file mode 100644
index 0000000000000000000000000000000000000000..a9a9afbb2d2a4d159d860111a537ab729bfc9ff8
--- /dev/null
+++ b/DM_Gruppenarbeit-main/datenvorverarbeitung/ParquettClean.py
@@ -0,0 +1,51 @@
+import pandas as pd
+import numpy as np
+import os
+# from CsvBearbeiten import change_csv
+# from CompressTimesteps import compressCsv
+
+
+# assign parquett path to variable
+parquet_train = 'data/series_train.parquet/'
+
+#train_df := dataframe > train df ist von der parquet train datei
+
+# store parquett file to train_df
+train_df = pd.read_parquet(parquet_train)
+
+# bezeichnung der Spalte
+id_spalte = "id"
+
+# setzen der einzelnen ids zur variabe / liste??
+train_unique_ids = train_df[id_spalte].unique()
+
+print("Eindeutige IDs in der Datei:", train_unique_ids)
+
+'''
+# durchlaufe eine schleife für jede unique id
+for unique_id in test_unique_ids:
+ print(unique_id)
+ # kreiere noch
+ subset = test_df[test_df[id_spalte] == unique_id]
+ output_file = f"CsvDateien/ParquettCleanCsv/TestDaten/output{unique_id}.csv"
+ subset.to_csv(output_file, index=False)
+ print(f"CSV-Datei für ID {unique_id} erstellt: {output_file}")
+
+'''
+# durchlaufe eine schleife für jede unique id
+
+for unique_id in train_unique_ids:
+ print(unique_id)
+ # kreiere noch
+ subset = train_df[train_df[id_spalte] == unique_id]
+ output_file = f"CsvDateien/ParquettCleanCsv/train/output{unique_id}.csv"
+ subset.to_csv(output_file, index=False)
+ print(f"CSV-Datei für ID {unique_id} erstellt: {output_file}")
+
+
+
+
+# test_parquett_df.to_csv("test_parquett.csv", index = False)
+# change_csv()
+
+# compressCsv()
diff --git a/DM_Gruppenarbeit-main/datenvorverarbeitung/ParquettCleanTest.py b/DM_Gruppenarbeit-main/datenvorverarbeitung/ParquettCleanTest.py
new file mode 100644
index 0000000000000000000000000000000000000000..c9dbfabfd30e6ceeb47b9530d6f7c16dd8baffa0
--- /dev/null
+++ b/DM_Gruppenarbeit-main/datenvorverarbeitung/ParquettCleanTest.py
@@ -0,0 +1,49 @@
+import pandas as pd
+import numpy as np
+import os
+from CsvBearbeiten import change_csv
+from CompressTimesteps import compressCsv
+
+
+# assign parquett path to variable
+parquet_test = 'data/series_test.parquet/'
+
+
+#test_df := dataframe > test df ist von der parquet test datei
+#train_df := dataframe > train df ist von der parquet train datei
+
+# store parquett file to test_df and train_df
+
+train_df = pd.read_parquet(parquet_test)
+
+# bezeichnung der Spalte
+id_spalte = "id"
+
+# setzen der einzelnen ids zur variabe / liste??
