need a solution to the missing segments in the template notebook (anaconda notebook), csv file for data is also provided.poly_data.csvpolynomial_feat
need a solution to the missing segments in the template notebook (anaconda notebook), csv file for data is also provided.
| X1 | X2 | y | |
| 0 | 1.764052345967664 | 33.32866113947716 | 1598.2295644302442 |
| 1 | 0.4001572083672233 | 1.1831235715093356 | -626.2774031038078 |
| 2 | 0.9787379841057392 | 27.434844935052976 | 417.0846230533294 |
| 3 | 2.240893199201458 | 11.530310954494428 | 113.74201170102543 |
| 4 | 1.8675579901499675 | 29.6725668627812 | 1266.8344208408478 |
| 5 | -0.977277879876411 | 38.52535325958009 | 684.5955409692374 |
| 6 | 0.9500884175255894 | 10.701372597278363 | 304.75087808092746 |
| 7 | -0.1513572082976979 | 23.47013604229564 | 292.48894852341607 |
| 8 | -0.10321885179355784 | 24.089635319601722 | -35.4333827842747 |
| 9 | 0.41059850193837233 | 23.31782432584406 | 494.14561375257233 |
| 10 | 0.144043571160878 | 9.700183672984114 | -553.9116737519084 |
| 11 | 1.454273506962975 | 38.15721144916242 | 1735.7353385530946 |
| 12 | 0.7610377251469934 | 18.437889766087466 | 105.76571579790351 |
| 13 | 0.12167501649282841 | 34.00993822637398 | 1156.535033941508 |
| 14 | 0.44386323274542566 | 28.279691737382667 | 421.1231874064828 |
| 15 | 0.33367432737426683 | 12.600041083350213 | -356.9203305807839 |
| 16 | 1.4940790731576061 | 32.738114968396616 | 1323.7982186293536 |
| 17 | -0.20515826376580087 | 16.4637238930324 | -304.1422473441858 |
| 18 | 0.31306770165090136 | 35.363024687335304 | 1229.315292940857 |
| 19 | -0.8540957393017248 | 23.66964203279849 | -325.5421038368411 |
| 20 | -2.5529898158340787 | 35.38767911233926 | -636.7698275878148 |
| 21 | 0.6536185954403606 | 28.00873201303287 | 544.9585201194459 |
| 22 | 0.8644361988595057 | 29.28491691296598 | 702.3993044669925 |
| 23 | -0.7421650204064419 | 20.55165089514139 | 208.62181108822568 |
| 24 | 2.2697546239876076 | 38.28726175420573 | 2463.3969441040845 |
| 25 | -1.4543656745987648 | 26.115617769955858 | -214.1296835815082 |
| 26 | 0.04575851730144607 | 17.530346893769007 | -327.5740358595372 |
| 27 | -0.1871838500258336 | 24.64933535098905 | 314.70528235951036 |
| 28 | 1.5327792143584575 | 1.7485347340640076 | -620.4445110166356 |
| 29 | 1.469358769900285 | 12.761417850307424 | -325.7672812754028 |
| 30 | 0.1549474256969163 | 26.746767962214715 | 608.938791784278 |
| 31 | 0.37816251960217356 | 12.313026681207319 | -130.42548133281335 |
| 32 | -0.8877857476301128 | 25.10260173095482 | 114.74966411295873 |
| 33 | -1.980796468223927 | 17.72197933688488 | -473.432124920577 |
| 34 | -0.3479121493261526 | 6.283488504675559 | -208.1477365855922 |
| 35 | 0.15634896910398005 | 12.6330107122852 | -347.26318974443586 |
| 36 | 1.2302906807277207 | 23.22863151734933 | 310.66504465150393 |
| 37 | 1.2023798487844113 | 24.044037688678756 | 698.5447367829943 |
| 38 | -0.3873268174079523 | 23.398684705133572 | -133.7845074472544 |
| 39 | -0.30230275057533557 | 26.474831974428213 | 54.956277690832025 |
| 40 | -1.0485529650670926 | 26.432027530065866 | -167.3424786619599 |
| 41 | -1.4200179371789752 | 17.825318981924983 | -403.6619951964197 |
| 42 | -1.7062701906250126 | 35.96531723819146 | -32.45351904383013 |
| 43 | 1.9507753952317897 | 15.334912931867965 | -72.94268197489865 |
| 44 | -0.5096521817516535 | 17.998732085359446 | -322.23937810333246 |
| 45 | -0.4380743016111864 | 35.78501084561121 | 218.07002923124423 |
| 46 | -1.2527953600499262 | 32.441565572797344 | 216.40414287204263 |
| 47 | 0.7774903558319101 | 28.451654758074287 | 389.1943295925412 |
| 48 | -1.6138978475579515 | 4.9088486051797435 | -653.6643285854608 |
| 49 | -0.2127402802139687 | 36.859821936042266 | 859.9203090810113 |
| 50 | -0.8954665611936756 | 28.855410682415346 | -67.84690217608792 |
| 51 | 0.386902497859262 | 39.95503325614679 | 1721.2623406567193 |
| 52 | -0.510805137568873 | 6.828483881661756 | -644.0922759367731 |
| 53 | -1.180632184122412 | 34.85691623736036 | 296.9482212226026 |
| 54 | -0.028182228338654868 | 7.337224452378618 | -355.99689610869785 |
