avg_monthly_hrs,department,filed_complaint,last_evaluation,n_projects,recently_promoted,salary,satisfaction,status,tenure 221,engineering,, XXXXXXXXXX,4,,low, XXXXXXXXXX,Left,5.0 232,support,,,3,,low,...

1 answer below »
EDA analysis and predictive analysis Multiple regression using the attached data. please provide code and comments


avg_monthly_hrs,department,filed_complaint,last_evaluation,n_projects,recently_promoted,salary,satisfaction,status,tenure 221,engineering,,0.932867640125,4,,low,0.829896195856,Left,5.0 232,support,,,3,,low,0.83454379486,Employed,2.0 184,sales,,0.788829841906,3,,medium,0.83498761146,Employed,3.0 206,sales,,0.575687930548,4,,low,0.424764288455,Employed,2.0 249,sales,,0.845217472223,3,,low,0.779043096207,Employed,3.0 140,sales,,0.589097371097,4,,medium,0.660019904653,Employed,4.0 121,sales,1.0,0.625398829756,3,,low,0.835570551332,Employed,3.0 150,engineering,,0.644585844252,4,,low,0.796683395041,Employed,3.0 215,engineering,1.0,0.524113534303,3,,medium,0.715005031778,Employed,7.0 269,support,,0.909364329867,5,,medium,0.994037397572,Employed,2.0 147,sales,,,2,,medium,0.403551807833,Left,3.0 188,sales,1.0,0.925479635028,6,,low,0.481408601913,Employed,5.0 191,support,,0.946723810178,5,,low,0.925337129205,Employed,4.0 290,engineering,,0.770247874835,6,,medium,0.0903427928993,Left,4.0 253,sales,,0.579966136579,5,,medium,0.627725954785,Employed,6.0 258,support,,0.83750251321,5,,medium,0.849667273388,Left,5.0 151,engineering,,0.452832173339,4,,medium,0.658608148415,Employed,3.0 252,IT,,0.919196292221,5,,low,0.893364991385,Left,5.0 276,product,,0.570038733441,4,,low,0.382260201481,Employed,2.0 154,marketing,,,2,,low,0.422224873681,Left,3.0 259,product,,0.763606785571,2,,low,0.787468036595,Employed,3.0 136,marketing,,,3,,low,0.441057742416,Employed,3.0 176,IT,,,3,,low,0.871386303936,Employed,3.0 231,support,,0.937508765506,4,,medium,0.668369478474,Employed,3.0 125,temp,,,2,,medium,,Employed, 109,sales,1.0,0.79806865971,3,,low,0.5378283407,Employed,3.0 130,procurement,1.0,0.716030369805,6,,low,0.17545398857,Employed,6.0 163,sales,,0.550433958621,2,,medium,0.544376725538,Employed,3.0 67,temp,,,1,,low,,Employed, 184,sales,,0.762948071371,4,,medium,0.61761099451,Employed,3.0 268,engineering,,0.943844634761,3,,low,0.936081051349,Employed,4.0 188,finance,,0.862307681863,3,,medium,1.0,Employed,4.0 107,engineering,,0.474348552013,2,,medium,0.615676166623,Employed,3.0 156,sales,,0.621111081488,3,,medium,0.428014178318,Employed,3.0 234,IT,1.0,0.861565035066,4,,medium,0.627199942868,Employed,2.0 127,sales,,,5,,low,0.834380141441,Employed,3.0 282,IT,,0.723354974146,5,,medium,0.213991819262,Employed,3.0 133,sales,,0.971212666951,3,,low,0.81881990402,Employed,4.0 270,engineering,,0.475292596644,6,,low,0.150336701933,Employed,5.0 184,,,1.0,5,,low,0.297124011916,Employed,5.0 132,marketing,,0.495013019585,2,,low,0.438559831841,Left,3.0 126,support,1.0,0.821486564704,3,,low,0.772891198476,Employed,10.0 139,product,,0.856088542854,4,,medium,0.683456025422,Employed,3.0 308,engineering,,0.863262837113,7,,medium,0.124350225113,Left,4.0 70,temp,,,3,,low,,Employed, 147,engineering,,,2,,low,0.401974706734,Left,3.0 274,support,,0.842357312745,5,,medium,0.803068372458,Left,6.0 278,sales,,0.766255776106,7,,low,0.148641753756,Left,4.0 156,engineering,,0.988904841707,4,,medium,0.456248436343,Employed,2.0 185,sales,,0.598749268628,4,,low,0.869204283803,Employed,2.0 263,sales,,0.744946770195,6,,low,0.759619999861,Employed,6.0 260,sales,,0.765783959048,5,,low,0.711329117452,Employed,3.0 224,support,,0.561019606401,5,,medium,0.905668551956,Employed,2.0 224,sales,,0.910270844185,4,,medium,0.615978243951,Employed,10.0 135,sales,,0.533361920913,2,,low,0.388070140261,Left,3.0 147,marketing,,0.713746663503,2,,low,0.967868020835,Employed,3.0 245,engineering,,0.932526593689,7,,medium,0.0611736227462,Left,4.0 203,product,1.0,0.494479274509,3,,medium,0.492376555226,Employed,3.0 235,finance,1.0,0.73584977947,4,,high,0.928861452039,Employed,2.0 187,IT,,0.734682887881,3,,low,0.213485171732,Employed,5.0 132,management,,0.535231622309,2,,low,0.42982208453,Left,3.0 258,product,,0.813773773076,7,,low,0.146593184754,Left,4.0 165,engineering,,0.522047696785,4,,low,0.851394379473,Employed,2.0 146,sales,,0.531577278913,2,,low,0.401953818946,Left,3.0 159,,,0.694100719628,3,,low,0.556401858998,Employed,3.0 198,engineering,,0.749982470997,2,,medium,0.526960725245,Left,2.0 159,marketing,,0.483106967958,2,,low,0.43881926966,Left,3.0 275,product,,0.573595902184,2,,low,0.894658512778,Employed,3.0 141,support,,0.93246221727,3,,medium,0.810725321567,Employed,3.0 173,finance,,,4,,low,0.862683386414,Employed,3.0 203,sales,,0.586132024189,5,,medium,0.703755788738,Employed,2.0 203,engineering,,0.60609019318,4,,low,0.274144973785,Employed,5.0 230,sales,,0.801731335549,5,,medium,0.129582617236,Employed,4.0 168,sales,,0.589119714762,4,,low,0.475901903919,Employed,2.0 