id,lcavol,lweight,age,lbph,svi,lcp,gleason,pgg45,lpsa 1, XXXXXXXXXX, XXXXXXXXXX,50, XXXXXXXXXX,0, XXXXXXXXXX,6,0, XXXXXXXXXX 2, XXXXXXXXXX, XXXXXXXXXX,58, XXXXXXXXXX,0, XXXXXXXXXX,6,0, XXXXXXXXXX 3,...

1 answer below ยป
This assignment deals with machine learning. The file is accessible with Jupyter in conjunction with Anaconda, and the .csv file provides the data needed. The hard deadline is April 30th at 4:30pm, but I would like to have it in a bit early to make corrections and get it ready for submission!


id,lcavol,lweight,age,lbph,svi,lcp,gleason,pgg45,lpsa 1,-0.579818495,2.769459,50,-1.38629436,0,-1.38629436,6,0,-0.4307829000000001 2,-0.994252273,3.3196260000000004,58,-1.38629436,0,-1.38629436,6,0,-0.1625189 3,-0.510825624,2.691243,74,-1.38629436,0,-1.38629436,7,20,-0.1625189 4,-1.203972804,3.282789,58,-1.38629436,0,-1.38629436,6,0,-0.1625189 5,0.751416089,3.432373,62,-1.38629436,0,-1.38629436,6,0,0.3715636 6,-1.049822124,3.228826,50,-1.38629436,0,-1.38629436,6,0,0.7654678 7,0.737164066,3.473518,64,0.61518564,0,-1.38629436,6,0,0.7654678 8,0.693147181,3.539509,58,1.53686722,0,-1.38629436,6,0,0.8544153 9,-0.7765287890000001,3.539509,47,-1.38629436,0,-1.38629436,6,0,1.047319 10,0.223143551,3.244544,63,-1.38629436,0,-1.38629436,6,0,1.047319 11,0.25464221800000003,3.604138,65,-1.38629436,0,-1.38629436,6,0,1.2669476 12,-1.347073648,3.598681,63,1.2669476,0,-1.38629436,6,0,1.2669476 13,1.613429934,3.022861,63,-1.38629436,0,-0.597837,7,30,1.2669476 14,1.477048724,2.9982290000000003,67,-1.38629436,0,-1.38629436,7,5,1.3480731 15,1.205970807,3.442019,57,-1.38629436,0,-0.43078292,7,5,1.3987169 16,1.541159072,3.061052,66,-1.38629436,0,-1.38629436,6,0,1.446919 17,-0.415515444,3.516013,70,1.24415459,0,-0.597837,7,30,1.4701758 18,2.288486169,3.649359,66,-1.38629436,0,0.37156356,6,0,1.4929041 19,-0.562118918,3.267666,41,-1.38629436,0,-1.38629436,6,0,1.5581446 20,0.182321557,3.825375,70,1.65822808,0,-1.38629436,6,0,1.5993876 21,1.147402453,3.419365,59,-1.38629436,0,-1.38629436,6,0,1.6389967 22,2.059238834,3.501043,60,1.47476301,0,1.34807315,7,20,1.6582281000000003 23,-0.544727175,3.3758800000000004,59,-0.7985076999999999,0,-1.38629436,6,0,1.6956156000000002 24,1.781709133,3.451574,63,0.43825493,0,1.178655,7,60,1.7137979 25,0.385262401,3.6674,69,1.59938758,0,-1.38629436,6,0,1.7316555 26,1.4469189830000002,3.124565,68,0.30010459,0,-1.38629436,6,0,1.7664417 27,0.512823626,3.719651,65,-1.38629436,0,-0.7985076999999999,7,70,1.8000583 28,-0.400477567,3.865979,67,1.81645208,0,-1.38629436,7,20,1.8164521 29,1.040276712,3.128951,67,0.22314355,0,0.04879016,7,80,1.8484548 30,2.409644165,3.3758800000000004,65,-1.38629436,0,1.61938824,6,0,1.8946169 31,0.285178942,4.090169,65,1.96290773,0,-0.7985076999999999,6,0,1.9242487 32,0.182321557,3.804438,65,1.70474809,0,-1.38629436,6,0,2.008214 33,1.2753628000000001,3.037354,71,1.2669476,0,-1.38629436,6,0,2.008214 34,0.009950331,3.267666,54,-1.38629436,0,-1.38629436,6,0,2.0215476000000003 35,-0.010050336,3.216874,63,-1.38629436,0,-0.7985076999999999,6,0,2.0476928 36,1.30833282,4.11985,64,2.17133681,0,-1.38629436,7,5,2.0856721 37,1.423108334,3.657131,73,-0.5798185,0,1.65822808,8,15,2.1575592999999995 38,0.457424847,2.374906,64,-1.38629436,0,-1.38629436,7,15,2.1916535 39,2.660958594,4.085136,68,1.37371558,1,1.83258146,7,35,2.2137539 40,0.797507196,3.013081,56,0.93609336,0,-0.16251893,7,5,2.2772673 41,0.620576488,3.141995,60,-1.38629436,0,-1.38629436,9,80,2.2975726 42,1.442201993,3.68261,68,-1.38629436,0,-1.38629436,7,10,2.3075726000000003 43,0.58221562,3.865979,62,1.71379793,0,-0.43078292,6,0,2.3272777 44,1.771556762,3.896909,61,-1.38629436,0,0.81093022,7,6,2.3749058 45,1.486139696,3.409496,66,1.74919985,0,-0.43078292,7,20,2.5217206 46,1.663926098,3.392829,61,0.61518564,0,-1.38629436,7,15,2.5533438 