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@@ -2,13 +2,13 @@
 
 
 
-<figure><img src="../.gitbook/assets/image.png" alt="" width="209"><figcaption></figcaption></figure>
+<figure><img src="../.gitbook/assets/image (5).png" alt="" width="209"><figcaption></figcaption></figure>
 
 1. Click on the _**Regressor**_ in the _**Machine Learning**_ category.
 
 
 
-<figure><img src="../.gitbook/assets/image (1).png" alt="" width="563"><figcaption></figcaption></figure>
+<figure><img src="../.gitbook/assets/image (1) (2).png" alt="" width="563"><figcaption></figcaption></figure>
 
 2. _**Model Type**_: Choose the regression model.
 
@@ -35,7 +35,7 @@
 
 
 
-<figure><img src="../.gitbook/assets/image (2).png" alt="" width="563"><figcaption></figcaption></figure>
+<figure><img src="../.gitbook/assets/image (2) (2).png" alt="" width="563"><figcaption></figcaption></figure>
 
 1. _**Fit Intercept**_: Choose whether to include the intercept.
 
@@ -47,7 +47,7 @@
 
 
 
-<figure><img src="../.gitbook/assets/image (3).png" alt="" width="563"><figcaption></figcaption></figure>
+<figure><img src="../.gitbook/assets/image (3) (2).png" alt="" width="563"><figcaption></figcaption></figure>
 
 1. _**Alpha**_: Adjust the level of regularization.
 
@@ -59,7 +59,7 @@
 
 
 
-<figure><img src="../.gitbook/assets/image (4).png" alt="" width="563"><figcaption></figcaption></figure>
+<figure><img src="../.gitbook/assets/image (4) (2).png" alt="" width="563"><figcaption></figcaption></figure>
 
 1. _**Alpha**_: Adjust the level of regularization.
 2. _**L1 ratio**_: Adjusts the balance (ratio) between _**L1 (Lasso)**_ and _**L2 (Ridge)**_ regularization.
@@ -72,7 +72,7 @@
 
 
 
-<figure><img src="../.gitbook/assets/image (5).png" alt="" width="563"><figcaption></figcaption></figure>
+<figure><img src="../.gitbook/assets/image (5) (2).png" alt="" width="563"><figcaption></figcaption></figure>
 
 1. _**C**_: Represents the degree of freedom for model regularization. Higher values of C make the model more complex, fitting the training data more closely.
 2. _**Kernel**_: Function mapping data to a higher-dimensional space, controlling model complexity.
 
@@ -1,2 +1,59 @@
 # 7. Clustering
 
+
+
+<figure><img src="../.gitbook/assets/image.png" alt="" width="210"><figcaption></figcaption></figure>
+
+1. Click on _**Clustering**_ under the _**Machine Learning**_ category.
+
+
+
+<figure><img src="../.gitbook/assets/image (1).png" alt="" width="563"><figcaption></figcaption></figure>
+
+2. _**Model type**_: Select the type of Model you want to use.
+   * [KMeans / AgglomerativeClustering](7.-clustering.md#kmeans-agglomerativeclustering)
+   * [GaussianMixture](7.-clustering.md#gaussianmixture)
+   * [DBSCAN](7.-clustering.md#dbscan)
+3. _**Allocate to**_: Specify the variable name to assign to the generated model.
+4. _**Code view**_: Preview the generated code.
+5. _**Run**_: Execute the code.
+
+
+
+***
+
+### KMeans / AgglomerativeClustering
+
+
+
+<figure><img src="../.gitbook/assets/image (2).png" alt="" width="563"><figcaption></figcaption></figure>
+
+1. _**N clusters**_: Specify the number of clusters to be generated.
+2. _**Random state**_: Set the seed value for the random number generator.
+
+
+
+***
+
+### GaussianMixture
+
+
+
+<figure><img src="../.gitbook/assets/image (3).png" alt="" width="563"><figcaption></figcaption></figure>
+
+1. _**N components**_: Specify the number of Gaussian distributions to be used by the model to describe the data, determining how many clusters the data will be divided into.
+2. _**Random state**_: Set the seed value for the random number generator.
+
+
+
+***
+
+### DBSCAN
+
+
+
+<figure><img src="../.gitbook/assets/image (4).png" alt="" width="563"><figcaption></figcaption></figure>
+
+1. _**Eps (Epsilon)**_: Specify the maximum distance (radius) for forming clusters.
+2. _**Min samples**_: Specify the minimum number of neighboring data points required for a point to be recognized as a cluster.
+