diff --git a/README.md b/README.md
index e69de29..db83b2e 100644
--- a/README.md
+++ b/README.md
@@ -0,0 +1,8 @@
+This reposetory is used for storing my datasets and scripts for data science.
+
+Install this Python libraries in your virtual environment. Use (uv) pip install ...
+
+    numpy
+    matplotlib
+    openpyxl
+    scikit-learn
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diff --git a/classification/classification_mnist_demo.py b/classification/classification_mnist_demo.py
new file mode 100644
index 0000000..6a888ee
--- /dev/null
+++ b/classification/classification_mnist_demo.py
@@ -0,0 +1,119 @@
+import math
+import matplotlib as mpl
+import matplotlib.pyplot as plt
+import numpy as np
+from sklearn.svm import SVC
+from sklearn.neighbors import KNeighborsClassifier
+from sklearn.multiclass import OneVsOneClassifier 
+from sklearn.multiclass import OneVsRestClassifier
+from sklearn.datasets import fetch_openml
+from sklearn.linear_model import SGDClassifier
+from sklearn.model_selection import cross_val_predict
+from sklearn.metrics import precision_score, recall_score, f1_score
+
+
+# Datensatz herunterladen
+print("✅ Datensatz herunterladen")
+mnist = fetch_openml('mnist_784', version=1, as_frame=False, parser='auto')
+mnist.keys()
+
+X, y = mnist["data"], mnist["target"]
+X.shape
+
+# Ziffer aus dem Datensatz: 5
+print("✅ Ziffer aus dem Datensatz: 5")
+some_digit = X[0]
+some_digit_image = some_digit.reshape(28, 28)
+plt.imshow(some_digit_image, cmap=mpl.cm.binary)
+plt.show()
+
+# Ziffer aus dem Datensatz: 0
+print("✅ Ziffer aus dem Datensatz: 0")
+some_other_digit = X[1]
+some_other_digit_image = some_other_digit.reshape(28, 28)
+plt.imshow(some_other_digit_image, cmap=mpl.cm.binary)
+plt.show()
+
+# Label
+print("✅ Label")
+print(y[0])
+y = y.astype(np.uint8)
+
+# Zahlen Matrix
+print("✅ Zahlen Matrix")
+i = 1
+for number in some_digit:
+    #28 Spalten
+    if i < 28:
+        if number > 0:
+            print("\x1b[31m{:03d}".format(math.trunc(number.item())), end = '\x1b[0m ')            
+        else:            
+            print("{:03d}".format(math.trunc(number.item())), end = ' ')
+    else:
+        print("{:03d}".format(math.trunc(number.item())))
+        i = 0
+    i = i+1
+
+# Train-Test-Split
+print("✅ Train-Test-Split")
+X_train, X_test, y_train, y_test = X[:60000], X[60000:], y[:60000], y[60000:]
+
+# Testdaten vorbereiten für die Klassifikation der Ziffer 5
+print("✅ Testdaten vorbereiten für die Klassifikation der Ziffer 5")
+
+y_train_5 = (y_train == 5)
+y_test_5 = (y_test == 5)
+
+print(y_train_5)
+
+# Logistische Regression zur binären Klassifikation (Ziffer aus dem Datensatz: 5)
+print("✅ Logistische Regression zur binären Klassifikation")
+model_log = SGDClassifier(loss="log_loss", max_iter=1000, tol=1e-3, random_state=42)
+model_log.fit(X_train, y_train_5)
+model_log.predict([some_digit])
+
+# Support Vector Machine zur binären Klassifikation (Ziffer aus dem Datensatz: 0)
+print("✅ Support Vector Machine zur binären Klassifikation")
+model_hinge = SGDClassifier(loss="hinge", max_iter=1000, tol=1e-3, random_state=42)
+model_hinge.fit(X_train, y_train_5)
+model_hinge.predict([some_other_digit])
+
+# Evaluation
+print("✅ Evaluation")
+model = model_hinge
+y_train_pred = cross_val_predict(model_hinge, X_train, y_train_5, cv=3)
+y_test_pred = cross_val_predict(model_hinge, X_test, y_test_5, cv=3)
+#precision_score(y_train_5, y_train_pred)
+precision_score(y_test_5, y_test_pred)
+#recall_score(y_train_5, y_train_pred)
+recall_score(y_test_5, y_test_pred)
+#f1_score(y_train_5, y_train_pred)
+f1_score(y_test_5, y_test_pred)
+
+# One-versus-One (OvO)
+print("✅ One-versus-One (OvO)")
+model_ovo = OneVsOneClassifier(SVC(gamma="auto", random_state=42))
+model_ovo.fit(X_train[:100], y_train[:100])
+model_ovo.predict([some_digit])
+
+# One-versus-the-Rest (OvR)
+print("✅ One-versus-the-Rest (OvR)")
+model_ovr = OneVsRestClassifier(SVC(gamma="auto", random_state=42))
+model_ovr.fit(X_train[:100], y_train[:100])
+model_ovr.predict([some_digit])
+
+# Multilabel Classification
+print("✅ Multilabel Classification")
+y_train_large = (y_train >= 7) # grosse ziffern (7,8,9)
+y_train_odd = (y_train % 2 == 1) # ungerade = true, gerade = false
+y_multilabel = np.c_[y_train_large, y_train_odd] # 1-D array als spalte in a 2-D array konvertieren
+
+model_knn = KNeighborsClassifier()
+model_knn.fit(X_train, y_multilabel)
+
+# Multiclass Multioutput Classification
+print("✅ Multiclass Multioutput Classification")
+model_svc = SVC(gamma="auto", random_state=42)
+model_svc.fit(X_train[:1000], y_train[:1000]) # y_train, not y_train_5
+model_svc.predict([some_digit])
+model_svc.classes_
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diff --git a/datasets/hardrock100_results_2022.xlsx b/datasets/hardrock100_results_2022.xlsx
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index 0000000..763d101
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diff --git a/datasets/hardrock100_results_2022_full.xlsx b/datasets/hardrock100_results_2022_full.xlsx
new file mode 100644
index 0000000..fd0e154
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diff --git a/exercise_2.py b/exercise_2.py
index 03272f0..485172f 100644
--- a/exercise_2.py
+++ b/exercise_2.py
@@ -56,6 +56,7 @@ def main():
 
     df["finish_seconds"] = df["finish"].apply(time_str_to_seconds)
 
+    # create boxplot
     df.boxplot(column=["finish_seconds"])
     plt.title("Verteilung der Zielzeiten in Sekunden")
     plt.ylabel("Sekunden")