Create a Jupyter Notebook in which you do the following: For the following examples, decide whether normalisation or standardisation makes more sense: You want to build a linear regression model to predict someone's grades given how much time they have spent on various activities during a normal school week. You notice that your measurements for how much time students spend studying aren't normally distributed: some students spend almost no time studying and others study for four or more hours every day. Should you normalise or standardise this variable? You're still working on your grades study, but you want to include information on how students perform on several fitness tests as well. You have information on how many jumping jacks and push-ups each student can complete in a minute. However, you notice that students perform far more jumping jacks than push-ups: the average for the former is 40, and for the latter only 10. Should you normalise or standardise this variable? From the countries dataset, scale the "EG.ELC.ACCS.ZS" column. Visualise the scaled data. From the countries dataset, standardise the "SP.DYN.CBRT.IN" column. Visualise the data.

K L M N 0 \begin{tabular}{l|l} Q & R \end{tabular} NE.IMP.GNFS.ZS NY.GDP.PCAP.CD SP.DYN.AMRT.FE SP.DYN.AMRT.MA SP.DYN.CBRT.IN SP.DYN.CDRT.IN SP.DYN.IMRT.IN SP.DYN.LEOO.IN SF 27.37071996 \begin{tabular}{|l|l||l|} \hline 2926.483907 & 150.5956385 & 214.978616 \\ \hline \end{tabular} 9.112597 7674.475705 86.70295777 130.4652766 28.29683943 7822.762509 113.3371105272.1989096 14.98414353 11.10123874 70.83656899 \begin{tabular}{|l|l|l|l|l|l|l|l|l|} \hline 36.39294431 & 23536.9688 & 81.83552059 & 183.3326904 & 12.69648658 & 10.2022574 & 10.95942033 & 75.92923514 \\ \hline 37.46757111 & 37617.72231 & 50.15759169 & 101.4649561 & 10.35784881 & 9.420780746 & 3.479135854 & 80.746781 \\ \hline 37.4824688 & 33687.58817 & 56.09278085 & 115.6456066 & 10.7517074 & 9.723570572 & 4.071447237 & 79.72484897 \\ \hline \end{tabular} 35.62297426 1466.468021 244.0047237 299.3589538 35.79843467 9.67591394464.18045984 \begin{tabular}{|l|l|l|} \hline 80.99292033 & 2861.771348 & 156.794 \\ \hline 29.05438002 & 39326.95266 & 59.20750813 \\ \hline \end{tabular} \begin{tabular}{|l|l|} \hline 185.22 & 23.745 \\ \hline 110.7718978 & 11.5725964. \\ \hline \end{tabular} \begin{tabular}{|l|l|l|} \hline \multicolumn{1}{|c|}{6.272} & 32.4 \\ \hline \hline 64 & 8.281462562 & 5.1 \\ \hline \end{tabular} \begin{tabular}{|l|l|} \hline 68.582 \\ \hline 79.81886244 \\ \hline \end{tabular} 37.66970056 742.4718941 268.0362867 319.2584152 39.07481379 20.10375092 8641.87814 99.86486811 187.8802613