Using the attached dataset to develop, train, and evaluate a group of linear regression models to predict the price (dependent variable) of a Monet painting from a few of its features (independent variables). Create your model in Python.

Tasks: 1. Create at least two simple linear regression models, each of them has one different independent variable (you may transform the raw independent variable into different formats, such as to conduct a logarithmic transformation or combine two variables into a new variable such as Size = width * height). You may consider one variable as Size, and another one as Width. Create a scatter plot for showing the relationship between the independent variable and the dependent variable for each model, and also showing the linear regression line in the same plot. Calculate the error of the prediction with test data.

2. Create a multivariate linear regression model. You may need to consider the normalization of the raw data. Calculate the error of the prediction with test data.

Dataset: monet.csv

Submit your code, test results including visualizations, and report.

PRICE,HEIGHT,WIDTH,SIGNED,PICTURE,HOUSE

3.9937800,21.3000000,25.6000000,1,1,1

8.8000000,31.9000000,25.6000000,1,2,2

.1316940,6.9000000,15.9000000,0,3,3

2.0375000,25.7000000,32,1,4,2

1.4875000,25.7000000,32,1,4,2

1.8700000,25.6000000,31.9000000,1,4,1

5.2825000,25.5000000,35.6000000,1,5,1

5.0657500,26,34.3000000,1,5,2

1.3750000,25.6000000,36.2000000,1,5,2

2.5300000,25.6000000,36.4000000,1,6,2

3.7425000,25.6000000,36.4000000,1,6,2

.3643430,25.6000000,36.2000000,1,7,2

2.7238700,31.9000000,39.4000000,1,8,2

3.5200000,23.6000000,31.9000000,1,9,1

.4975000,19.5000000,25,1,10,2

9.3500000,32.7000000,26.8000000,1,11,1

1.2195000,25.5000000,36,1,12,2

.4070000,25.6000000,39.4000000,1,12,2

3.7133900,25.6000000,36.2000000,1,13,3

5.2964060,25.8000000,21.5000000,1,14,2

2.8620000,21.4000000,28.9000000,1,15,2

4.1800000,26,32.5000000,1,16,1

2.4807500,26,32.5000000,1,16,2

.2370680,50,14.6000000,0,17,2

.5274460,22.7000000,28.1000000,1,18,1

33.0135040,35,39.4000000,1,19,2

.0355490,37.4000000,39.4000000,0,20,3

.4950000,32.1000000,23.5000000,1,21,1

1.0780000,32.1000000,23.5000000,1,21,1

.4315000,25.7000000,36.4000000,0,22,2

3.4800000,25.7000000,36.5000000,1,23,2

.4645000,21.5000000,25.9000000,1,24,1

1.6500000,25.6000000,31.9000000,1,25,1

4.4880000,28.7000000,36.2000000,1,26,2

.1565000,23.7000000,23.7000000,0,27,2

.1913460,23.6000000,23.6000000,0,27,2

1.2160000,29.3000000,29.3000000,1,28,1

.7975000,32,32.6000000,1,29,2

1.2100000,25.7000000,32,1,30,1

.6995160,21.3000000,25.6000000,1,31,2

.7625090,25.6000000,31.9000000,1,32,2

.7604860,23.9000000,31.5000000,1,33,1

.8825000,23.6000000,28.7000000,1,34,1

3.7400000,32.3000000,23.6000000,1,35,2

.2190050,19.7000000,22.4000000,0,36,2

1.3906200,23.6000000,31.5000000,1,37,2

2.8695000,23.6000000,28,1,38,1

1.8725000,21.3000000,25.8000000,1,39,2

.7150000,21.3000000,25.8000000,1,39,1

2.3157500,18.1000000,15,1,40,2

5.9791900,51.6000000,38.2000000,0,41,1

1.1333110,21.7000000,25.6000000,1,42,1

9.0225000,24.4000000,31.5000000,1,43,1

.9960000,27.2000000,36.5000000,1,44,1

.7627740,27.2000000,36.5000000,1,44,1

.9960000,36.3000000,28.9000000,1,45,1

3.5591110,26,36.5000000,1,46,1

.9925000,25.7000000,31.7000000,0,47,1

3.5225000,25.6000000,31.9000000,1,48,1