183,IT,,0.716132845639,3,,low,0.786998508273,Employed,3.0 246,support,,0.328416715168,5,,medium,0.461553348758,Employed,3.0 209,engineering,,0.707901456874,5,,low,0.831582239545,Employed,3.0 224,sales,,1.0,3,,medium,0.527417463367,Employed,6.0 143,support,,0.583222823704,2,,low,0.477948490242,Left,3.0 140,sales,,0.999150412396,2,,medium,0.503040551037,Employed,2.0 212,support,,0.789082021068,3,,low,0.725231676188,Employed,4.0 156,marketing,,0.50256282621,3,,low,0.63550328871,Employed,5.0 186,IT,,0.859763957297,3,,medium,0.72966753059,Employed,2.0 162,sales,,0.502258814493,3,,low,0.800482237797,Employed,3.0 158,support,,0.543576313346,2,,low,0.352520082651,Left,3.0 156,sales,,,3,,low,0.549054592827,Employed,2.0 283,IT,,,3,,low,0.55000365191,Employed,3.0 113,,,0.511793620362,4,,low,0.632133263962,Employed,3.0 214,IT,1.0,,4,,low,0.96940778655,Employed,2.0 181,product,1.0,0.674893831177,3,,medium,0.547941153654,Employed,3.0 140,support,,0.801322765258,4,,low,0.526745455356,Employed,3.0 184,engineering,,0.918450089622,4,,low,0.914882604937,Employed,3.0 168,sales,1.0,0.389626690049,3,,low,0.394051564841,Employed,3.0 146,engineering,,0.55743729675,2,,medium,0.471104658609,Left,3.0 251,engineering,,0.871911091013,4,,medium,0.69605347067,Employed,2.0 219,support,,1.0,5,,low,0.734579415209,Left,5.0 170,product,,0.518013684148,2,,low,0.352082285557,Employed,3.0 187,marketing,,0.680370266157,4,,medium,0.1891154715,Employed,5.0 207,product,,0.495033514598,3,,medium,0.576405944943,Employed,3.0 165,support,,0.598530648395,5,,low,0.769536380891,Employed,3.0 241,,,0.766544820877,3,,low,0.851892327123,Employed,3.0 139,engineering,,,4,,low,0.771816992189,Employed,3.0 165,support,,0.838420070517,3,,low,0.59824818897,Employed,4.0 167,engineering,,0.625649373344,5,,low,0.487113849919,Employed,2.0 163,sales,,0.927717667159,4,,medium,0.792414076414,Employed,3.0 161,,,0.695097614045,2,,medium,0.611688696654,Employed,5.0 271,IT,,0.96930400526,6,,low,0.172048602687,Left,4.0 310,sales,,0.858179169027,7,,medium,0.14712496977,Left,4.0 257,support,,0.725018808946,4,,low,0.762030408475,Employed,3.0 278,sales,,0.862236739522,7,,low,0.0979844302499,Left,4.0 229,engineering,,0.630154827545,3,,medium,0.0815637410362,Employed,6.0 186,IT,,0.560171575754,3,,medium,0.736063189186,Employed,3.0 211,sales,,0.947729025431,4,,high,0.355979751381,Employed,7.0 248,management,,,4,,high,0.62362639988,Employed,3.0 231,management,1.0,,3,,high,0.74538874343,Employed,2.0 266,sales,,0.872041703232,3,,low,0.694190317681,Employed,5.0 135,engineering,,0.451192121989,2,,low,0.391651859939,Left,3.0 248,support,,1.0,5,,medium,0.700604105528,Left,5.0 161,IT,,0.725723556158,4,,low,0.785723886722,Employed,3.0 212,product,,0.872785533979,3,,medium,0.531863130276,Employed,2.0 103,,,0.583870365374,3,,low,0.381824395741,Employed,3.0 160,sales,,0.890712724018,3,,low,0.761765030332,Employed,2.0 268,engineering,,0.797235381041,5,,low,0.851411583712,Employed,3.0 116,sales,1.0,0.845078318802,4,,low,0.309868647222,Employed,3.0 150,,,0.559400039223,2,,low,0.444263576884,Left,3.0 146,IT,,0.500957306437,2,,low,0.411233749144,Left,3.0 215,IT,,0.421692216116,6,,medium,0.606946725712,Employed,3.0 194,sales,,0.534309122293,5,,high,0.969190797803,Employed,2.0 200,product,,0.425131894498,5,,medium,0.565407293567,Employed,4.0 213,sales,,0.770463824334,3,,high,0.543262177395,Employed,3.0 163,information_technology,,0.905180515303,3,,low,0.93331321955,Employed,2.0 148,sales,,0.47302692426,2,,low,0.443427763613,Left,3.0 245,product,,0.506201222856,2,,medium,0.454911641867,Employed,5.0 250,support,,0.873045465289,3,,low,0.5534421048,Left,2.0 250,sales,1.0,0.828825763738,5,,low,0.832955695628,Employed,2.0 173,engineering,,0.725963978215,2,,medium,0.452392397848,Employed,2.0 248,sales,1.0,0.896942281485,4,,low,0.741786835002,Employed,3.0 158,management,,0.63554573583,5,,medium,0.956238933842,Employed,3.0 196,support,,0.840207778701,4,,low,0.468987245804,Employed,2.0 265,procurement,,0.626653131615,3,,low,0.906159310018,Employed,8.0 159,engineering,,0.868121020214,3,,high,0.961235399317,Employed,3.0 262,sales,,0.907731548868,5,,medium,0.972423058356,Employed,3.0 175,sales,,0.403619418352,3,,medium,0.2846772958,Employed,3.0 278,sales,,0.7778811669,7,,low,0.114724686913,Left,4.0 248,sales,,0.859383829667,5,,low,0.933486376879,Left,5.0 275,product,,0.524095060049,3,,low,0.63172969046,Employed,2.0 258,support,,0.866158286663,5,,low,0.293123234813,Employed,6.0 253,engineering,,1.0,5,,medium,0.852952125868,Left,5.0 165,support,,0.643410636607,4,,low,0.161553642486,Employed,5.0 269,support,1.0,0.767369069925,3,,medium,0.248522034985,Employed,4.0 148,finance,,0.538478484226,2,,medium,0.467529517276,Left,3.0 156,sales,1.0,0.950282237768,4,,low,0.672060063083,Employed,2.0 274,sales,1.0,0.811500420662,4,,low,0.839058813805,Employed,2.0 196,support,1.0,1
Answered 2 days AfterOct 22, 2021

