There is a bike rent store. Given excel file gives number of rentals based on given features. There are 3 features in the dataset and 732 samples. The features are temperature, humidity, and windspeed. In not cold and not hot temperatures, low humidity, and low windspeed, it is expected to have more bike rentals.

Requirements:

In this assignment will develop a linear regression model with multiple features. You can use scikit-learn API. Make sure to split your data in training and test sets (randomly; in order not to have bias). Here is a scikit-learn example on how to split your data in training and test sets:

from sklearn.model_selection import train_test_split

x_train, x_test, y_train, y_test = train_test_split(x, y, random_state = 1234)

Visualize your data using scatter plot from matplotlib such as

bikerentals.plot(kind = 'scatter', x = 'temperature', y = 'rentals')

Visualize temperature in x axis, and rentals on y axis

Visualize humidity in x axis, and rentals on y axis

Visualize windspeed in x axis and rentals in y axis.

From these visualizations, explain if you see any correlation between individual features and rentals. Clearly explain for each feature.

After training your linear regression model on training dataset, test it on test dataset.

Report your results and accuracy of your results

From results you obtained from scikit linear regression model, write the final hypothesis function by obtaining coefficients (parameters; Thetas mentioned in class). Note: In scikit-learn your model's intercept_ variable is Theta0, coef_ variable is a list of other Theta parameters (coefficients) e.g. Theta 1, Theta 2, ...etc. Based on these obtained values clearly write the Hypothesis function.

Deliverables:

Your Python code. Name your file: YourFullNAme_HW3_CPSC4350.py

Word or pdf document of your answers to questions including 3 visualization plots.

Name file: YourFullNAme_HW3_CPSC4350.docx or

YourFullNAme_HW3_CPSC4350.pdf

CPSC 4370 Artificial Intelligence

Please answer as soon as possible and correctly.

basic steps are upload csv as data frame, use the function he gave us from sci kit to split the data into train and test sets, then fit the sci kit learn linear regression model to the training data, then score it with the score function and provide the accuracy/coef/intercepts

The bike csv file:

temperature	humidity	windspeed	rentals
46.7165282	0.8159692	13.6696631	985
48.3502388	0.80049727	15.1997823	801
34.2123944	0.59209698	13.2475582	1349
34.52	0.62319613	11.6879633	1562
36.8005622	0.62464313	13.1482806	1600
34.8878408	0.66519592	12.185233	1606
34.2257612	0.62151557	12.4281967	1510
31.559	0.60276607	12.9317391	959
29.3029718	0.5384484	13.5800833	822
30.3604718	0.57283205	12.2688203	1321
31.9050986	0.65803418	11.1894099	1263
32.2127042	0.56059373	12.1166019	1162
31.559	0.52601634	12.3854956	1406
31.209602	0.62874985	11.7910542	1421
37.3399718	0.69168325	13.6018819	1248
37.1990282	0.67687759	13.7874552	1204
32.4754718	0.65186172	12.9935467	1000
35.9300282	0.75484462	11.9722567	683
42.3179204	0.65996046	11.8048736	1650
39.7370282	0.60758215	12.1903301	1927
32.6165	0.48712683	12.3628894	1543
22.6024318	0.56981912	12.2354367	981
25.7657358	0.55438512	12.5362377	986
25.839304	0.55634385	11.1553073	1416
36.5062388	0.60923523	10.8854242	1985
36.0005	0.68643815	12.3858887	506
34.097	0.78836857	13.4958513	431
34.8142388	0.69813878	11.1596613	1167
34.2257612	0.68720248	12.2494892	1098
35.9177612	0.74847609	12.0467512	1096
32.8984718	0.59801211	11.4324512	1501
33.8579204	0.70806574	10.2718255	1360
39.596	0.61365489	11.34231	1526
33.4165622	0.54096722	12.6353634	1550
35.4763184	0.6346517	11.5663961	1708
37.3399718	0.7199601	10.994626	1005
41.7814718	0.69919419	13.0438374	1623
40.5830282	0.73593953	11.3688245	1712
36.2824718	0.51976001	12.4714527	1530
29.0026418	0.54536912	11.2794431	1605
29.8118408	0.54123293	12.0041642	1538
33.5970986	0.62321201	11.7139256	1746
36.4235	0.62660872	12.5846894	1472
44.3777612	0.65543901	14.4147632	1589
52.709	0.60063832	15.7999179	1913
40.1109602	0.5660776	14.163857	1815
44.5248806	0.59714588	13.482255	2115
54.4714718	0.66462167	13.8818417	2475
61.7330282	0.65898719	14.0906809	2927
51.3695282	0.56641524	17.5750343	1635
41.7293582	0.62473529	13.7804169	1812
43.2619718	0.67518132	14.2320055	1107
33.0159812	0.60025906	11.7622766	1450
36.3591194	0.63833171	12.4409585	1917
42.6121592	0.6172376	11.8242439	1807
48.4238408	0.65847581	13.5780314	1461
41.4995	0.62564812	12.4205214	1969
46.6582388	0.66104841	11.3276389	2402
52.0552958	0.73162811	13.0717596	1446
40.1600282	0.60851879	12.467204	1851
45.941	0.57413277	13.5066395	2134
34.3789718	0.56595584	13.3807033	1685
39.7370282	0.60085877	11.621071	1944
50.1005282	0.65719909	11.9038337	2077
49.4537612	0.77712846	13.9981532	605
39.7431194	0.5243784	12.2224576	1872
42.3455	0.65450049	13.0601115	2133
42.6274718	0.62574628	11.0600997	1891
50.5170986	0.83588367	21.1266271	623
44.3777612	0.62449843	12.1095207	1977
45.4475282	0.62341025	12.3541219	2132
50.1158408	0.60348146	13.0505822	2417
45.1133582	0.68272404	13.2084675	2046
44.4512786	0.64555243	11.8966772	2056
48.4973582	0.6571822	11.438113	2192
52.709	0.6263918	12.2197159	2744
63.284	0.65661258	13.5955172	3239
57.5735	0.53009903	13.9014476	3117
45.7295	0.59215668	12.5154864	2471
54.014801	0.67321081	13.0034786	2077
54.9650282	0.64387852	12.5688967	2703
46.9525622	0.63137188	10.8539209	2121
41.711	0.60523313	10.7278419	1865
39.9485282	0.55293264	11.8974419	2210
40.0894718	0.53188446	12.0076206	2496
39.0074378	0.63661236	12.9208869	1693
39.9638408	0.58718227	13.7368211	2028
43.1915	0.56072636	13.3288804	2425
42.98	0.70265503	12.8877431	1536
40.3009718	0.62979763	10.0551627	1685
42.98	0.56359413	11.0084094	2227
44.249	0.61122656	11.4230328	2252
49.6069718	0.54975792	11.3934278	3249
66.1039718	0.60429354	15.1262993	3115
52.6385282	0.63309258	14.0998876	1795
50.6644718	0.5649972	12.6686495	2808
54.6125	0.62522788	11.6568133	3141
46.0114718	0.7073286	12.1994406	1471
46.5755	0.64878466	9.73799348	2455
53.6960282	0.65633498	10.1814293	2895
67.9921592	0.63193156	12.7993459	3348
60.1115	0.74772353	14.2088594	2034
52.4975	0.67121708	11.9280015	2162
57.1505	0.6667454	12.2929938	3267
55.3880282	0.59510993	11.3311119	3126
54.0484718	0.80068145	14.8460177	795
56.2340282	0.47891514	11.475794	3744
60.9575	0.65166986	12.6044583	3429
60.3934718	0.6775039	12.1108906	3204
67.937	0.6084324	12.1753936	3944
56.4455282	0.41221725	11.0250913	4189
46.0820282	0.68630393	12.5096848	1683