+
+train_unique_ids = train_df[id_spalte].unique()
+
+print("Eindeutige IDs in der Datei:", train_unique_ids)
+
+'''
+# durchlaufe eine schleife für jede unique id
+for unique_id in test_unique_ids:
+ print(unique_id)
+ # kreiere noch
+ subset = test_df[test_df[id_spalte] == unique_id]
+ output_file = f"CsvDateien/ParquettCleanCsv/TestDaten/output{unique_id}.csv"
+ subset.to_csv(output_file, index=False)
+ print(f"CSV-Datei für ID {unique_id} erstellt: {output_file}")
+
+'''
+# # durchlaufe eine schleife für jede unique id
+# ticker = 0
+# for unique_id in train_unique_ids:
+# print(unique_id)
+# # kreiere noch
+# subset = train_df[train_df[id_spalte] == unique_id]
+# output_file = f"CsvDateien/ParquettCleanCsv/TestDaten/output{unique_id}.csv"
+# subset.to_csv(output_file, index=False)
+# print(f"CSV-Datei für ID {unique_id} erstellt: {output_file}")
+# ticker += 1
+
diff --git a/DM_Gruppenarbeit-main/datenvorverarbeitung/SpaltenBeschreibung.txt b/DM_Gruppenarbeit-main/datenvorverarbeitung/SpaltenBeschreibung.txt
new file mode 100644
index 0000000000000000000000000000000000000000..532064466512ef7e5b99d8f980a1c59a955b6117
--- /dev/null
+++ b/DM_Gruppenarbeit-main/datenvorverarbeitung/SpaltenBeschreibung.txt
@@ -0,0 +1,31 @@
+Description of the columns:
+ https://www.kaggle.com/competitions/child-mind-institute-problematic-internet-use/data
+
+ Actigraphy Files and Field Descriptions
+
+ During their participation in the HBN study, some participants were given an accelerometer to wear for up to 30 days continually while at home and going about their regular daily lives.
+
+ series_{train|test}.parquet/id={id} - Series to be used as training data, partitioned by id. Each series is a continuous recording of accelerometer data for a single subject spanning many days.
+ id - The patient identifier corresponding to the id field in train/test.csv.
+
+ step - An integer timestep for each observation within a series.
+
+ X, Y, Z - Measure of acceleration, in g, experienced by the wrist-worn watch along each standard axis.
+
+ enmo - As calculated and described by the wristpy package, ENMO is the Euclidean Norm Minus One of all accelerometer signals (along each of the x-, y-, and z-axis, measured in g-force) with negative values rounded to zero. Zero values are indicative of periods of no motion. While no standard measure of acceleration exists in this space, this is one of the several commonly computed features.
+
+ anglez - As calculated and described by the wristpy package, Angle-Z is a metric derived from individual accelerometer components and refers to the angle of the arm relative to the horizontal plane.
+
+ non-wear_flag - A flag (0: watch is being worn, 1: the watch is not worn) to help determine periods when the watch has been removed, based on the GGIR definition, which uses the standard deviation and range of the accelerometer data.
+
