diff --git a/ИАД/lr2/task2_Fisher.py b/ИАД/lr2/task2_Fisher.py
new file mode 100644
index 0000000..47b4e6c
--- /dev/null
+++ b/ИАД/lr2/task2_Fisher.py
@@ -0,0 +1,64 @@
+import numpy as np
+from matplotlib import pyplot as plt
+from scipy.cluster.hierarchy import dendrogram
+import pandas as pd
+from sklearn.cluster import AgglomerativeClustering
+from sklearn.datasets import load_iris
+from scipy.spatial import distance
+
+iris = load_iris()
+iris_pd=pd.DataFrame(data=np.c_[iris['data']], columns=iris['feature_names'])
+data = iris_pd[['petal length (cm)', 'petal width (cm)', 'sepal length (cm)']].to_numpy()
+
+# расчет матрицы расстояний Чебышева
+distance_matrix = np.zeros((len(data), len(data)))
+for i in range(len(data)):
+    for j in range(i+1, len(data)):
+        distance_matrix[i][j] = distance.chebyshev(data[i], data[j])
+        distance_matrix[j][i] = distance_matrix[i][j]
+
+def plot_dendrogram(model, **kwargs):
+    # Create linkage matrix and then plot the dendrogram
+
+    # create the counts of samples under each node
+    counts = np.zeros(model.children_.shape[0])
+    n_samples = len(model.labels_)
+    for i, merge in enumerate(model.children_):
+        current_count = 0
+        for child_idx in merge:
+            if child_idx < n_samples:
+                current_count += 1  # leaf node
+            else:
+                current_count += counts[child_idx - n_samples]
+        counts[i] = current_count
+
+    linkage_matrix = np.column_stack(
+        [model.children_, model.distances_, counts]
+    ).astype(float)
+
+    # Plot the corresponding dendrogram
+    dendrogram(linkage_matrix, **kwargs)
+
+
+# setting distance_threshold=0 ensures we compute the full tree.
+metric='precomputed'
+linkage="single"
+model = AgglomerativeClustering(compute_distances=True, metric=metric, linkage=linkage)
+
+model = model.fit(distance_matrix)
+print(model.labels_)
+
+plt.title('Hierarchical Clustering Dendrogram \n metric="{}", linkage="{}'.format(metric, linkage))
+# plot the top three levels of the dendrogram
+plot_dendrogram(model, truncate_mode="level", p=3)
+plt.xlabel("Number of points in node")
+
+fig1 = plt.figure()
+ax = plt.axes(projection='3d')
+ax.scatter3D(iris_pd['petal length (cm)'], iris_pd['petal width (cm)'], iris_pd['sepal length (cm)'], c = model.labels_, cmap='tab10')
+ax.set_title('Agglomerative Clustering \n metric="{}", linkage="{}"'.format(metric, linkage))
+ax.set_xlabel('petal length (cm)')
+ax.set_ylabel('petal width (cm)')
+ax.set_zlabel('sepal length (cm)')
+
+plt.show()
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diff --git a/ИАД/lr2/task3_Fisher.py b/ИАД/lr2/task3_Fisher.py
new file mode 100644
index 0000000..6c4cd95
--- /dev/null
+++ b/ИАД/lr2/task3_Fisher.py
@@ -0,0 +1,26 @@
+import numpy as np
+from matplotlib import pyplot as plt
+import pandas as pd
+from sklearn.datasets import load_iris
+from sklearn.decomposition import PCA
+
+
+iris = load_iris()
+iris_pd=pd.DataFrame(data=np.c_[iris['data']], columns=iris['feature_names'])
+data = iris_pd[['petal length (cm)', 'petal width (cm)', 'sepal length (cm)']].to_numpy()
+
+pca = PCA(n_components=2)
+pca.fit(data)
+principalComponents = pca.fit_transform(data)
+
+# вывод объясненной дисперсии
+print(pca.explained_variance_ratio_)
+
+pc_df = pd.DataFrame(data=principalComponents, columns=['PC1', 'PC2'])
+
+
+plt.scatter(pc_df['PC1'], pc_df['PC2'])
+plt.xlabel('PC1')
+plt.ylabel('PC2')
+plt.title("Projection on First Two Principal Components")
+plt.show()
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diff --git a/ИАД/lr2/task4_Fisher.py b/ИАД/lr2/task4_Fisher.py
new file mode 100644
index 0000000..179da26
--- /dev/null
+++ b/ИАД/lr2/task4_Fisher.py
@@ -0,0 +1,19 @@
+import numpy as np
+from matplotlib import pyplot as plt
+import pandas as pd
+from sklearn.datasets import load_iris
+from sklearn.manifold import TSNE
+
+
+iris = load_iris()
+iris_pd=pd.DataFrame(data=np.c_[iris['data']], columns=iris['feature_names'])
+data = iris_pd[['petal length (cm)', 'petal width (cm)', 'sepal length (cm)']].to_numpy()
+
+data_embedded = TSNE(n_components=2, learning_rate='auto', init='random', perplexity=3).fit_transform(data)
+print(data_embedded)
+
+plt.scatter(data_embedded[:, 0], data_embedded[:, 1])
+plt.title('Visualization using t-SNE')
+
+
+plt.show()
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diff --git a/ИАД/lr2/task6_Fisher.py b/ИАД/lr2/task6_Fisher.py
new file mode 100644
index 0000000..3f551b8
--- /dev/null
+++ b/ИАД/lr2/task6_Fisher.py
@@ -0,0 +1,24 @@
+import numpy as np
+from matplotlib import pyplot as plt
+import pandas as pd
+from sklearn.datasets import load_iris
+from sklearn.cluster import KMeans
+
+
+iris = load_iris()
+iris_pd=pd.DataFrame(data=np.c_[iris['data']], columns=iris['feature_names'])
+data = iris_pd[['petal length (cm)', 'petal width (cm)', 'sepal length (cm)']].to_numpy()
+
+
+model = KMeans(n_clusters=2, random_state=0, n_init="auto").fit(data)
+
+      
+fig1 = plt.figure()
+ax = plt.axes(projection='3d')
+ax.scatter3D(iris_pd['petal length (cm)'], iris_pd['petal width (cm)'], iris_pd['sepal length (cm)'], c = model.labels_, cmap='tab10')
+ax.set_title('K-means Method \n n_clusters=2')
+ax.set_xlabel('petal length (cm)')
+ax.set_ylabel('petal width (cm)')
+ax.set_zlabel('sepal length (cm)')
+
+plt.show()
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diff --git a/ИАД/lr2/task_1_Fisher.py b/ИАД/lr2/task_1_Fisher.py
new file mode 100644
index 0000000..f055b55
--- /dev/null
+++ b/ИАД/lr2/task_1_Fisher.py
@@ -0,0 +1,21 @@
+from sklearn.datasets import load_iris
+import matplotlib.pyplot as plt
+import pandas as pd
+import numpy as np
+
+# Датасет Ирисы Фишера
+iris = load_iris()
+iris_pd=pd.DataFrame(data=np.c_[iris['data'], iris['target']], columns=iris['feature_names'] + ['target'])
+
+
+
+fig = plt.figure()
+ax = plt.axes(projection='3d')
+
+ax.scatter3D(iris_pd['petal length (cm)'], iris_pd['petal width (cm)'], iris_pd['sepal length (cm)'])
+ax.set_title('3D Scatter Plot')
+ax.set_xlabel('petal length (cm)')
+ax.set_ylabel('petal width (cm)')
+ax.set_zlabel('sepal length (cm)')
+
+plt.show()
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