.3300000,25.8000000,32.1000000,0,49,2

3.9625000,25.5000000,31.7000000,1,50,1

4.0003700,28.7000000,39.4000000,1,51,1

.7700000,26,31.9000000,1,52,2

1.0620260,23.6000000,31.9000000,1,53,1

.2750000,22.9000000,30.7000000,1,54,1

.7920000,23.6000000,29.1000000,1,55,2

.1210000,31.9000000,23.4000000,1,56,1

1.4036010,51.2000000,35,1,57,1

2.0375000,21.1000000,24.2000000,1,58,1

3.5027840,21.1000000,24.2000000,1,58,1

1.0941110,25.6000000,39.4000000,0,59,2

.2665000,46.6000000,14.5000000,0,60,2

.6050000,46.7000000,14.6000000,0,60,2

.7757500,23.5000000,32,1,61,2

.7362010,23,32,1,61,2

6.1000000,51.8000000,34.8000000,1,62,1

2.2475000,21.3000000,31.9000000,1,63,1

1.8725000,21.3000000,31.9000000,1,63,1

.8250000,21.3000000,28.7000000,1,64,1

.7975000,24,19.7000000,1,65,2

.6646570,50.6000000,14.6000000,1,66,2

2.6183500,46.9000000,14.4000000,1,66,3

.5940000,47.5000000,14.9000000,1,66,2

.2765800,50.6000000,14.6000000,1,66,2

15.4025000,23.7000000,32.2000000,1,67,2

25.4177000,23.6000000,32.3000000,1,67,2

.3235080,23.7000000,29.4000000,0,68,3

.1650000,23.6000000,28.7000000,0,68,1

.7210000,28.7000000,36.2000000,1,69,1

1.0450000,18.9000000,29.9000000,1,70,1

1.1550000,23.6000000,31.9000000,1,71,2

.4600000,23.5000000,32,1,72,2

2.8657500,25.5000000,32,1,73,2

.7705770,28.7000000,36.4000000,1,74,2

1.7892300,23.2000000,31.5000000,1,75,3

.7725000,23.6000000,31.9000000,1,76,2

.1100000,23.6000000,28.7000000,0,77,1

1.5425000,24,29.1000000,1,78,1

2.2000000,24.1000000,29.4000000,1,78,1

.7700000,25.8000000,36.2000000,1,79,1

3.3000000,25.6000000,36.2000000,1,80,2

2.4225000,25.6000000,31.9000000,1,81,2

.1314800,25.6000000,31.9000000,1,82,2

.0589010,11.2000000,19.7000000,0,83,3

1.0450000,23.6000000,29,1,84,1

.2090000,23.8000000,31.9000000,1,85,2

.0505450,11,11,0,86,1

.0521720,18.3000000,29,0,87,1

.0326580,11.7000000,27.5000000,0,88,3

1.7600000,35,27,1,89,2

.5985370,21.3000000,25.6000000,0,90,2

1.4300000,25.6000000,39.4000000,1,91,1

.6845000,23.6000000,29.1000000,1,92,2

.7000000,21.7000000,31.9000000,1,93,1

1.6560000,24,31.1000000,1,94,1

.5305000,23.5000000,29,1,95,1

3.1146800,25.2000000,39,1,96,2

.3435000,15.5000000,18.9000000,1,97,2

1.6829900,28.7000000,39.4000000,1,98,1

2.1280000,23.6000000,29.5000000,1,99,2

4.7300000,23.6000000,29.5000000,1,100,1

3.0895000,32.6000000,24.5000000,1,101,2

11,21.3000000,29.1000000,1,102,2

1.0475000,21.9000000,28.7000000,1,103,2

.4910710,23.6000000,39.4000000,1,104,2

.5759200,15.9000000,21.3000000,1,105,2

2.3358050,25.6000000,36.2000000,1,106,1

.7175000,25.5000000,36.3000000,1,107,2

1.8150000,25.9000000,32.9000000,1,108,2

.7222720,23.9000000,32,1,109,1

.4975000,23.7000000,32,1,109,1

.2844820,23.6000000,31.9000000,0,110,2

1.4591800,23.6000000,39.4000000,1,111,2

.4860480,31.9000000,23.6000000,1,112,1

2.2625230,47.2000000,39.4000000,0,113,2

1.6560000,47.5000000,39.5000000,0,113,1

.8525000,78.7000000,39.8000000,0,114,2

1.0805000,39.8000000,39.8000000,1,115,1

.6075000,39.8000000,39.8000000,0,115,2

3.5000000,78.7000000,39.5000000,0,116,1

6.6000000,23.8000000,28.9000000,1,117,2

.6325000,21.7000000,29.1000000,1,118,2

5.0180900,24,31.5000000,1,119,2

.6325000,21.5000000,28.9000000,1,120,2

.4728540,23.6000000,28.9000000,1,121,1

1.2248600,41.7000000,28.5000000,1,122,2

12.1893000,41.7000000,28.7000000,1,122,1

.3405860,20.5000000,26.8000000,1,123,3