+ light - Measure of ambient light in lux. See here for details. (from outside and inside)
+
+ battery_voltage - A measure of the battery voltage in mV.
+
+ time_of_day - Time of day representing the start of a 5s window that the data has been sampled over, with format %H:%M:%S.%9f.
+
+ weekday - The day of the week, coded as an integer with 1 being Monday and 7 being Sunday.
+
+ quarter - The quarter of the year, an integer from 1 to 4.
+
+ relative_date_PCIAT - The number of days (integer) since the PCIAT test was administered (negative days indicate that the actigraphy data has been collected before the test was administered).
diff --git a/DM_Gruppenarbeit-main/datenvorverarbeitung/TestCsvClean.py b/DM_Gruppenarbeit-main/datenvorverarbeitung/TestCsvClean.py
new file mode 100644
index 0000000000000000000000000000000000000000..b6b5ffc317136c0a1c7fac1d2b8a4a7650c2ec03
--- /dev/null
+++ b/DM_Gruppenarbeit-main/datenvorverarbeitung/TestCsvClean.py
@@ -0,0 +1,244 @@
+import pandas as pd
+
+# Dateipfade
+input_file_path = 'test.csv' # Ändere diesen Pfad bei Bedarf
+output_file_path = 'cleaned_test.csv' # Bereinigte Datei
+
+# Konstanten
+LEAST_AMOUNT_COLUMNS = 20
+IMPORTANT_COLUMNS = [
+ "BIA-BIA_FFM", "BIA-BIA_DEE", "BIA-BIA_ECW",
+ "BIA-BIA_Fat", "BIA-BIA_Frame_num","BIA-BIA_ICW",
+ "BIA-BIA_LDM", "BIA-BIA_LST", "Basic_Demos-Age",
+ "Physical-BMI", "BIA-BIA_BMR", "BIA-BIA_BMI", "BIA-BIA_SMM",
+ "BIA-BIA_TBW"
+]
+
+# Funktion zum Bereinigen der IDs
+def clean_ids(df):
+ missing_count = df.isna().sum(axis=1)
+ rows_to_drop_threshold = missing_count >= LEAST_AMOUNT_COLUMNS
+ rows_to_drop_columns = df[IMPORTANT_COLUMNS].isna().all(axis=1)
+ rows_to_drop = rows_to_drop_columns | rows_to_drop_threshold
+ line_ids = df.loc[rows_to_drop, "id"]
+ df = df[~rows_to_drop]
+
+ return df, line_ids
+
+# Lade die CSV-Datei
+data = pd.read_csv(input_file_path)
+
+# Bereinige die Daten mit clean_ids
+# data, dropped_ids = clean_ids(data)
+
+# Liste der zu entfernenden Spalten
+columns_to_remove = [
+ "Basic_Demos-Enroll_Season",
+ "Physical-Height",
+ "Physical-Weight",
+ "Physical-Waist_Circumference",
+ "FGC-FGC_CU",
+ "FGC-FGC_GSND",
+ "FGC-FGC_GSD",
+ "FGC-FGC_PU",
+ "FGC-FGC_SRL",
+ "FGC-FGC_SRR",
+ "FGC-FGC_TL",
+ "PCIAT-PCIAT_01",
+ "PCIAT-PCIAT_02",
+ "PCIAT-PCIAT_03",
+ "PCIAT-PCIAT_04",
+ "PCIAT-PCIAT_05",
+ "PCIAT-PCIAT_06",
+ "PCIAT-PCIAT_07",
+ "PCIAT-PCIAT_08",
+ "PCIAT-PCIAT_09",
+ "PCIAT-PCIAT_10",
+ "PCIAT-PCIAT_11",
+ "PCIAT-PCIAT_12",
+ "PCIAT-PCIAT_13",
+ "PCIAT-PCIAT_14",
+ "PCIAT-PCIAT_15",
+ "PCIAT-PCIAT_16",
+ "PCIAT-PCIAT_17",
+ "PCIAT-PCIAT_18",
+ "PCIAT-PCIAT_19",
+ "PCIAT-PCIAT_20",
+ "SDS-Season",
+ "SDS-SDS_Total_Raw",
+ "PCIAT-PCIAT_Total",
+]
+data = data.drop(columns=columns_to_remove, errors='ignore')
+
+# Kombiniere die Spalten Fitness_Endurance-Time_Mins und Fitness_Endurance-Time_Sec