| 55 | 0.42833187053041766 | 25.006823007069922 | 135.0740434657735 |
| 56 | 0.06651722238316789 | 5.8289793311282185 | -258.2168685705965 |
| 57 | 0.3024718977397814 | 34.07232094356714 | 869.0122167214756 |
| 58 | -0.6343220936809636 | 32.48543939027542 | 525.5389229279147 |
| 59 | -0.3627411659871381 | 23.194928805969138 | 190.87728579171852 |
| 60 | -0.672460447775951 | 16.880148591813985 | 173.7643369864353 |
| 61 | -0.3595531615405413 | 3.697512822750384 | -124.71174743151903 |
| 62 | -0.813146282044454 | 28.19972215263798 | 9.295117956704985 |
| 63 | -1.7262826023316769 | 18.688164624444685 | -516.4728879250229 |
| 64 | 0.17742614225375283 | 29.16016837934357 | 740.725456108238 |
| 65 | -0.4017809362082619 | 34.78891071121654 | 747.5680474168734 |
| 66 | -1.6301983469660446 | 39.04533869511255 | 360.34046730123526 |
| 67 | 0.4627822555257742 | 34.37633035331183 | 678.2467575248726 |
| 68 | -0.9072983643832422 | 1.4568492832150768 | -387.1010253696748 |
| 69 | 0.05194539579613895 | 15.039144514656192 | -311.7926135506771 |
| 70 | 0.7290905621775369 | 29.469631934538263 | 817.0393883456112 |
| 71 | 0.12898291075741067 | 7.69355741319618 | -350.7301438415101 |
| 72 | 1.1394006845433007 | 21.320427641961043 | 554.10980290304 |
| 73 | -1.2348258203536526 | 3.1191815452308918 | -336.7335140984222 |
| 74 | 0.402341641177549 | 8.799864470959603 | -128.4440586134867 |
| 75 | -0.6848100909403132 | 1.722349983963945 | -481.0861308402101 |
| 76 | -0.8707971491818818 | 31.954210430939405 | 48.87138115905887 |
| 77 | -0.5788496647644155 | 9.733062834354826 | -448.968209228791 |
| 78 | -0.31155253212737266 | 14.468715547179205 | -249.31179136259846 |
| 79 | 0.05616534222974544 | 37.19517044515804 | 1088.3974754807073 |
,
{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# Polynomial Feature Selection Assignment Templaten", "1. Create an empty notebook for the assignment n", "2. Copy the cells from this template to your notebookn", "3. Add your code to cells 2, 6, 8, 17, and 18 to generate (similar) results as shown in the templaten", "4. Fully execute your notebook and submit the resultn" ] }, { "cell_type": "code", "execution_count": 1, "metadata": { "collapsed": false }, "outputs": [], "source": [ "#import packagesn", "import timen", "import numpy as npn", "from matplotlib import pyplot as pltn", "import seaborn as snsn", "from pandas import Series, DataFramen", "import pandas as pdn", "%matplotlib inlinen" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### 1. Import Data" ] }, { "cell_type": "code", "execution_count": 2, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ " Unnamed: 0 X1 X2 yn", "0 0 1.764052 33.328661 1598.229564n", "1 1 0.400157 1.183124 -626.277403n", "2 2 0.978738 27.434845 417.084623n", "3 3 2.240893 11.530311 113.742012n", "4 4 1.867558 29.672567 1266.834421n", "5 5 -0.977278 38.525353 684.595541n" ] } ], "source": [ "#Read in data from a data file to data_df in DateFrame formatn", "n", "## Type your code here to import data from poly_data.csv to data_dfn", "#########################################################n", "data_df=n", "#########################################################n", "n", "n", "#verify the dataframe is imported correctly n", "print(data_df.head(6))" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### 2. Observe Data" ] }, { "cell_type": "code", "execution_count": 3, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "<seaborn.axisgrid.JointGrid at 0xaa96780>" ] }, "execution_count": 3, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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n", "text/plain": [ "<matplotlib.figure.Figure at 0xaa966d8>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "#joint plot (or scatter plot) of X1 and yn", "sns.jointplot(data_df['X1'], data_df['y'])" ] }, { "cell_type": "code", "execution_count": 4, "metadata": { "collapsed": false, "scrolled": true }, "outputs": [ { "data": { "text/plain": [ "<seaborn.axisgrid.JointGrid at 0xad9b9b0>" ] }, "execution_count": 4, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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