47,2.727852828,3.995445,79,1.87946505,1,2.65675691,9,100,2.5687881 48,1.16315081,4.035125,68,1.71379793,0,-0.43078292,7,40,2.5687881 49,1.745715531,3.498022,43,-1.38629436,0,-1.38629436,6,0,2.5915164 50,1.220829921,3.5681230000000004,70,1.37371558,0,-0.7985076999999999,6,0,2.5915164 51,1.091923301,3.993603,68,-1.38629436,0,-1.38629436,7,50,2.6567569 52,1.6601310269999998,4.234831,64,2.07317193,0,-1.38629436,6,0,2.677591 53,0.512823626,3.633631,64,1.4929041,0,0.04879016,7,70,2.6844403 54,2.12704052,4.121473,68,1.76644166,0,1.44691898,7,40,2.6912431 55,3.153590358,3.516013,59,-1.38629436,0,-1.38629436,7,5,2.7047113 56,1.266947603,4.280132,66,2.12226154,0,-1.38629436,7,15,2.7180005 57,0.97455964,2.865054,47,-1.38629436,0,0.50077529,7,4,2.7880929 58,0.463734016,3.764682,49,1.42310833,0,-1.38629436,6,0,2.7942279 59,0.542324291,4.178226,70,0.43825493,0,-1.38629436,7,20,2.8063861 60,1.061256502,3.851211,61,1.29472717,0,-1.38629436,7,40,2.8124102000000004 61,0.457424847,4.524502,73,2.32630162,0,-1.38629436,6,0,2.8419982000000004 62,1.997417706,3.719651,63,1.61938824,1,1.9095425,7,40,2.8535925 63,2.77570885,3.524889,72,-1.38629436,0,1.55814462,9,95,2.8535925 64,2.034705648,3.917011,66,2.00821403,1,2.1102132,7,60,2.8820035 65,2.073171929,3.623007,64,-1.38629436,0,-1.38629436,6,0,2.8820035 66,1.458615023,3.836221,61,1.32175584,0,-0.43078292,7,20,2.8875901 67,2.02287119,3.878466,68,1.78339122,0,1.32175584,7,70,2.9204698 68,2.198335072,4.050915,72,2.30757263,0,-0.43078292,7,10,2.9626924 69,-0.446287103,4.408547,69,-1.38629436,0,-1.38629436,6,0,2.9626924 70,1.193922468,4.7803830000000005,72,2.32630162,0,-0.7985076999999999,7,5,2.9729753 71,1.864080131,3.5931940000000004,60,-1.38629436,1,1.32175584,7,60,3.0130809 72,1.160020917,3.341093,77,1.74919985,0,-1.38629436,7,25,3.0373539000000003 73,1.214912744,3.825375,69,-1.38629436,1,0.22314355,7,20,3.0563569 74,1.838961071,3.236716,60,0.43825493,1,1.178655,9,90,3.0750055 75,2.9992261630000003,3.849083,69,-1.38629436,1,1.9095425,7,20,3.2752562000000003 76,3.1411304760000003,3.263849,68,-0.05129329,1,2.42036813,7,50,3.3375474 77,2.010894999,4.433789,72,2.12226154,0,0.50077529,7,60,3.3928291 78,2.537657215,4.354784,78,2.32630162,0,-1.38629436,7,10,3.4355988 79,2.648300197,3.582129,69,-1.38629436,1,2.58399755,7,70,3.4578927000000004 80,2.779440197,3.823192,63,-1.38629436,0,0.37156356,7,50,3.5130369 81,1.467874348,3.070376,66,0.55961579,0,0.22314355,7,40,3.5160131000000003 82,2.513656063,3.473518,57,0.43825493,0,2.32727771,7,60,3.5307626000000005 83,2.613006652,3.888754,77,-0.52763274,1,0.55961579,7,30,3.5652984 84,2.677590994,3.838376,65,1.11514159,0,1.74919985,9,70,3.5709402000000003 85,1.562346305,3.709907,60,1.69561561,0,0.81093022,7,30,3.5876769 86,3.302849259,3.5189800000000004,64,-1.38629436,1,2.32727771,7,60,3.6309855 87,2.024193067,3.731699,58,1.63899671,0,-1.38629436,6,0,3.6800909 88,1.731655545,3.369018,62,-1.38629436,1,0.30010459,7,30,3.7123518 89,2.807593831,4.718051999999999,65,-1.38629436,1,2.46385324,7,60,3.9843437000000006 90,1.562346305,3.69511,76,0.93609336,1,0.81093022,7,75,3.993603 91,3.246490992,4.101817,68,-1.38629436,0,-1.38629436,6,0,4.029806 92,2.532902848,3.677566000000001,61,1.34807315,1,-1.38629436,7,15,4.1295508000000005 93,2.830267834,3.876396,68,-1.38629436,1,1.32175584,7,60,4.3851468 94,3.821003607,3.896909,44,-1.38629436,1,2.1690537,7,40,4.6844434 95,2.907447359,3.396185,52,-1.38629436,1,2.46385324,7,10,5.1431245 96,2.882563575,3.77391,68,1.55814462,1,1.55814462,7,80,5.477509 97,3.4719664530000003,3.974998,68,0.43825493,1,2.90416508,7,20,5.5829322 PS3 PS3
ps3-lpf3mgrq.ipynb prostate-oscidvgo.csv problem-set-3-zwpr23n2-mljcz0tx.pdf problem-set-3-wsh0ugns-mm0b1ehy.pdf
Answered 1 days AfterApr 27, 2021

Answer To: id,lcavol,lweight,age,lbph,svi,lcp,gleason,pgg45,lpsa 1, XXXXXXXXXX, XXXXXXXXXX,50, XXXXXXXXXX,0,...