.6075000,25.8000000,31.9000000,1,124,1

1.0510000,26.1000000,32.2000000,1,125,1

1.2125000,26.3000000,32.1000000,1,125,1

2.2025000,24,39.3000000,1,126,2

1.1645580,23.2000000,31.5000000,1,127,2

.3280670,24,29.1000000,1,128,2

.9080000,25.6000000,39.4000000,1,129,1

2.4257500,25.5000000,31.9000000,1,130,2

1.4875000,25.6000000,31.9000000,1,130,1

9.6825000,57.9000000,89,1,131,1

1.2760500,19.7000000,24,1,132,2

.2885000,21,28.7000000,0,133,2

.3595470,28.7000000,35.8000000,1,134,2

.4059680,24.5000000,32,1,135,1

.8695000,27.9000000,36.5000000,0,136,1

.8050070,36.3000000,28.9000000,0,137,1

4.7115000,28.7000000,36.2000000,1,138,1

1.5425000,25.6000000,36.2000000,1,139,1

.2530000,25.6000000,31.7000000,0,140,1

.5800000,28.7000000,36.2000000,0,141,1

6.6025000,25.6000000,36.3000000,1,142,3

.3238600,23.2000000,31.5000000,1,143,2

16.5665470,18.7000000,29.1000000,1,144,2

10.7800000,18.7000000,29.1000000,1,144,1

1.1862500,28.7000000,36.2000000,1,145,2

.9849480,28.7000000,23.6000000,1,146,2

3.8500000,29,36.5000000,1,147,1

.7902290,29,36.5000000,1,147,2

2.3261290,29,36.5000000,1,147,2

.2860000,25.6000000,36.2000000,0,148,2

5.1375450,20.9000000,28.7000000,1,149,1

4.6300800,20.9000000,28.7000000,1,149,2

.7876600,25.6000000,31.5000000,1,150,1

.6075000,23.6000000,37.9000000,1,151,2

.4442730,23.6000000,31.9000000,0,152,3

.2539400,23.6000000,31.9000000,1,152,2

3.3680000,26.8000000,35.4000000,1,153,2

.3850000,25.6000000,31.9000000,1,154,2

2.9195000,23.6000000,28.7000000,1,155,1

.3733930,11.6000000,23.3000000,0,156,1

7.6000000,23.5000000,31.4000000,1,157,1

2.6400000,21.7000000,26,1,158,2

.5500000,23.6000000,39.4000000,1,159,1

.3048580,15.7000000,28.7000000,1,160,2

.8000000,19.8000000,25.5000000,1,161,2

.3991660,17.7000000,23.6000000,0,162,3

.5350740,22,29.3000000,1,163,2

.8480000,22.1000000,28.9000000,1,163,2

.7725000,19.8000000,32.6000000,0,164,2

.4400000,23.8000000,39.6000000,1,165,1

1.2125000,26,39.6000000,1,166,2

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1.6525000,25.5000000,32,1,168,2

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2.3595000,25.7000000,31.9000000,1,169,1

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2.2025000,25.6000000,36.2000000,1,171,1

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1.9857000,28.7000000,39.4000000,1,173,2

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1.4635000,28.9000000,36.3000000,1,177,1

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2.7525000,23.6000000,28.7000000,1,199,1

5.8300000,35,36.2000000,1,200,2

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1.2675000,25.6000000,39.4000000,1,202,1

1.0145000,28.3000000,36,1,203,1

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4.1825000,25,35.4000000,1,205,1

2,32,26,1,206,2

2.5287400,31.9000000,26,1,207,2

3.0800000,25.6000000,36.2000000,1,208,1

14.3000000,32.3000000,36.5000000,1,209,1

14.5807500,32.1000000,36.6000000,1,210,2

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6.8750000,35,45.5000000,1,216,2

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3.0250000,24,39.5000000,1,230,2

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8.3657500,39.4000000,32,1,271,2

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3.3000000,51.2000000,59.1000000,1,286,1

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7.6884830,59.1000000,78.7000000,0,288,2