Answer To: avg_monthly_hrs,department,filed_complaint,last_evaluation,n_projects,recently_promoted,salary,satis...

Suraj answered on Oct 25 2021
121 Votes
Exploratory Data Analysis and Modelling

Introduction: As we are given the data set of house price with different variables and th
e requirement is to do the exploratory data analysis (EDA) and then fit a multiple linear regression to predict the price of a house with given values of different variables.
First, we will do some visualization to see the pattern about the data. The different plots are given as follows:
Here, first we plot the histograms for all the variables in the data set to see their distribution. The first histogram is of the dependent variable price. We can clearly see that the distribution of dependent variable is positively skewed as there is long tail towards to the right side.
The next histograms are for all the independent variables. We can see that the distribution of all the variables is positively skewed as there is long tail towards right side for all the variables.
From the next plot, we can see that there are number of outliers in the price variable. These outliers will affect the model in the fitting of the model.
From the next plot, we can see that the distribution of the number of bedrooms is symmetric as the bars are looking symmetric in both sides. Thus, we can say that the distribution of the number of bedrooms is normal distribution.
From the next plot, we can see...
SOLUTION.PDF

Answer To This Question Is Available To Download

Related Questions & Answers

More Questions »

Submit New Assignment

Copy and Paste Your Assignment Here