Bikram answered on Apr 28 2021
150 Votes
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"# Problem Set \\#3: Due Friday, April 30th by 4:30 pm EDT\n",
"\n",
"Answer the questions below. This notebook will be your workspace so add cells as you please. When you are finished, you will submit two objects to eLC:\n",
"\n",
"1. **Written pdf Report.** This should be short. You should include:\n",
" * the figure you create describing data variation (Exercise 1).\n",
" * the table of results (Exercise 3).\n",
" * discussion of results (Exercise 3)\n",
"\n",
" Your analysis should be professional: i.e., well-written, clear, and concise. Figures should be incorporated in your analysis. For example, it would be useful to set the title of each figure in this notebook (eg, \"Figure 1: Data Variation ...\") so in the final report you can reference the figure in the appopriate commentary. Save your report as a pdf (\"File/Save As Adobe PDF\") with the naming convention **'PS3_Report_[insert last name]'**. For example, ''PS3_Report_Thurk.pdf'. \n",
"\n",
"\n",
"2. **Jupyter Notebook.** Print the notebook as a pdf. [To print: From the file menu, choose 'print preview'. A new tab will open with the notebook presented as html. Print as a pdf.] Save your pdf notebook with the naming conention **'PS3_Workbook_[insert last name]'**. For example, 'PS3_Workbook_Thurk.pdf'. Think of the notebook as your opportunity to show your work.\n",
"\n",
"**Grading:** The problem set is worth **100 points** and partial credit is indicated for each exercise. I will grade your report (1) and use your notebook (2) to assign partial credit in the event there are errors.\n",
"\n",
"\n",
"**A reminder:** My office hours are Wednesdays 2:00PM-4:00PM EDT. Zoom information is located on the eLC course page. I'll host extra office hours friday as well.\n",
"\n",
"*You should feel free to work on this with classmates but the work you submit must be your own.*"
]
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"metadata": {
"id": "xxWE0t3WVNIN"
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"source": [
"# Exercise 1: Exploratory Data Analysis [30 points]\n",
"\n",
"The file \"prostate.csv\" contains data from a 1989 scientific [study](https://pubmed.ncbi.nlm.nih.gov/2468795/) on prostate cancer. The data includes 8 predictors and the outcome of interest is lpsa (log prostate specific antigen).\n",
"\n",
"### Part (a): Load the data. [10 points]"
]
},
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"id": "cJ350cXPVNIO",
"outputId": "a37beed7-dc29-4e3e-e69c-3d0c9c8ee0dd"
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"source": [
"import pandas as pd\n",
"\n",
"df=pd.read_csv('prostate-oscidvgo.csv')\n",
"df.head()"
],
"execution_count": 2,
"outputs": [
{
"output_type": "execute_result",
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idlcavollweightagelbphsvilcpgleasonpgg45lpsa
1-0.5798182.76945950-1.3862940-1.38629460-0.430783
12-0.9942523.31962658-1.3862940-1.38629460-0.162519
23-0.5108262.69124374-1.3862940-1.386294720-0.162519
34-1.2039733.28278958-1.3862940-1.38629460-0.162519
450.7514163.43237362-1.3862940-1.386294600.371564
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" id lcavol lweight age ... lcp gleason pgg45 lpsa\n",
"0 1 -0.579818 2.769459 50 ... -1.386294 6 0 -0.430783\n",
"1 2 -0.994252 3.319626 58 ... -1.386294 6 0 -0.162519\n",
"2 3 -0.510826 2.691243 74 ... -1.386294 7 20 -0.162519\n",
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"[5 rows x 10 columns]"
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"metadata": {
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"source": [
"### Part (b): Create a heatmap to describe variation in the data. [10 points]\n",
"\n",
"Hint: See Random Forest lecture notes."
