From cc5238cbf7ad360dfccc4c5e3b92bdf525771497 Mon Sep 17 00:00:00 2001 From: wassname Date: Wed, 21 Oct 2020 03:55:36 +0000 Subject: [PATCH] fix solutions --- notebooks/b05_Supervised_Learning/supervised_part1.ipynb | 4 ++-- notebooks/b05_Supervised_Learning/supervised_part1.py | 4 ++-- 2 files changed, 4 insertions(+), 4 deletions(-) diff --git a/notebooks/b05_Supervised_Learning/supervised_part1.ipynb b/notebooks/b05_Supervised_Learning/supervised_part1.ipynb index cfddfba..67c62d6 100644 --- 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 n_neighbors= 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 index ad2afdb..4e1a19c 100644 --- 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 n_neighbors= 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 #