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``` ```
![image3](image3.png) ![image3](image3.png)
## KMeans methods
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|---|---|
|[fit](https://scikit-learn.org/stable/modules/generated/sklearn.cluster.KMeans.html#sklearn.cluster.KMeans.fit)(X[, y, sample_weight])|Compute k-means clustering.|
|[fit_predict](https://scikit-learn.org/stable/modules/generated/sklearn.cluster.KMeans.html#sklearn.cluster.KMeans.fit_predict)(X[, y, sample_weight])|Compute cluster centers and predict cluster index for each sample.|
|[fit_transform](https://scikit-learn.org/stable/modules/generated/sklearn.cluster.KMeans.html#sklearn.cluster.KMeans.fit_transform)(X[, y, sample_weight])|Compute clustering and transform X to cluster-distance space.|
|[get_feature_names_out](https://scikit-learn.org/stable/modules/generated/sklearn.cluster.KMeans.html#sklearn.cluster.KMeans.get_feature_names_out)([input_features])|Get output feature names for transformation.|
|[get_metadata_routing](https://scikit-learn.org/stable/modules/generated/sklearn.cluster.KMeans.html#sklearn.cluster.KMeans.get_metadata_routing)()|Get metadata routing of this object.|
|[get_params](https://scikit-learn.org/stable/modules/generated/sklearn.cluster.KMeans.html#sklearn.cluster.KMeans.get_params)([deep])|Get parameters for this estimator.|
|[predict](https://scikit-learn.org/stable/modules/generated/sklearn.cluster.KMeans.html#sklearn.cluster.KMeans.predict)(X[, sample_weight])|Predict the closest cluster each sample in X belongs to.|
|[score](https://scikit-learn.org/stable/modules/generated/sklearn.cluster.KMeans.html#sklearn.cluster.KMeans.score)(X[, y, sample_weight])|Opposite of the value of X on the K-means objective.|
|[set_fit_request](https://scikit-learn.org/stable/modules/generated/sklearn.cluster.KMeans.html#sklearn.cluster.KMeans.set_fit_request)(*[, sample_weight])|Request metadata passed to the fit method.|
|[set_output](https://scikit-learn.org/stable/modules/generated/sklearn.cluster.KMeans.html#sklearn.cluster.KMeans.set_output)(*[, transform])|Set output container.|
|[set_params](https://scikit-learn.org/stable/modules/generated/sklearn.cluster.KMeans.html#sklearn.cluster.KMeans.set_params)(**params)|Set the parameters of this estimator.|
|[set_predict_request](https://scikit-learn.org/stable/modules/generated/sklearn.cluster.KMeans.html#sklearn.cluster.KMeans.set_predict_request)(*[, sample_weight])|Request metadata passed to the predict method.|
|[set_score_request](https://scikit-learn.org/stable/modules/generated/sklearn.cluster.KMeans.html#sklearn.cluster.KMeans.set_score_request)(*[, sample_weight])|Request metadata passed to the score method.|
|[transform](https://scikit-learn.org/stable/modules/generated/sklearn.cluster.KMeans.html#sklearn.cluster.KMeans.transform)(X)|Transform X to a cluster-distance space.|
## KMeans Attributes
> **cluster_centers_** : ndarray of shape (n_clusters, n_features)
>
> Coordinates of cluster centers. If the algorithm stops before fully converging (see tol and max_iter), these will not be consistent with labels_.
> **labels_** ndarray of shape (n_samples,)
>
> Labels of each point
> **inertia_** : float
>
> Sum of squared distances of samples to their closest cluster center, weighted by the sample weights if provided.
> **n_iter_** : int
>
> Number of iterations run.
> **n_features_in_** : int
>
> Number of features seen during fit.
> **feature_names_in_** : ndarray of shape (n_features_in_,)
>
> Names of features seen during fit. Defined only when X has feature names that are all strings.