]
},
{
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"metadata": {
"colab": {
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"id": "3uJhEoPrcWh-",
"outputId": "ce528ad2-2e62-43b6-970c-026f8cc5f573"
},
"source": [
"import seaborn as sns;\n",
"\n",
"corr = df.drop(['id'], axis=1).corr()\n",
"sns.heatmap(corr)"
],
"execution_count": 4,
"outputs": [
{
"output_type": "execute_result",
"data": {
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FFv0cYLHtJbYfpxhavs8Ix30C+AzwaDv1S6KPiOjQsnFszdO1lNthTafaELir6fndZdmTyptLN7L9k3brN5AXY9fdZY2xD6rA1LN7/7k5VfV8Vj932rq1xH30k++tJe5qR36x5zFf81//3POYAOtvu6yWuEvvnLjtznb63p881p4HzFuROJJWAv4fxf1FbRvIRB8R0UtdXEnwHmCjpufTy7KGNYGtgEslATwXmC9pb9sLl3fSJPqIiA51cXjlAmBzSZtSJPgDgDc1dpYTPj45TbukS4EjRkvykD76iIiOeRzbqOexl1HMBnABcBvwXdu3SDpW0t4rWr+06CMiOtTNKRBsnwec11J29HKO3bWdcybRR0R0aEj9fWdsEn1ERIcyqVlExIDr4qibSiTRR0R0KJOatUHS0rrrEBGxoro16qYqadFHRHSo37tu+qJF30zShyXdLOlGSceVZZdK+qKkGyT9QtKcuusZEdEwNI6tDn2V6CXtTjFT2/a2ZwKfbdq9uu1ZwLuAk0Z47ZMTBZ100297U+GICIoWfbtbHfoq0QO7ASfbfhjA9l+a9p1Rll0OrCVpneYX2p5ne7bt2Qdv84KeVTgiolvz0VdlIvXRt17HqOu6RkTE0/T7OPp+a9FfBLxd0uoAktZr2rd/WbYz8EA5uU9ERO2s9rc69FWL3vb5kmYBCyU9TjHfw7+Vux+VdD0wFTi4rjpGRLSqZwb/9vVForc9renxccBxIxz2Hdvv612tIiLa0+/9yH2R6CMiJrJ+H0c/IRJ9u1NxRkTUod8vxk6IRB8R0c+S6CMiBlz66CMiBtyy9NFHRAy2tOhrcP3pq9QS93b9b89jvm6VeqZ7+POzHq4l7rfPXruWuK/5r3/uecyNLz2h5zEBnjjts2MfVIGfX/hQLXH368I5hvs81Q9koo+I6KVcjI2IGHD93Z5Poo+I6Fha9BERA26Z+rtNn0QfEdGh/k7zSfQRER1L101ExIDL8MqIiAHX32m+gxWmJJ0iad9uViYiYiJahtve6pAWfUREhwaiRS/pKEl3SLpS0hmSjmjZv62kyyQtknSBpOeV5YdKWiDpRklnN60Fu5+kX5Tll5dlq0k6WdLNkq6X9Hdl+dsknSPpfEm/lFTP/dkREcsxPI6tDmMmeknbAW8AZgK7A7Nb9k8F/hPY1/a2wEnAp8rd59jezvZM4DbgkLL8aOA1ZfneZdm7AdveGjgQOFXSauW+WRSLg28N7C9poxHqeZikhZIWzn94SXvvPiKiCzyO/8YiaW7ZsF4s6SMj7P+ApFsl3STpYkljTnjVTot+J+BHth+1/RDw45b9Lwa2Ai6SdANwJDC93LeVpCsk3QwcBLy0LL8KOEXSocCUsmxn4DsAtm8HfgtsUe672PYDth8FbgWe8cZsz7M92/bsvVffrI23FRHRHd1q0UuaAnyFolE9AzhQ0oyWw64HZtveBvg+MGYvRzf66AXcYnvHEfadArzO9o2S3gbsCmD7nZK2B14LLJK07RgxHmt6PESuLUREH+ni8Mo5wGLbSwAknQnsQ9HABcD2JU3HXw28eayTttOivwrYq+xDnwbs2bL/DmADSTuWFZsqqdFyXxP4fdm9c1DjBZJeaPsa20cDfwQ2Aq5oHCNpC2Dj8twREX1tCLe9NXczl9thTafaELir6fndZdnyHAL8dKz6jdkytr1A0nzgJuAPwM3AA037Hy+HWX5J0trlOf8DuAU4CriGIplfQ5H4AT4naXOKbwMXAzcCtwMnlN08y4C32X5M6vOlWyJi0hvPRVbb84B5ncaU9GaKa6avGOvYdrtAPm/7Y+WomcuBRba/0dhp+wbg5a0vsn0C8IzVE2y/foQYjwJvH+HYUyi6gBrPW79RRETUqp2LrG26h6KHo2F6WfY0knYD/h14he3HWve3ajfRzysvCKwGnGr7ujZfFxEx8Lo4bHIBsLmkTSkS/AHAm5oPkPS3wNeBubbvbeekbSV6228a+6iIiMmpWy1628skHQ5cQDEi8STbt0g6Flhoez7wOWAa8L2ya/t/bO+93JOS0SsRER3r5o1Qts8DzmspO7rp8W7jPWcSfUREh4bc35MgJNFHRHQo0xRHRAy4Lo66qUQSfRett/IaPY85ZexDBsocL60l7vrbLut5zCdOq2f+vqkH/WstcZ/96WdM6zJhZIWpiIgBl66biIgBN5REHxEx2JxRNxERgy1dNxERAy4XYyMiBlyGV0ZEDLh03UREDLh+nwKhnRWmekrSFEnXSzq3qewUSb+WdEO5zaqzjhERzbq5OHgV+rFF/17gNmCtlvIP2f5+DfWJiBhVv3fddNyil7SJpNslnSbpNknfl7S6pD3K8kWSvtRooUvaQNJFkm6RdKKk30pav9w3nWLB8BM7rVdERK/YbnurQ7e6bl4MfNX2S4AHgQ9QrICyu+1tgQ2ajj0G+JntlwLfp1gEvOE/gH9l5NFKn5J0k6TjJa3aurN5wd35Dy/pzruKiGjDMG57q0O3Ev1dtq8qH3+HYsHaJbZ/XZad0XTszsCZALbPB+4DkLQncK/tRSOc/6PAlsB2wHrAh1sPsD3P9mzbs/defbMuvKWIiPYMebjtrQ7dSvStH1Nrr8A5dgL2lvQbig+CV0r6DoDt37vwGHAyMKeTykZEdJPHsdWhW4l+Y0k7lo/fBPwXsJmkTcqy/ZuOvQp4I4CkvwfWBbD9UdvTbW9CsSDuz2y/uTzueeVPAa8DftGlekdEdKzfu266NermDuDdkk4CbgXeA9wEnC/prxQrmzd8HDhD0luA/wb+F3hojPOfJmkDQMANwDu7VO+IiI71+6ibbiX6ZY3Wd4OkS2xvWbbCvwIsLHc9ALymXO18R2C7skvmSbYvBS5tev7KLtUzIqLrJvPslYdKeiuwCnA9xSgcKEbZfFfSSsDjwKEV1iEionID36K3/RtgqxHKjweOH6H8l8Dfdho3IqJfDNc0mqZd/XhnbETEhDLwLfqIiMluMvfRR0RMCmnRR0QMuCw8UoNtP9g68WVvHPOF3sf8+qqP9D4osMXU9WqJu9Emf64l7tI7ez+j988vHOv2kmo8+9MfqSXuLrccV0vcbhhO101ExGCraw6bdiXRR0R0KF03EREDrt+7bvpuKcGIiImmm0sJSpor6Q5JiyU944KJpFUlnVXuv6Zp8sjlSqKPiOjQsN32NhpJUyjmBtsdmAEcKGlGy2GHAPfZfhHF7AOfGat+SfQRER0a9lDb2xjmAIttL7H9OMXaHPu0HLMPcGr5+PvAq8rJI5criT4iokPjmY++ednTcjus6VQbAnc1Pb+7LGOkY2wvo5gR+Nmj1S8XYyMiOjSeKRBszwPmVVebZ0qij4joUBenQLgH2Kjp+fSybKRj7pa0MsXSraPeSdjTrhtJS3sZLyKiF2y3vY1hAbC5pE0lrUKxrOr8lmPmA28tH+9LsezqqCdOiz4iokPdGkdfrrx3OHABMAU4yfYtko4FFtqeD3wT+LakxcBfKD4MRlVLope0K3AsxVqxLwIuAd5FsSbsN4HZFAumn2T7eEmHAodRrFa1GHiL7YdrqHpExDN0c+ER2+cB57WUHd30+FFgv/Gcs85RN3OAf6EYK/pC4PXALGBD21vZ3ho4uTz2HNvb2Z4J3EYxjvRpmq9kn3T1Hb15BxERjG/UTR3qTPTXlmNFh4AzgJ2BJcBmkv5T0lzgwfLYrSRdIelm4CDgpa0nsz3P9mzbsw/e4cW9eg8REd3so69EnYm+9R3b9n3ATOBS4J3AieW+U4DDy1b+x4HVelTHiIgxdevO2KrU2nVTXlleCdgfuFLS+sBKts8GjgReVh67JvB7SVMpWvQREX2j31v0dY66WQB8macuxv4A2Bo4uUz+AB8tfx4FXAP8sfy5Zm+rGhGxfFlKsIntaU1PH7S9Z8shN/JUK775dScAJ1RZt4iIFTU0nIVHIiIGWhYeGYHtSykuuEZETHj9vvBIWvQRER2q6yJru5LoIyI6lK6biIgBN5yLsRERg62/2/Ogfu9b6jVJh5ULAyTuAMVM3MGOW9d7nSiylOAzHTb2IYk7AWMm7mDHreu9TghJ9BERAy6JPiJiwCXRP1Nd/XyTKe5keq+JO7gxJ4xcjI2IGHBp0UdEDLgk+oiIAZdEHxEx4JLoayTpGSu5j1RWUezVexFnspO0lqQslBO1mtQXYyW9frT9ts+pOP51tl82VlmXY/4firV4p9neWNJM4J9sv6uqmGXcnYCPAS+gmHpDFOsEb1ZRvLVsPyhpvZH22/5LFXGb4m8HnESxGpqA+4GDbS+qOO6GPPVvDIDtyyuKtY3tm8rHU4EPA3OAXwCftP1wFXGb4q8GHAK8lKZ1pG0fXGXciWiyz3Wz1yj7DFSS6CXtDuwBbCjpS0271gKWVRGzyfHAa4D5ALZvlPTyimMCfBN4P7AIGOpBvNOBPct4pki2DQYq+YBp8k3gXbavAJC0M3AysE1VASV9hmL95Vt56t/YQCWJHjiFp1aEOw54NvAF4HXA14B/rChuw7eB2yl+n4+lWE/6topjTkiTOtHbfntNoX8HLAT2pkhEDQ9RJMNK2b5Las57PUm8D9j+aQ/iANBYptL2pr2K2WKokeTLelwpqeoP8dcBL7b9WMVxGpp/iV4FbGf7CUmXUywLWrUX2d5P0j62T5V0OnDFmK+ahCZ1om+QtDZwDNBo2V4GHGv7gSri2b4RuFHS6bafqCLGKO4qu29cft1+LxW2giQ1WnyXSPocxbekJxOR7euqil3Gnw+cAfyo6q6EFpdJ+noZ2xQt7Usb/x4Vve8lwFSa/n0rtrakf6C41rdq43fZtiX1ok+48bdzv6StgP8FntODuBPOpO6jb5B0NkW/4qll0VuAmbZH7cPvQtye9luXMdcHvgjsVsa7EHiv7T9XFO+SUXbb9iuriNsU/xUUSfa1wALgTOBc249WHLfn77v8PZ4JXMzTP0zf0+1YZbyTW4o+YvsPkp4LnGb7VVXEbYr/DuBsYGuKbqRpwNG2v1Zl3IkoiR6QdIPtWWOVVRD3dkbot64q6U5mkqYArwQOBebaXqvmKnWdpLeOVG771JHKY/JI103hEUk7274SnmxpP9KDuD3ttwZoufj7ZD2AhbZ/VGHcZ1N0j+1M0ZVxJUX3WOUfapKeRXHhfX+Ki4eVJz5JnwY+a/v+8vm6wAdtH1lVzLKfehVgi7Lojl53DUr6lu2qL8I2Yr2X4gL3Q8A3KP7ffsT2hb2IP5GkRQ9ImkXxx792WXQf8NbG0LEK4jX6rd8ITKGH/daS5gFbAt8ri94A/JpixMQS2++rKO5FFKM/vlMWHQTsanu3KuI1xf0uxZC/8ym6bS63Xfm6b5Kut/23LWVVD53dleL3+DcU3XIbUfweVzW8cn5rEfB3wM8AbO9dRdym+DfaninpNcA7gSOBb1f5bzxRpUVfuLn8hVkLwPaDFcf7Qsvz2U2PTdHFUJVtgJ1sDwFIOoFipMLOwM0Vxn2e7U80Pf+kpP0rjNdwLvCOckz9UcB7JH3C9vUVx50iadXGCJjyW8WqFcf8AvD3tu8oY25BcTF424riTacYynkiTw1hnc0zf7+r0hj1swfwLdu3qGU4WRSS6Au/lnQ+cBZla6RKtv+u6hijWJfiolVjRNEawHq2hyRVOVrjQkkHAN8tn+8LXFBhvIYjbH+rHMf+SuDzFGO8t6847mnAxU0XLN9O9V1GUxtJHsD2neXIqqrMphi19e/Ah2zfIOkR25dVGLPZIkkXApsCHy3vQO7vVbprkq4bnpwOYE/gAIp+vnOBMxt99hXG/cAIxQ8Ai2zfUFHMQyi+4l5K0SJ6OfBpipbfx2x/qKK4D1F8qDT+EFcC/lo+dlUXRxtdKJL+L8U3t9NH6lapKPbuFOPLAS6yXekHm6STKP59G91jbwZWqvpOUUnTKW7E+wOwt+2Nq4zXFHclYBZFl+P95XWgDavqcp3IkuhblBfNvggcZHtKxbFOp2gV/bgs2hO4CdgE+J7tz1YU9/kUQ0hvo2jd311VP27dJJ0L3AO8muJD/BHgWtsza61YBSStCrybohsOii65r/bqBipJr6XoFvy3XsQrY76epgv8tn/Qq9gTSRJ9qW9NaVwAAAW7SURBVGm89VyKu1bPsn12xTEvB/awvbR8Pg34SVmHRbZnVBDzHRRft6cDNwA7AP9d9Xj2MnbzH+UVtn/Yg5irU/x73mz7l5KeB2xd1ciM8pvLSH9UjXskejKss5zjZ3ovWrfLmU/ooapH/Ej6KvAiim+jUPz9/sr2u6uMOxEl0QOSfgNcT9F/PN/2X0d/Rdfi3k6RdJ4on68K3Gh7y6q6FyTdDGwHXG17lqQtgU/34Oaw/FFWTNKlFNNqrExxb8a9wM9tVzqtRvn3sxHFaDUB61DcpfoH4FBXNJFb+ffzEpdJrOzKudX2llXEm8hyMbawTQ9G2ozkNOAaSY3x63sBp0tag2I0QxUetf2oJMpRIbdLenFFsZq9kqf/UZ4K3NKDuJPJ2uXoondQjEI5RlIv+qsvAr7fuAYh6e8phu2eDHyV6i58LwY2Bn5bPt8I+GVFsSa0JPrC45LeTY+nO7X9CUk/BXYqi95pe2H5+KCKwt4taR3gh8BFku7jqT+UKo30R7m4B3Enk5XLrqk3UoyE6ZUdbB/aeGL7Qkmft/1P5bfUqqwJ3CbpWorusjnAwsb4/qrH8U8kSfSFnk53qqfPlb6k3Br71nOFc6Xb/ofy4cfK+VjWpriZqBKSfkzxR9j6R7k9cG1VcSepYymGrF5pe4GkzehNC/f3kj5McUMaFN1yfyinnahyuOPRFZ57oKSPnqcNwbvJ9jbl2OMrbO9QUbxzbe8p6dc8daPJkz9d4aRmvVZe5F6uHo65joqUE+U1prcAuAr4OMVQ4Y1t55tbzZLoAUnX2p5TjoJ5F8WFpGsHKeHG4NMkWXGpX0Y2TSTpuinMK8fPH0mx8tI0evC1sLxd+yBg07K/fmPgubYHpksjf5Q9VcuKS03dc80eoBim/HV3eUpo21mDd5zSoq9ROc/MMPBK2y8pP2wutL1dzVWLCajXXZBNcb8IbMDTh84+SJH817L9lirjx9jSoqeeKWVL29t+maTrAWzfV04zG7Ei6lpx6f+0NE5+LGmB7e0kZQhtH1ip7gr0id0bSR6KhEsxI17VnihHJjTGlm9AJmWKFdfogjyKogvyVqCSaTRaTCu7HQGQ9AKK7k+Ax3sQP8aQFn2hjillAb4E/AB4jqRPUczoWPW3iBhQtk8sH14G9HIgwQeBKyX9qny+GfCu8sa/rG7VB9JHD5RjgPeiuJMPiill51c1qVhL7C0pZjgUcLHtyi+exWCS9DcUM5E+3/bukmYAO9r+ZsVxV6NI9rMp7su4CDi+2xdhY8Ul0Zd6PaVsGfMTFKsu/bxX8+vE4Crvsj4Z+HcXC+msDFxve+uK436X4uLraWXRm4B1bO9XZdxoXxJ9jSS9HdgF2JFi3csrKJa6q2zt1hhcTRdAn5wQT71Z5P7W1plWRyqL+kzqPvq6x3jbPhk4WdJzKeYnOQI4jGK6gIjx+mu5+Ebj4v4OPLWSWJWuk7SD7avLuNtTjKGPPpEWfY0knQjMoJjO9QrgSuA628tqrVhMSCoWnf9PYCvgFxRj2/etek56SbcBLwb+pyzaGLgDWEbRYNqmyvgxtkndou8DzwamAPcDfwH+lCQfK8r2deXcQi+m+FZ6R9WLf5Tm9iBGdCAt+j4g6SUUt62/H5hie3rNVYoJpFy5a7lsn9OrukR/Sou+RpL2pLgY+3