fix solutions

3springs · Oct 21, 2020 · cc5238c · cc5238c
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diff --git a/notebooks/b05_Supervised_Learning/supervised_part1.ipynb b/notebooks/b05_Supervised_Learning/supervised_part1.ipynb
@@ -1377,7 +1377,7 @@
     "\n",
     "  Let's practice some of the key concepts we have learned so far. \n",
     "    \n",
-    "  0. Choose the data for well log '30_6-14'. \n",
+    "  0. Choose the data for well log '30_6-11'. \n",
     "  1. Normalised and split the dataset provided (Use 85\\% of the data for training and 15\\% for testing)\n",
     "  2. Transform the data using label encoding (This is done automatically by scikit-learn for some ML methods. However, it is good to get used to this concept).\n",
     "  3. Train a KNN model using <code>n_neighbors</code>= 1,5,10,15\n",
@@ -1410,7 +1410,7 @@
     "    # Add Depth as column\n",
     "    geolink['DEPT'] = geolink.index.get_level_values(1)\n",
     "    # Work with one well\n",
-    "    geolink = geolink.xs('30_6-14')\n",
+    "    geolink = geolink.xs('30_6-11')\n",
     "\n",
     "    # 1. Normalise dataset and Split well log dataset here\n",
     "\n",

diff --git a/notebooks/b05_Supervised_Learning/supervised_part1.py b/notebooks/b05_Supervised_Learning/supervised_part1.py
@@ -328,7 +328,7 @@
 #
 #   Let's practice some of the key concepts we have learned so far. 
 #     
-#   0. Choose the data for well log '30_6-14'. 
+#   0. Choose the data for well log '30_6-11'. 
 #   1. Normalised and split the dataset provided (Use 85\% of the data for training and 15\% for testing)
 #   2. Transform the data using label encoding (This is done automatically by scikit-learn for some ML methods. However, it is good to get used to this concept).
 #   3. Train a KNN model using <code>n_neighbors</code>= 1,5,10,15
@@ -361,7 +361,7 @@
 #     # Add Depth as column
 #     geolink['DEPT'] = geolink.index.get_level_values(1)
 #     # Work with one well
-#     geolink = geolink.xs('30_6-14')
+#     geolink = geolink.xs('30_6-11')
 #
 #     # 1. Normalise dataset and Split well log dataset here
 #