<prompt> Ho fanoe ka lintlha tse ngata tsa data, theha mohlala oa linear regression ho bolela esale pele phapang ea tlhahiso.Y 6, 8, 9, 10, 12, 13, 15 X 1, 2, 3, 4, 5, 6, 7  <response>: Instruction in English: Given a set of data points, create a linear regression model to predict an output variable. . Response in English: Okay, lets create a linear regression model to predict an output variable based on the given input data points. First, well import necessary libraries, Numpy and matplotlib:pythonimport numpy as npimport matplotlib.pyplot as pltNow, lets define our input data points as numpy arrays:pythonX np.array1, 2, 3, 4, 5, 6, 7.reshape-1, 1Y np.array6, 8, 9, 10, 12, 13, 15Next, well construct a linear regression model using scikit-learns Linear Regression class. This can be done by calling the fit method of LinearRegression object.pythonfrom sklearn.linear_model import LinearRegressionmodel LinearRegressionmodel.fitX, YWe can use the fitted model to make predictions.pythonx_new np.array8, 9, 10y_new model.predictx_newprinty_newThis will return an array of predicted values for the new inputs.Now lets plot the data points and the fitted regression line:pythonplt.scatterX, Y, colorbplt.plotX, model.predictX, colorkplt.showThis will plot the given data points as blue circles and the fitted regression line as a black line.As you can see, we have created a linear regression model to predict an output variable given a set of data points. You can use the model.predict method to make predictions for new inputs. . Response in Sesotho: Ho lokile, ha re theheng mohlala oa tatellano ea tatellano ho bolela esale pele phapang e hlahisoang e ipapisitse le lintlha tse fanoeng tsa data. Taba ea pele, re tla tlisa lilaebrari tse hlokahalang, Numpy le matplotlib: python import numpy as np import matplotlib.pyplot as plt Joale, ha re hlaloseng lintlha tsa rona tsa ho kenya data e le numpy arrays: python X np.array1, 2, 3, 4, 5, 6, 7.reshape-1, 1 Y np.array6, 8, 9, 10, 12, 13, 15 Ka mora moo, re tla etsa mohlala oa linear regression re sebelisa sehlopha sa scikit-learns Linear Regression class. Sena se ka etsoa ka ho bitsa fit mokhoa oa LinearRegression ntho. python ho tsoa ho sklearn.linear_model import LinearRegression model LinearRegression model.fitX, Y Re ka sebelisa mohlala o kentsoeng ho etsa likhakanyo. python x_new np.array8, 9, 10 y_new model.predictx_ncha printy_ncha Sena se tla khutlisa letoto la boleng bo boletsoeng esale pele bakeng sa e ncha. tse kenang. Joale ha re rale lintlha tsa data le mohala o kentsoeng oa ho khutlisa: python plt.scatterX, Y, colorb plt.plotX, model.predictX, colork plt .show Sena se tla rala lintlha tse fanoeng tsa data joalo ka selikalikoe se boputsoa le mola o kentsoeng oa regression joalo ka mola o motšo. Joalo ka ha u bona, re thehile mohlala oa regression oa linear ho bolela esale pele phapang e hlahisoang e fuoang lintlha tsa data. U ka sebelisa mokhoa oa model.predict ho etsa likhakanyo bakeng sa lintlha tse ncha. 