diff --git a/__init__.pyc b/__init__.pyc
index 6c0d88a..aa4f997 100644
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diff --git a/q01_k_means/__init__.pyc b/q01_k_means/__init__.pyc
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diff --git a/q01_k_means/build.py b/q01_k_means/build.py
index fca565c..9d2a3e9 100644
--- a/q01_k_means/build.py
+++ b/q01_k_means/build.py
@@ -1,16 +1,21 @@
+# %load q01_k_means/build.py
 # Default imports
 from sklearn.cluster import KMeans
 import matplotlib.pyplot as plt
 from sklearn import datasets
-
+import numpy as np
 
 digits = datasets.load_digits()
 
 X_train = digits.images
 y_train = digits.target
-
+def k_means(X_train,y_train,cluster=10,random_state=9):
+    X=X_train.reshape(1797,64)
+#X=np.matrix(zip(X_train,y_train))
+    mod=KMeans(n_clusters=cluster,random_state=random_state).fit(X)
+#mod.fit(X_train)
+#y_pred=mod.predict(y_train)
 # Write your solution here :
-
-
-
-
+#y_train.shape
+#X_train.shape
+    mod.cluster_centers_
diff --git a/q01_k_means/build.pyc b/q01_k_means/build.pyc
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diff --git a/q01_k_means/tests/__init__.pyc b/q01_k_means/tests/__init__.pyc
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diff --git a/q01_k_means/tests/test_q01_k_means.pyc b/q01_k_means/tests/test_q01_k_means.pyc
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diff --git a/q02_hierarchy_clustering/__init__.pyc b/q02_hierarchy_clustering/__init__.pyc
index 9e9464b..e3dfbe5 100644
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diff --git a/q02_hierarchy_clustering/build.py b/q02_hierarchy_clustering/build.py
index 2ba8b26..665e2f7 100644
--- a/q02_hierarchy_clustering/build.py
+++ b/q02_hierarchy_clustering/build.py
@@ -1,12 +1,65 @@
-# Default imports
-
-import pandas as pd
-import matplotlib.pyplot as plt
-from sklearn.preprocessing import scale
-from scipy.cluster import hierarchy
-from sklearn import datasets
-
-digits = datasets.load_digits()
-df = pd.DataFrame(scale(digits.data), index=digits.target)
-
-# Write your solution here :
+# %load q02_hierarchy_clustering/build.py
+# Default imports
+
+import pandas as pd
+import matplotlib.pyplot as plt
+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()
+X_train=digits.data
+y_train=digits.target
+df = pd.DataFrame(scale(X_train), index=digits.target)
+def hierarchy_clustering(df):
+# generate the linkage matrix
+    fig = plt.figure()
+    ax1 = fig.add_subplot(221)
+    Z2 = linkage(df, 'average')
+    plt.title('average')
+    plt.xlabel('sample index')
+    plt.ylabel('distance')
+    dendrogram(
+        Z2,
+        leaf_rotation=90.,  # rotates the x axis labels
+        leaf_font_size=8.,  # font size for the x axis labels
+    )
+
+    ax2 = fig.add_subplot(222)
+    Z3 = linkage(df, 'single')
+    plt.title('Single')
+    plt.xlabel('sample index')
+    plt.ylabel('distance')
+    dendrogram(
+        Z3,
+        leaf_rotation=90.,  # rotates the x axis labels
+        leaf_font_size=8.,  # font size for the x axis labels
+    )
+
+
+    ax3 = fig.add_subplot(223)
+    Z4 = linkage(df, 'ward')
+    plt.title('ward')
+    plt.xlabel('sample index')
+    plt.ylabel('distance')
+    dendrogram(
+        Z4,
+        leaf_rotation=90.,  # rotates the x axis labels
+        leaf_font_size=8.,  # font size for the x axis labels
+    )
+    ax4 = fig.add_subplot(224)
+    Z1 = linkage(df, 'complete')
+    plt.title('complete')
+    plt.xlabel('sample index')
+    plt.ylabel('distance')
+    dendrogram(
+        Z1,
+        leaf_rotation=90.,  # rotates the x axis labels
+        leaf_font_size=8.,  # font size for the x axis labels
+    )
+    plt.show()
+
+
+
+# Write your solution here :
diff --git a/q02_hierarchy_clustering/build.pyc b/q02_hierarchy_clustering/build.pyc
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diff --git a/q02_hierarchy_clustering/tests/__init__.pyc b/q02_hierarchy_clustering/tests/__init__.pyc
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diff --git a/q02_hierarchy_clustering/tests/test_q02_hierarchy_clustering.pyc b/q02_hierarchy_clustering/tests/test_q02_hierarchy_clustering.pyc
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