visualpython
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@@ -1,14 +1,14 @@
-# 1. Data Sets
-
+---
+description: Import or Create Sample Data
+---
 
+# 1. Data Sets
 
-<figure><img src="../.gitbook/assets/image (144).png" alt="" width="211"><figcaption></figcaption></figure>
+<figure><img src="../.gitbook/assets/image (318).png" alt="" width="527"><figcaption></figcaption></figure>
 
 1. Click on _**Data Sets**_ in the _**Machine Learning**_ category.
 
-
-
-<figure><img src="../.gitbook/assets/image (145).png" alt="" width="563"><figcaption></figcaption></figure>
+<figure><img src="../.gitbook/assets/image (319).png" alt="" width="563"><figcaption></figcaption></figure>
 
 2. _**Load Type**_: You can load sample data (_**Load Data**_) or generate data (_**Create Data**_).
 3. _**Allocate to**_: Specify variable names to assign to the data.
 
@@ -1,16 +1,16 @@
-# 2. Data Split
-
+---
+description: Divide Your Data into Training and Testing Data
+---
 
+# 2. Data Split
 
-<figure><img src="../.gitbook/assets/image (146).png" alt="" width="211"><figcaption></figcaption></figure>
+<figure><img src="../.gitbook/assets/image (320).png" alt="" width="516"><figcaption></figcaption></figure>
 
 1. Click on _**Data Split**_ in the _**Machine Learning**_ category.
 
+<figure><img src="../.gitbook/assets/image (321).png" alt="" width="563"><figcaption></figcaption></figure>
 
-
-<figure><img src="../.gitbook/assets/image (147).png" alt="" width="563"><figcaption></figcaption></figure>
-
-2. _**Input Data**_: Choose whether the target data is included in the input data. If it is, select _**Feature Data**_ and _**Target Data**_ separately. You can also select specific columns from one dataset using the _**funnel icon**_.
+2. _**Input Data**_: Choose whether the target data is included in the input data. If it is, select _**Feature Data**_ and _**Target Data**_ separately. You can also select specific columns from one dataset using the funnel icon.
 3. _**Test Size**_: Select the percentage of input data to use for testing purposes.
 4. _**Random State**_: Generate the same random state, ensuring consistent data splits each time. (If not set, data will be randomly split differently each time.)
 5. _**Shuffle**_: Shuffle the data randomly to prevent the model from relying on the order of the data, thereby reducing bias and improving generalization performance.