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--- ease:machinelearning:data_preparation [2020/06/22 09:46] – s_fuyedc
+++ ease:machinelearning:data_preparation [2020/06/22 11:39] (current) – s_fuyedc
@@ Line 1: / Line 1: @@
 ===== NEEMS Lecture: 2. Data Preparation =====
-In [[https://ease-crc.org/material/ease/machinelearning/visualizing_the_data|the previous section]] we started off by visualizing the NEEMS data as pie-charts and tables. This section is about preparing the data for training, including filling empty data cells, transforming the data in one-hot-encoding and shrinking down the tables to the relevant bits.
+In [[https://ease-crc.org/material/ease/machinelearning/visualizing_the_data|the previous section]] we started off by visualizing the NEEMS data as pie charts and tables. This section is about preparing the data for training, including filling empty data cells, transforming the data in one-hot-encoding and shrinking down the tables to the relevant bits.
 == 2.1 Filling Empty Cells ==
-We are working on the //narratives// variable here. If you want to check your implementation, put the following code below the implementation of the function, and execute the line.
+We are working on the //narratives// variable here. If you want to check your implementation, put the following code below the implementation of the function, and execute the line. The given implementation is also used later down in the notebook to check for inconsistencies in the data.
 <code python># Print modified data
 fill_empty_cells(narratives)
@@ Line 24: / Line 24: @@
 </code>
 == 2.2 Transform Categorical Values to Numeric Values ==
-One-hot-encoding transforms our data into values of 0 or 1, which makes it easier to work with. When you print out the function output on the narratives data, scroll to the left to see that the table has expanded.
+One-hot-encoding transforms our feature data into values of 0 or 1, which makes it easier to work with. When you print out the function output on the narratives data, scroll to the left to see that the table has expanded.
 <code python>
 def transform_categorial_to_one_hot_encoded(data):