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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..2826499 100644
--- a/q01_k_means/build.py
+++ b/q01_k_means/build.py
@@ -1,3 +1,4 @@
+# %load q01_k_means/build.py
 # Default imports
 from sklearn.cluster import KMeans
 import matplotlib.pyplot as plt
@@ -10,8 +11,11 @@
 X_train = digits.images
 y_train = digits.target
 
-# Write your solution here :
-
+def k_means(X_train,y_train,cluster=10,random_state=9):
+    km = KMeans(init=9, n_clusters=cluster)
+    km.fit(X_train.reshape(-1,64))
+    km.labels_
+    km.cluster_centers_
 
 
 
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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..5712b07 100644
--- a/q02_hierarchy_clustering/build.py
+++ b/q02_hierarchy_clustering/build.py
@@ -1,3 +1,4 @@
+# %load q02_hierarchy_clustering/build.py
 # Default imports
 
 import pandas as pd
@@ -6,10 +7,38 @@
 from sklearn.preprocessing import scale
 from scipy.cluster import hierarchy
 from sklearn import datasets
-
+from scipy.cluster.hierarchy import dendrogram, linkage
 digits = datasets.load_digits()
 df = pd.DataFrame(scale(digits.data), index=digits.target)
+X_train=df.iloc[:, :-1]
+y_train=df.iloc[:, :-1]
+def hierarchy_clustering(df):
+    Z = linkage(df, 'average')
+    Z1 = linkage(df, 'complete')
+    Z2 = linkage(df, 'single')
+    Z3= linkage(df, 'ward')
+    plt.figure(figsize=(25, 10))
+    plt.subplot(1,1,4)
+    plt.title('Hierarc	hical Clustering Dendrogram')
+    plt.xlabel('sample index')
+    plt.ylabel('distance')
+    dendrogram(Z,leaf_rotation=90,leaf_font_size=8.,) 
+    plt.subplot(1,2,4)
+    plt.title('Hierarc	hical Clustering Dendrogram')
+    plt.xlabel('sample index')
+    plt.ylabel('distance')
+    dendrogram(Z1,leaf_rotation=90,leaf_font_size=8.,)
+    plt.subplot(2,1,4)
+    plt.title('Hierarc	hical Clustering Dendrogram')
+    plt.xlabel('sample index')
+    plt.ylabel('distance')
+    dendrogram(Z2,leaf_rotation=90,leaf_font_size=8.,) 
+    plt.subplot(2,2,4)
+    plt.title('Hierarc	hical Clustering Dendrogram')
+    plt.xlabel('sample index')
+    plt.ylabel('distance')
+    dendrogram(Z3,leaf_rotation=90,leaf_font_size=8.,) 
+    plt.show()
 
-# Write your solution here :
 
 
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