+if "Fitness_Endurance-Time_Mins" in data.columns and "Fitness_Endurance-Time_Sec" in data.columns:
+ data["Fitness_Endurance-Total_Time_Seconds"] = (
+ data["Fitness_Endurance-Time_Mins"].fillna(0) * 60 +
+ data["Fitness_Endurance-Time_Sec"].fillna(0)
+ )
+ data = data.drop(columns=["Fitness_Endurance-Time_Mins", "Fitness_Endurance-Time_Sec"], errors='ignore')
+
+# Kombiniere die PAQ_A- und PAQ_C-Spalten in PAQ-Spalten
+if "PAQ_A-Season" in data.columns and "PAQ_C-Season" in data.columns:
+ data["PAQ-Season"] = data["PAQ_A-Season"].combine_first(data["PAQ_C-Season"])
+ data = data.drop(columns=["PAQ_A-Season", "PAQ_C-Season"], errors='ignore')
+
+if "PAQ_A-PAQ_A_Total" in data.columns and "PAQ_C-PAQ_C_Total" in data.columns:
+ data["PAQ-PAQ_Total"] = data["PAQ_A-PAQ_A_Total"].combine_first(data["PAQ_C-PAQ_C_Total"])
+ data = data.drop(columns=["PAQ_A-PAQ_A_Total", "PAQ_C-PAQ_C_Total"], errors='ignore')
+
+# Definiere die Zuordnung von Seasons zu Zahlen
+season_mapping = {
+ "Spring": 1,
+ "Summer": 2,
+ "Fall": 3,
+ "Winter": 4
+}
+
+# Liste der Season-Spalten, die konvertiert werden sollen
+season_columns = [
+ "PAQ-Season",
+ "CGAS-Season",
+ "FGC-Season",
+ "Physical-Season",
+ "Fitness_Endurance-Season",
+ "BIA-Season",
+ "PCIAT-Season",
+ "PreInt_EduHx-Season"
+]
+
+# Wandle die Seasons in Zahlen um
+for column in season_columns:
+ if column in data.columns:
+ data[column] = data[column].map(season_mapping)
+
+# Speichere die bereinigte Datei
+data.to_csv(output_file_path, index=False)
+
+print(f"Die bereinigte Datei wurde gespeichert unter: {output_file_path}")
+
+
+# import pandas as pd
+
+# # Dateipfad der ursprünglichen Datei
+# input_file_path = 'test.csv' # Ändere diesen Pfad bei Bedarf
+# output_file_path = 'cleaned_test.csv' # Dateipfad für die bereinigte Datei
+
+# # Lade die CSV-Datei
+# data = pd.read_csv(input_file_path)
+
+# """
+# how to get the ids that are not viable and have not enough columns:
+# 1. the row should have not more than 20 empty columns
+# """
+
+# LEAST_AMOUNT_COLUMNS = 20
+# IMPORTANT_COLUMNS = [
+# "BIA-BIA_FFM", "BIA-BIA_DEE", "BIA-BIA_ECW",
+# "BIA-BIA_Fat", "BIA-BIA_Frame_num","BIA-BIA_ICW",
+# "BIA-BIA_LDM", "BIA-BIA_LST", "Basic_Demos-Age",
+# "Physical-BMI", "BIA-BIA_BMR", "BIA-BIA_BMI", "BIA-BIA_SMM",
+# "BIA-BIA_TBW"
+# ]
+
+# def clean_ids(df):
+
+# missing_count = df.isna().sum(axis = 1)
+# rows_to_drop_threshold = missing_count >= LEAST_AMOUNT_COLUMNS
+# rows_to_drop_columns = df[IMPORTANT_COLUMNS].isna().all(axis=1)
+# rows_to_drop = rows_to_drop_columns | rows_to_drop_threshold
+# line_ids = df.loc[rows_to_drop, "id"]
+# df = df[~rows_to_drop]
+
+# return df, line_ids
+
+
+# # Liste der zu entfernenden Spalten
+# columns_to_remove = [
+# "Basic_Demos-Enroll_Season",
+# "Physical-Height",
+# "Physical-Weight",
+# "Physical-Waist_Circumference",
+# "FGC-FGC_CU",
+# "FGC-FGC_GSND",
+# "FGC-FGC_GSD",
+# "FGC-FGC_PU",