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10.4240000,36.2000000,35,1,290,2

4.1515000,66.5000000,48.5000000,0,291,1

18.7095000,35,36.3000000,1,292,2

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22.5525000,35.2000000,36.5000000,1,295,1

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11.5500000,39.5000000,39.5000000,1,297,1

10.3548150,36.2000000,35,1,298,2

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4.4000000,51.3000000,77.8000000,0,300,1

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6.1724200,36.2000000,28.7000000,1,306,2

6.6301570,36.6000000,29.3000000,1,307,1

4.0395000,36.4000000,25.6000000,1,308,1

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2.1507300,25.8000000,36.6000000,1,312,2

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7.4726000,39.4000000,25.6000000,1,313,2

4.8212980,28.7000000,36.2000000,1,314,1

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.4400000,24,32.5000000,1,318,1

1.6557500,25.5000000,31.8000000,0,319,2

5.9425000,36.2000000,35,1,320,1

2.8600000,35,36.2000000,1,321,2

.4407180,18.3000000,15,1,322,2

.3520000,16.1000000,13.4000000,1,323,2

.2310000,15.9000000,13,1,324,2

.7175000,22.1000000,14.9000000,1,325,1

.3985000,17.8000000,15,1,326,2

1.7995000,23.2000000,27.5000000,1,327,1

4.2002110,28.7000000,36.2000000,1,328,1

.2530000,25.6000000,31.9000000,0,329,1

1.6500000,23.2000000,31.7000000,1,330,1

.7175000,25.5000000,36.3000000,1,331,1

1.7600000,35.4000000,36.2000000,1,332,1

1.2911600,35.4000000,36.2000000,1,333,2

1.4300000,25.5000000,32,1,334,1

1.0475000,44.1000000,34,0,336,1

1.4300000,40.2000000,29.5000000,0,337,2

.5390000,31.9000000,25.6000000,1,338,2

3.1146800,25.8000000,32.1000000,1,339,2

.3105000,16.7000000,23.3000000,1,340,1

.5165160,18.9000000,29.1000000,1,341,2

.9792280,18.9000000,29.1000000,1,341,2

6.6000000,25.5000000,36.2000000,1,342,2

2.5000000,23.6000000,28.7000000,1,343,3

.1985700,19.7000000,24,0,344,2

11.4096000,28.3000000,35.8000000,1,345,2

1.3255640,28.7000000,36.2000000,1,346,2

1.7625000,19.7000000,25.6000000,1,347,1

.6145000,24.4000000,29.5000000,1,348,2

1.1575170,28.7000000,21.3000000,1,349,2

.3991660,22,18.1000000,0,350,3

.8475000,25.7000000,39.4000000,1,351,1

6.9024400,19.7000000,27.6000000,1,352,1

1.0510000,23.7000000,29,1,353,1

.1826360,14.5000000,11.6000000,0,354,1

1.3000000,23.6000000,32.1000000,1,355,1

4.5100000,25.6000000,31.9000000,1,356,2

.6795000,29,36.5000000,1,357,1

.8418660,23.6000000,28.7000000,1,358,2

.2060150,28.7000000,28.7000000,0,359,3

1.4325000,39.5000000,32,1,360,2

.7920000,19.8000000,14.8000000,1,361,2

7.5396580,23.6000000,39.4000000,1,362,1

4.9992730,35.4000000,36.5000000,1,363,2

2.2025000,28.9000000,37,1,364,2

4.0760000,23.8000000,31.3000000,1,365,1

1.1060000,17.2000000,25.9000000,1,366,1

.3745980,7.1000000,15.1000000,1,367,1

.6625000,20.5000000,25.2000000,1,368,1

3.7425000,25.5000000,31.5000000,1,369,2

.5445540,21.3000000,28.7000000,1,370,2

4.5125000,21.3000000,28.9000000,1,371,2

5.5025000,55.1000000,59.1000000,0,372,1

1.8700000,24,31.9000000,1,373,2

3.8500000,25.6000000,39.4000000,1,374,1

5.2825000,25.6000000,39.4000000,1,375,1

9.3525000,25.9000000,39.6000000,1,375,2

8.2525000,25.6000000,39.6000000,1,375,1

3.4100000,25.6000000,39.4000000,1,386,2

1.5425000,25.7000000,32,1,387,1

10:39 AM Sat 23 Jan 2% 4 Back Assignments INFO 5505 Section 002 - Applied Machine Learni...