KKVXl+RtGFEzEee7U8b7TeGrOiJtFPcmnR10jSlykS+xW2f1d3fWJik/RBnprumvLxAxTrD99QW8Widkn0EQNC0ukUNy3Np0j2ewI3AZsA3+vFDYDRn5Loa1D3sM4YTOV6CnvYXlo+nwb8hOJi6aKMa5+80kdfg8ynHRV5DvBY0/MngL+x/Yikx5bzmpgEkugjBsdpwDWSGndW7wWcXk4udmt91Yq6pesmYoBImg3sVD69ynbuUI0k+oiIQZeFRyIiBlwSfUTEgEuij4gYcEn0ERED7v8DeKgFvf0JpGIAAAAASUVORK5CYII=\n",
"text/plain": [
"
"
]
},
"metadata": {
"tags": [],
"needs_background": "light"
}
}
]
},
{
"cell_type": "code",
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/",
"height": 403
},
"id": "uMjY8SgjVNIR",
"outputId": "92002f6e-a7d6-468c-f107-5588528c690e"
},
"source": [
"heatmap_lcavol = pd.pivot_table(df, values='lpsa', columns='lcavol')\n",
"heatmap_lweight = pd.pivot_table(df, values='lpsa', columns='lweight')\n",
"heatmap_age = pd.pivot_table(df, values='lpsa', columns='age')\n",
"heatmap_lbph = pd.pivot_table(df, values='lpsa', columns='lbph')\n",
"heatmap_svi = pd.pivot_table(df, values='lpsa', columns='svi')\n",
"heatmap_lcp = pd.pivot_table(df, values='lpsa', columns='lcp')\n",
"heatmap_gleason = pd.pivot_table(df, values='lpsa', columns='gleason')\n",
"heatmap_pgg45 = pd.pivot_table(df, values='lpsa', columns='pgg45')\n",
"\t\n",
"sns.heatmap(heatmap_lcavol)"
],
"execution_count": 19,
"outputs": [
{
"output_type": "execute_result",
"data": {
"text/plain": [
""
]
},
"metadata": {
"tags": []
},
"execution_count": 19
},
{
"output_type": "display_data",
"data": {
"image/png": 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\n",
"text/plain": [
"
"
]
},
"metadata": {
"tags": [],
"needs_background": "light"
}
}
]
},
{
"cell_type": "code",
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/",
"height": 398
},
"id": "F_JHnRziaSZ1",
"outputId": "626b170d-1292-4dfd-b293-1ee21110511b"
},
"source": [
"sns.heatmap(heatmap_lweight)"
],
"execution_count": 9,
"outputs": [
{
"output_type": "execute_result",
"data": {
"text/plain": [
""
]
},
"metadata": {
"tags": []
},
"execution_count": 9
},
{
"output_type": "display_data",
"data": {
"image/png":...
SOLUTION.PDF
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