+# "FGC-FGC_SRL",
+# "FGC-FGC_SRR",
+# "FGC-FGC_TL",
+# "PCIAT-PCIAT_01",
+# "PCIAT-PCIAT_02",
+# "PCIAT-PCIAT_03",
+# "PCIAT-PCIAT_04",
+# "PCIAT-PCIAT_05",
+# "PCIAT-PCIAT_06",
+# "PCIAT-PCIAT_07",
+# "PCIAT-PCIAT_08",
+# "PCIAT-PCIAT_09",
+# "PCIAT-PCIAT_10",
+# "PCIAT-PCIAT_11",
+# "PCIAT-PCIAT_12",
+# "PCIAT-PCIAT_13",
+# "PCIAT-PCIAT_14",
+# "PCIAT-PCIAT_15",
+# "PCIAT-PCIAT_16",
+# "PCIAT-PCIAT_17",
+# "PCIAT-PCIAT_18",
+# "PCIAT-PCIAT_19",
+# "PCIAT-PCIAT_20",
+# "SDS-Season",
+# "SDS-SDS_Total_Raw",
+# ]
+# train_csv_data, dropped_ids = clean_ids(data)
+# train_csv_data = train_csv_data.drop(columns = columns_to_remove, errors = 'ignore')
+# train_csv_data.to_csv("train_data.csv", index = False)
+# # Entferne die angegebenen Spalten
+# data_cleaned = data.drop(columns=columns_to_remove, errors='ignore')
+
+# # Kombiniere die Spalten Fitness_Endurance-Time_Mins und Fitness_Endurance-Time_Sec
+# if "Fitness_Endurance-Time_Mins" in data_cleaned.columns and "Fitness_Endurance-Time_Sec" in data_cleaned.columns:
+# data_cleaned["Fitness_Endurance-Total_Time_Seconds"] = (
+# data_cleaned["Fitness_Endurance-Time_Mins"].fillna(0) * 60 +
+# data_cleaned["Fitness_Endurance-Time_Sec"].fillna(0)
+# )
+# # Entferne die ursprünglichen Spalten
+# data_cleaned = data_cleaned.drop(columns=["Fitness_Endurance-Time_Mins", "Fitness_Endurance-Time_Sec"], errors='ignore')
+
+# # Kombiniere die PAQ_A- und PAQ_C-Spalten in PAQ-Spalten
+# if "PAQ_A-Season" in data_cleaned.columns and "PAQ_C-Season" in data_cleaned.columns:
+# data_cleaned["PAQ-Season"] = data_cleaned["PAQ_A-Season"].combine_first(data_cleaned["PAQ_C-Season"])
+# data_cleaned = data_cleaned.drop(columns=["PAQ_A-Season", "PAQ_C-Season"], errors='ignore')
+
+# if "PAQ_A-PAQ_A_Total" in data_cleaned.columns and "PAQ_C-PAQ_C_Total" in data_cleaned.columns:
+# data_cleaned["PAQ-PAQ_Total"] = data_cleaned["PAQ_A-PAQ_A_Total"].combine_first(data_cleaned["PAQ_C-PAQ_C_Total"])
+# data_cleaned = data_cleaned.drop(columns=["PAQ_A-PAQ_A_Total", "PAQ_C-PAQ_C_Total"], errors='ignore')
+
+# # Definiere die Zuordnung von Seasons zu Zahlen
+# season_mapping = {
+# "Spring": 1,
+# "Summer": 2,
+# "Fall": 3,
+# "Winter": 4
+# }
+
+# # Liste der Season-Spalten, die konvertiert werden sollen
+# season_columns = [
+# "PAQ-Season",
+# "CGAS-Season",
+# "Physical-Season",
+# "BIA-Season",
+# "PCIAT-Season",
+# "PreInt_EduHx-Season"
+# ]
+
+# # Wandle die Seasons in Zahlen um
+# for column in season_columns:
+# if column in data_cleaned.columns:
+# data_cleaned[column] = data_cleaned[column].map(season_mapping)
+
+# # Speichere die bereinigte Datei
+# data_cleaned.to_csv(output_file_path, index=False)
+
+
+
+# print(f"Die bereinigte Datei wurde gespeichert unter: {output_file_path}")
diff --git a/DM_Gruppenarbeit-main/datenvorverarbeitung/TrainCsvClean.py b/DM_Gruppenarbeit-main/datenvorverarbeitung/TrainCsvClean.py
