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<speak> Here also, it is balanced.<break strength="strong"/> So here you see enough values in the research experience column. <break strength="x-strong"/> Univariate analysis is a routine check to find whether data has enough distribution or not. <break strength="x-strong"/> Whether it is discrete or continuous value?<break strength="x-strong"/> We should see that it is having enough variations so that our model is predicting well.<break strength="x-strong"/> Next, you learn bivariate analysis. <break strength="x-strong"/> In Bivariate analysis, we use different plots. <break strength="x-strong"/> One of the plots is the scatter plot.<break strength="x-strong"/> A scatter plot is a type of data visualization<break strength="weak"/> that shows the relationship between different variables.<break strength="x-strong"/> We use the data.plot.scatter method<break strength="weak"/> to draw a scatter plot.<break strength="x-strong"/> It will take the data and take both of our variables and represent them along both of these axes<break strength="weak"/> here on the bottom one we call this our x-axis. <break strength="strong"/>This is where we will display our independent variable.<break strength="x-strong"/> The plot shows all (x,y) points on the graph.<break strength="x-strong"/>  If you see it in the graph, <break strength="weak"/>it is evident. <break strength="strong"/>For example, <break strength="weak"/>this one is around 315 and 0.4. <break strength="x-strong"/> On the x-axis, <break strength="weak"/>we took a GRE score, and on the y-axis, <break strength="weak"/>we took Chance of admit.<break strength="strong"/> In this graph, we are trying to establish a relation between the GRE score and the chance of admit. <break strength="x-strong"/> The graph explains how the chance of admit depends on the GRE score.<break strength="strong"/> We can see it has a linear trend. <break strength="strong"/>As the GRE score increases, <break strength="weak"/>the chance of admit will also increase. <break strength="strong"/>They are positively correlated.<break strength="strong"/> So it will give the correlation also. <break strength="x-strong"/> </speak>