[{'role': 'system', 'content': "You are an expert in machine learning and your task is to classify code snippets into one of the following categories: ['helper_functions', 'load_data', 'data_exploration', 'data_preprocessing', 'evaluation', 'modelling', 'prediction', 'result_visualization', 'save_results', 'comment_only']."}, {'role': 'user', 'content': "Identify the single most appropriate category to which the provided code belongs. Provide your response as a single category label without any additional information, commentary, or personal opinions.\\nPython Code:def anime(k):\nx_plot = np.array([df[k].ix[:,0].min(), df[k].ix[:,1].max()])\ny_plot = (-graph_b[k][-1][0] - np.dot(graph_w[k][-1][0], x_plot)) / graph_w[k][-1][1]\ndef animate(i, x_plot, y_plot, line):\ny_plot = (-graph_b[k][i][0] - np.dot(graph_w[k][i][0], x_plot)) / graph_w[k][i][1]\nline.set_data(x_plot, y_plot)\nline.axes.axis([-2, 2, -2, 2])\nreturn line,\nfig, ax = plt.subplots()\ndat1 = df[k][df[k].ix[:,2]==0]\ndat2 = df[k][df[k].ix[:,2]==1]\nplt.scatter(dat1.ix[:,0], dat1.ix[:,1], c=['red'], )\nplt.scatter(dat2.ix[:,0], dat2.ix[:,1], c=['black'])\nline, = ax.plot(x_plot, y_plot)\nanim = animation.FuncAnimation(fig, animate, frames=len(graph_w[k]), fargs=[x_plot, y_plot, line], blit=True, interval = 1000)\nprint(x_plot, y_plot)\nanim.save('data'+str(k)+'.gif')\nplt.show()"}, {'role': 'assistant', 'content': 'result_visualization'}]