new file mode 100644
index 0000000000000000000000000000000000000000..eee4feabe7a6f849bafea75b3a624c2eafbf00b1
--- /dev/null
+++ b/DM_Gruppenarbeit-main/datenvorverarbeitung/TrainCsvClean.py
@@ -0,0 +1,245 @@
+import pandas as pd
+
+# Dateipfade
+input_file_path = 'train.csv' # Ändere diesen Pfad bei Bedarf
+output_file_path = 'cleaned_train.csv' # Bereinigte Datei
+
+# Konstanten
+LEAST_AMOUNT_COLUMNS = 20
+IMPORTANT_COLUMNS = [
+ "BIA-BIA_FFM", "BIA-BIA_DEE", "BIA-BIA_ECW",
+ "BIA-BIA_Fat", "BIA-BIA_Frame_num","BIA-BIA_ICW",
+ "BIA-BIA_LDM", "BIA-BIA_LST", "Basic_Demos-Age",
+ "Physical-BMI", "BIA-BIA_BMR", "BIA-BIA_BMI", "BIA-BIA_SMM",
+ "BIA-BIA_TBW"
+]
+
+# Funktion zum Bereinigen der IDs
+def clean_ids(df):
+ missing_count = df.isna().sum(axis=1)
+ rows_to_drop_threshold = missing_count >= LEAST_AMOUNT_COLUMNS
+ rows_to_drop_columns = df[IMPORTANT_COLUMNS].isna().all(axis=1)
+ rows_to_drop = rows_to_drop_columns | rows_to_drop_threshold
+ line_ids = df.loc[rows_to_drop, "id"]
+ df = df[~rows_to_drop]
+
+ return df, line_ids
+
+# Lade die CSV-Datei
+data = pd.read_csv(input_file_path)
+
+# Bereinige die Daten mit clean_ids
+data, dropped_ids = clean_ids(data)
+
+# Liste der zu entfernenden Spalten
+columns_to_remove = [
+ "Basic_Demos-Enroll_Season",
+ "Physical-Height",
+ "Physical-Weight",
+ "Physical-Waist_Circumference",
+ "FGC-FGC_CU",
+ "FGC-FGC_GSND",
+ "FGC-FGC_GSD",
+ "FGC-FGC_PU",
+ "FGC-FGC_SRL",
+ "FGC-FGC_SRR",
+ "FGC-FGC_TL",
+ "PCIAT-PCIAT_01",
+ "PCIAT-PCIAT_02",
+ "PCIAT-PCIAT_03",
+ "PCIAT-PCIAT_04",
+ "PCIAT-PCIAT_05",
+ "PCIAT-PCIAT_06",
+ "PCIAT-PCIAT_07",
+ "PCIAT-PCIAT_08",
+ "PCIAT-PCIAT_09",
+ "PCIAT-PCIAT_10",
+ "PCIAT-PCIAT_11",
+ "PCIAT-PCIAT_12",
+ "PCIAT-PCIAT_13",
+ "PCIAT-PCIAT_14",
+ "PCIAT-PCIAT_15",
+ "PCIAT-PCIAT_16",
+ "PCIAT-PCIAT_17",
+ "PCIAT-PCIAT_18",
+ "PCIAT-PCIAT_19",
+ "PCIAT-PCIAT_20",
+ "PCIAT-Season",
+ "SDS-Season",
+ "SDS-SDS_Total_Raw",
+ "PCIAT-PCIAT_Total",
+]
+data = data.drop(columns=columns_to_remove, errors='ignore')
+
+# Kombiniere die Spalten Fitness_Endurance-Time_Mins und Fitness_Endurance-Time_Sec
+if "Fitness_Endurance-Time_Mins" in data.columns and "Fitness_Endurance-Time_Sec" in data.columns:
+ data["Fitness_Endurance-Total_Time_Seconds"] = (
+ data["Fitness_Endurance-Time_Mins"].fillna(0) * 60 +
+ data["Fitness_Endurance-Time_Sec"].fillna(0)
+ )
+ data = data.drop(columns=["Fitness_Endurance-Time_Mins", "Fitness_Endurance-Time_Sec"], errors='ignore')
+
+# Kombiniere die PAQ_A- und PAQ_C-Spalten in PAQ-Spalten
+if "PAQ_A-Season" in data.columns and "PAQ_C-Season" in data.columns:
+ data["PAQ-Season"] = data["PAQ_A-Season"].combine_first(data["PAQ_C-Season"])
+ data = data.drop(columns=["PAQ_A-Season", "PAQ_C-Season"], errors='ignore')
+
+if "PAQ_A-PAQ_A_Total" in data.columns and "PAQ_C-PAQ_C_Total" in data.columns:
+ data["PAQ-PAQ_Total"] = data["PAQ_A-PAQ_A_Total"].combine_first(data["PAQ_C-PAQ_C_Total"])
