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diff --git a/q01_k_means/__pycache__/__init__.cpython-36.pyc b/q01_k_means/__pycache__/__init__.cpython-36.pyc
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diff --git a/q01_k_means/__pycache__/build.cpython-36.pyc b/q01_k_means/__pycache__/build.cpython-36.pyc
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diff --git a/q01_k_means/build.py b/q01_k_means/build.py
index 925feb4..8d6f14f 100644
--- a/q01_k_means/build.py
+++ b/q01_k_means/build.py
@@ -1,18 +1,23 @@
+# %load q01_k_means/build.py
 # Default imports
 from sklearn.cluster import KMeans
 import matplotlib.pyplot as plt
 plt.switch_backend('agg')
 from sklearn import datasets
 
-
 digits = datasets.load_digits()
-
 X_train = digits.images
 y_train = digits.target
 
-# Write your solution here :
-
-
+def k_means(X_train, y_train, cluster=10, random_states=9):
+    kmeans = KMeans(n_clusters=cluster, random_state=random_states)
+    clusters = kmeans.fit_predict(digits.data)
+    centers = kmeans.cluster_centers_.reshape(10, 8, 8)
+    fig, ax = plt.subplots(1, 10, figsize=(8, 3))
+    for axi, center in zip(ax.flat, centers):
+        axi.set(xticks=[], yticks=[])
+        axi.imshow(center, interpolation='nearest', cmap='viridis')
+    plt.show();
 
 
 
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diff --git a/q02_hierarchy_clustering/__pycache__/__init__.cpython-36.pyc b/q02_hierarchy_clustering/__pycache__/__init__.cpython-36.pyc
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diff --git a/q02_hierarchy_clustering/build.py b/q02_hierarchy_clustering/build.py
index 724237c..38da838 100644
--- a/q02_hierarchy_clustering/build.py
+++ b/q02_hierarchy_clustering/build.py
@@ -1,15 +1,39 @@
+# %load q02_hierarchy_clustering/build.py
 # Default imports
 
-import pandas as pd
+import pandas as pd, numpy as np
 import matplotlib.pyplot as plt
 plt.switch_backend('agg')
 from sklearn.preprocessing import scale
 from scipy.cluster import hierarchy
+from scipy.cluster.hierarchy import dendrogram, linkage
 from sklearn import datasets
 
 digits = datasets.load_digits()
 df = pd.DataFrame(scale(digits.data), index=digits.target)
 
-# Write your solution here :
+def hierarchy_clustering(df):
+    Zc = linkage(df, 'complete')
+    Za = linkage(df, 'average')
+    Zs = linkage(df, 'single')
+    Zw = linkage(df, 'ward')
+    plt.figure(figsize=(20,10))
+    plt.locator_params(nbins=10)
+    plt.subplot(141)
+    plt.title('Complete Linkage')
+    dendrogram(Zc,p=20, truncate_mode='lastp',leaf_rotation=90, leaf_font_size=8)
+    plt.subplot(142)
+    plt.title('Average Linkage')
+    dendrogram(Za,p=20, truncate_mode='lastp',leaf_rotation=90, leaf_font_size=8)
+    plt.subplot(143)
+    plt.title('Single Linkage')
+    dendrogram(Zs,p=20, truncate_mode='lastp',leaf_rotation=90, leaf_font_size=8)
+    plt.subplot(144)
+    plt.title('Ward Linkage')
+    dendrogram(Zw,p=20, truncate_mode='lastp',leaf_rotation=90, leaf_font_size=8)
+    plt.tight_layout()
+    plt.show();
+
+
 
 
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