(self,
                                preprocess=True,
                                scatters=[],
                                generate_plots=True,
                                drop=None,
                                clusters=None,
                                base_clusters=2,
                                verbose=0,
                                n_init=10,
                                max_iter=300,
                                random_state=42,
                                text=[]
                                )
Calls the body of the kmeans_clustering code in the supplementaries.py file. Can be used without any preprocessing and/or parameters.

        :param dataset: The dataset being used in the k-means clustering algorithm (str).
        :param scatters: A list of various types of scatter plots.
        :param preprocess: Preprocess the data (bool).
        :param generate_plots: Generate plots for the model (bool).
        :param drop: A list of the dataset's columns to drop.
        :param base_clusters: Number of clusters to generate (int).
        :param verbose: Printing the logging information (int).
        :param n_init: Number of times the function will run with different seeds (int).
        :param max_iter: Maximum number of iterations the function will run (int).
        :param random_state: Initialize a pseudo-random number generator (int).
        :param text: A list of columns to perform text embedding on.

        :return: a model and information to along with it stored in the self.models dictionary.
Calls the body of the kmeans_clustering code in the supplementaries.py file. Can be used without any preprocessing and/or parameters.
Calls the body of the kmeans_clustering code in the supplementaries . py file . Can be used without any preprocessing and / or parameters .
def kmeans_clustering_query(self,
                                preprocess=True,
                                scatters=[],
                                generate_plots=True,
                                drop=None,
                                clusters=None,
                                base_clusters=2,
                                verbose=0,
                                n_init=10,
                                max_iter=300,
                                random_state=42,
                                text=[]
                                ):
        '''
        Calls the body of the kmeans_clustering code in the supplementaries.py file. Can be used without any preprocessing and/or parameters.

        :param dataset: The dataset being used in the k-means clustering algorithm (str).
        :param scatters: A list of various types of scatter plots.
        :param preprocess: Preprocess the data (bool).
        :param generate_plots: Generate plots for the model (bool).
        :param drop: A list of the dataset's columns to drop.
        :param base_clusters: Number of clusters to generate (int).
        :param verbose: Printing the logging information (int).
        :param n_init: Number of times the function will run with different seeds (int).
        :param max_iter: Maximum number of iterations the function will run (int).
        :param random_state: Initialize a pseudo-random number generator (int).
        :param text: A list of columns to perform text embedding on.

        :return: a model and information to along with it stored in the self.models dictionary.
        '''

        self.models['k_means_clustering'] = k_means_clustering(
            dataset=self.dataset,
            scatters=scatters,
            clusters=clusters,
            preprocess=preprocess,
            generate_plots=generate_plots,
            drop=drop,
            base_clusters=base_clusters,
            verbose=verbose,
            n_init=n_init,
            max_iter=max_iter,
            random_state=random_state,
            text=text
        )

        self.latest_model = 'k_means_clustering'
        clearLog()
def kmeans_clustering_query ( self , preprocess = True , scatters = [ ] , generate_plots = True , drop = None , clusters = None , base_clusters = 2 , verbose = 0 , n_init = 10 , max_iter = 300 , random_state = 42 , text = [ ] ) : self . models [ 'k_means_clustering' ] = k_means_clustering ( dataset = self . dataset , scatters = scatters , clusters = clusters , preprocess = preprocess , generate_plots = generate_plots , drop = drop , base_clusters = base_clusters , verbose = verbose , n_init = n_init , max_iter = max_iter , random_state = random_state , text = text ) self . latest_model = 'k_means_clustering' clearLog ( )