+ data = data.drop(columns=["PAQ_A-PAQ_A_Total", "PAQ_C-PAQ_C_Total"], errors='ignore')
+
+# Definiere die Zuordnung von Seasons zu Zahlen
+season_mapping = {
+ "Spring": 1,
+ "Summer": 2,
+ "Fall": 3,
+ "Winter": 4
+}
+
+# Liste der Season-Spalten, die konvertiert werden sollen
+season_columns = [
+ "PAQ-Season",
+ "CGAS-Season",
+ "FGC-Season",
+ "Physical-Season",
+ "Fitness_Endurance-Season",
+ "BIA-Season",
+ "PCIAT-Season",
+ "PreInt_EduHx-Season"
+]
+
+# Wandle die Seasons in Zahlen um
+for column in season_columns:
+ if column in data.columns:
+ data[column] = data[column].map(season_mapping)
+
+# Speichere die bereinigte Datei
+data.to_csv(output_file_path, index=False)
+
+print(f"Die bereinigte Datei wurde gespeichert unter: {output_file_path}")
+
+
+# import pandas as pd
+
+# # Dateipfad der ursprünglichen Datei
+# input_file_path = 'train.csv' # Ändere diesen Pfad bei Bedarf
+# output_file_path = 'cleaned_train.csv' # Dateipfad für die bereinigte Datei
+
+# # Lade die CSV-Datei
+# data = pd.read_csv(input_file_path)
+
+# """
+# how to get the ids that are not viable and have not enough columns:
+# 1. the row should have not more than 20 empty columns
+# """
+
+# LEAST_AMOUNT_COLUMNS = 20
+# IMPORTANT_COLUMNS = [
+# "BIA-BIA_FFM", "BIA-BIA_DEE", "BIA-BIA_ECW",
+# "BIA-BIA_Fat", "BIA-BIA_Frame_num","BIA-BIA_ICW",
+# "BIA-BIA_LDM", "BIA-BIA_LST", "Basic_Demos-Age",
+# "Physical-BMI", "BIA-BIA_BMR", "BIA-BIA_BMI", "BIA-BIA_SMM",
+# "BIA-BIA_TBW"
+# ]
+
+# def clean_ids(df):
+
+# missing_count = df.isna().sum(axis = 1)
+# rows_to_drop_threshold = missing_count >= LEAST_AMOUNT_COLUMNS
+# rows_to_drop_columns = df[IMPORTANT_COLUMNS].isna().all(axis=1)
+# rows_to_drop = rows_to_drop_columns | rows_to_drop_threshold
+# line_ids = df.loc[rows_to_drop, "id"]
+# df = df[~rows_to_drop]
+
+# return df, line_ids
+
+
+# # Liste der zu entfernenden Spalten
+# columns_to_remove = [
+# "Basic_Demos-Enroll_Season",
+# "Physical-Height",
+# "Physical-Weight",
+# "Physical-Waist_Circumference",
+# "FGC-FGC_CU",
+# "FGC-FGC_GSND",
+# "FGC-FGC_GSD",
+# "FGC-FGC_PU",
+# "FGC-FGC_SRL",
+# "FGC-FGC_SRR",
+# "FGC-FGC_TL",
+# "PCIAT-PCIAT_01",
+# "PCIAT-PCIAT_02",
+# "PCIAT-PCIAT_03",
+# "PCIAT-PCIAT_04",
+# "PCIAT-PCIAT_05",
+# "PCIAT-PCIAT_06",
+# "PCIAT-PCIAT_07",
+# "PCIAT-PCIAT_08",
+# "PCIAT-PCIAT_09",
+# "PCIAT-PCIAT_10",
+# "PCIAT-PCIAT_11",
+# "PCIAT-PCIAT_12",
+# "PCIAT-PCIAT_13",
+# "PCIAT-PCIAT_14",
+# "PCIAT-PCIAT_15",
+# "PCIAT-PCIAT_16",
+# "PCIAT-PCIAT_17",
+# "PCIAT-PCIAT_18",
+# "PCIAT-PCIAT_19",
+# "PCIAT-PCIAT_20",
+# "SDS-Season",
+# "SDS-SDS_Total_Raw",
+# ]
+# train_csv_data, dropped_ids = clean_ids(data)
+# train_csv_data = train_csv_data.drop(columns = columns_to_remove, errors = 'ignore')
+# train_csv_data.to_csv("train_data.csv", index = False)
+# # Entferne die angegebenen Spalten
