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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 5901df3..42afc78 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
@@ -8,10 +9,41 @@
 
 X_train = digits.images
 y_train = digits.target
-
+import numpy as np
+X = np.reshape(X_train, (len(X_train), -1))
 # Write your solution here :
 
-
+def k_means(X_train,y_train,cluster=10,random_state=9):
+    km = KMeans(init='random', n_clusters=cluster)
+    km.fit(X)
+    
+    plt.imshow(X)
+    plt.show()
+    
+
+
+
+
+def k_means(X_train,y_train,cluster=10,random_state=9):
+    km = KMeans(init='random', n_clusters=cluster)
+    km.fit(X)
+    
+    plt.imshow(km.cluster_centers_)
+    plt.show()
+    
+km = KMeans(init='random', n_clusters=10)
+
+import pandas as pd
+X_train.shape
+len(X_train)
+import numpy as np
+X = np.reshape(X_train, (len(X_train), -1))
+km.fit(X)
+km.labels_.shape
+plt.figure(figsize=(10,5))
+plt.imshow(km.labels_.reshape(-1,1))
+plt.show()
+km.cluster_centers_.shape
 
 
 
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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/__pycache__/build.cpython-36.pyc b/q02_hierarchy_clustering/__pycache__/build.cpython-36.pyc
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diff --git a/q02_hierarchy_clustering/build.py b/q02_hierarchy_clustering/build.py
index ed49ea6..b92da99 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
@@ -17,7 +18,7 @@ def hierarchy_clustering(df):
             [hierarchy.complete(df), hierarchy.average(df), hierarchy.single(df), hierarchy.ward(df)],
             ['c1', 'c2', 'c3', 'c4'],
             [ax1, ax2, ax3, ax4]):
-        cluster = hierarchy.dendrogram(linkage, labels=df.index, p=12, truncate_mode="lastp", orientation='top',
+        cluster = hierarchy.dendrogram(linkage, labels=df.index, p=12, truncate_mode='lastp', orientation='top',
                                        color_threshold=0, leaf_font_size=10, distance_sort=True, ax=ax)
     ax1.set_title('Complete Linkage')
     ax2.set_title('Average Linkage')
@@ -25,3 +26,6 @@ def hierarchy_clustering(df):
     ax4.set_title('Ward')
     plt.show()
 
+
+
+
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