keeper to predict the likelihood occurrencei buying play station or not the logistic function is the sigmoid curve that is used to build the function with various parameters prerequisites before building the classifier using logistic regressionwe need to install the tkinter package on our system it can be installed from nowwith the help of the code given belowwe can create classifier using logistic regressionfirstwe will import some packagesimport numpy as np from sklearn import linear_model import matplotlib pyplot as plt nowwe need to define the sample data which can be done as followsx np array([[ ][ ][ ][ ][ ][ ][ ][ ][ ][ ][ ][ ]] np array([ ]nextwe need to create the logistic regression classifierwhich can be done as followsclassifier_lr linear_model logisticregression(solver='liblinear' = last but not the leastwe need to train this classifierclassifier_lr fit(xynowhow we can visualize the outputit can be done by creating function named logistic_visualize()def logistic_visualize(classifier_lrxy)min_xmax_x [: min( [: max( min_ymax_y [: min( [: max( in the above linewe defined the minimum and maximum values and to be used in mesh grid in additionwe will define the step size for plotting the mesh grid mesh_step_size let us define the mesh grid of and values as followsx_valsy_vals np meshgrid(np arange(min_xmax_xmesh_step_size)np arange(min_ymax_ymesh_step_size)