+# data_cleaned = data.drop(columns=columns_to_remove, errors='ignore')
+
+# # Kombiniere die Spalten Fitness_Endurance-Time_Mins und Fitness_Endurance-Time_Sec
+# if "Fitness_Endurance-Time_Mins" in data_cleaned.columns and "Fitness_Endurance-Time_Sec" in data_cleaned.columns:
+# data_cleaned["Fitness_Endurance-Total_Time_Seconds"] = (
+# data_cleaned["Fitness_Endurance-Time_Mins"].fillna(0) * 60 +
+# data_cleaned["Fitness_Endurance-Time_Sec"].fillna(0)
+# )
+# # Entferne die ursprünglichen Spalten
+# data_cleaned = data_cleaned.drop(columns=["Fitness_Endurance-Time_Mins", "Fitness_Endurance-Time_Sec"], errors='ignore')
+
+# # Kombiniere die PAQ_A- und PAQ_C-Spalten in PAQ-Spalten
+# if "PAQ_A-Season" in data_cleaned.columns and "PAQ_C-Season" in data_cleaned.columns:
+# data_cleaned["PAQ-Season"] = data_cleaned["PAQ_A-Season"].combine_first(data_cleaned["PAQ_C-Season"])
+# data_cleaned = data_cleaned.drop(columns=["PAQ_A-Season", "PAQ_C-Season"], errors='ignore')
+
+# if "PAQ_A-PAQ_A_Total" in data_cleaned.columns and "PAQ_C-PAQ_C_Total" in data_cleaned.columns:
+# data_cleaned["PAQ-PAQ_Total"] = data_cleaned["PAQ_A-PAQ_A_Total"].combine_first(data_cleaned["PAQ_C-PAQ_C_Total"])
+# data_cleaned = data_cleaned.drop(columns=["PAQ_A-PAQ_A_Total", "PAQ_C-PAQ_C_Total"], errors='ignore')
+
+# # Definiere die Zuordnung von Seasons zu Zahlen
+# season_mapping = {
+# "Spring": 1,
+# "Summer": 2,
+# "Fall": 3,
+# "Winter": 4
+# }
+
+# # Liste der Season-Spalten, die konvertiert werden sollen
+# season_columns = [
+# "PAQ-Season",
+# "CGAS-Season",
+# "Physical-Season",
+# "BIA-Season",
+# "PCIAT-Season",
+# "PreInt_EduHx-Season"
+# ]
+
+# # Wandle die Seasons in Zahlen um
+# for column in season_columns:
+# if column in data_cleaned.columns:
+# data_cleaned[column] = data_cleaned[column].map(season_mapping)
+
+# # Speichere die bereinigte Datei
+# data_cleaned.to_csv(output_file_path, index=False)
+
+
+
+# print(f"Die bereinigte Datei wurde gespeichert unter: {output_file_path}")
diff --git a/DM_Gruppenarbeit-main/datenvorverarbeitung/parquet_avg.py b/DM_Gruppenarbeit-main/datenvorverarbeitung/parquet_avg.py
new file mode 100644
index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391
diff --git a/DM_Gruppenarbeit-main/readme.txt b/DM_Gruppenarbeit-main/readme.txt
new file mode 100644
index 0000000000000000000000000000000000000000..8c54e517c0ad9a139efb42e7f86701218980259a
--- /dev/null
+++ b/DM_Gruppenarbeit-main/readme.txt
@@ -0,0 +1,14 @@
+Description of what happens inside the python scripts and what purposes we have:
+
+Which folders do we have:
+ I. data
+ II. datenvorverarbeitung
+ III. methoden
+
+In folder number I. we store all the data which is necessary. Our data is coming from this website:
+https://www.kaggle.com/competitions/child-mind-institute-problematic-internet-use/data.
+
+Most important data:
+ - train parquet
+ - test parquet
+