56.516	0.49049724	7.85309777	4036
66.8090282	0.59734351	9.95404324	4191
68.9240282	0.63725725	10.8649533	4073
71.0390282	0.53016569	10.8511473	4400
70.052	0.59485594	11.1019624	3872
69.8405	0.56423938	11.6255003	4058
60.746	0.45723741	10.5139328	4595
57.5735	0.33968744	7.94319861	5312
55.8110282	0.64569163	10.2235647	3351
64.0595282	0.54933215	9.56099029	4401
69.7700282	0.50120679	10.7368683	4451
52.6385282	0.56551445	11.4750902	2633
56.4455282	0.40038703	10.1915239	4433
58.1375282	0.43703358	9.02740743	4608
61.592	0.50016553	9.7767261	4714
62.2969718	0.60768792	10.1168524	4333
62.6495	0.54479294	10.4305975	4362
62.6495	0.52734485	10.1494467	4803
63.4955	0.60986416	10.6748179	4182
62.861	0.47602407	8.14801485	4864
60.9575	0.5576274	8.69097374	4105
61.6624718	0.67888552	9.98494429	3409
65.1875	0.56051899	8.41051491	4553
66.4565	0.66326026	10.5933818	3958
65.1170282	0.5362249	9.60040573	4123
64.13	0.60673083	9.79914316	3855
62.5084718	0.55890664	8.14378392	4575
63.0020282	0.51088789	8.25743109	4917
68.5715	0.4585218	7.92264875	5805
68.7125282	0.58530896	9.67615667	4660
71.0390282	0.60322786	10.5462098	4274
73.436	0.61650684	10.9940751	4492
73.5064718	0.58953731	10.2179063	4978
77.5249718	0.63873763	11.7856007	4677
75.2690282	0.59066095	11.5414011	4679
73.0834718	0.56569129	10.400706	4758
74.0705	0.58177246	9.8571803	4788
79.6399718	0.77410201	13.4496159	4098
83.165	0.83585858	14.71907	3982
82.2485282	0.76842769	14.2718897	3974
78.089	0.5507746	13.9601264	4968
70.052	0.45244865	11.3319133	5312
71.321	0.54184208	10.7451709	5342
72.4489718	0.59761141	10.5109759	4906
74.9869718	0.67772696	12.1860319	4548
77.4545	0.62597631	11.9994506	4833
83.2354718	0.76868383	13.876248	4401
85.9849718	0.8483129	15.8935338	3915
81.473	0.72586626	13.2530266	4586
78.935	0.64456459	11.5488605	4966
76.1855	0.67475621	11.5204449	4460
71.321	0.46899651	11.2033127	5020
68.7125282	0.48146683	10.9288418	4891
70.6160282	0.52724432	10.8746555	5180
70.7569718	0.68040676	12.5545775	3767
72.5195282	0.60333271	10.0535099	4844
76.5380282	0.62369324	10.6405585	5119
76.7495282	0.68319366	11.5688814	4744
71.321	0.68739243	11.6736588	4010
75.1984718	0.61371677	10.3117937	4835
79.6399718	0.69783821	12.3440792	4507
79.2169718	0.61723631	11.6465993	4790
78.8645282	0.59914375	12.0760622	4991
76.397	0.55612121	11.7999171	5202
75.128	0.60197614	11.109486	5305
75.3395	0.67020234	11.4539806	4708
80.5565282	0.7067464	12.7276958	4648
79.2169718	0.54890339	12.1576531	5225
76.5380282	0.53364099	11.4680376	5515
78.7235	0.62560004	12.4999224	5362
80.0629718	0.66139628	12.9489231	5119
78.2300282	0.63112257	11.9426586	4649
79.0760282	0.55753566	9.2219602	6043
80.7680282	0.71789984	13.0492498	4665
78.512	0.68533234	11.5929747	4629
81.05	0.70936833	12.8213812	4592
77.5955282	0.70283483	12.6711637	4040
79.6399718	0.59748062	11.1619543	5336
80.8385	0.65630704	12.6546157	4881
82.1075	0.706492	14.2919685	4086
84.7865282	0.76256803	14.9525138	4258
80.7680282	0.74573966	13.4882352	4342
75.1984718	0.53962238	11.9031244	5084
73.7179718	0.50430791	9.74192862	5538
75.6920282	0.46025364	9.26553353	5923
78.4415282	0.54202938	11.0567906	5302
80.7680282	0.68816042	13.0775852	4458
83.3060282	0.7558326	13.3590289	4541
82.6009718	0.78520702	13.2931317	4332
86.549	0.82551189	15.394135	3784
89.3689718	0.95795569	17.6396568	3387
89.4395282	0.9725	18.4785275	3285
87.818	0.89298221	17.0690764	3606
80.4859718	0.8396136	13.6677862	3840
82.8830282	0.70238877	13.9747297	4590
83.165	0.70738906	14.8892055	4656
83.5175282	0.74704418	14.2383388	4390
88.5229718	0.86707308	16.7082127	3846
85.6325282	0.76364353	15.2956045	4475
85.7734718	0.78885583	15.6087845	4302
82.8830282	0.76963746	14.6458609	4266
83.8699718	0.67702846	13.9345705	4845
79.4990282	0.83514045	14.8303613	3574
77.666	0.65423732	11.4552287	4576
77.7364718	0.62660254	11.7332843	4866
78.2300282	0.67562897	12.2253454	4294
80.4155	0.77718808	13.8118982	3785
82.319	0.7346115	14.1009787	4326
83.165	0.73413679	13.781337	4602
82.4600282	0.67456867	14.3184447	4780
78.3005	0.65373105	13.5300654	4792
77.5249718	0.65624923	13.1922833	4905
75.6214718	0.6780252	12.264219	4150
74.8460282	0.70442018	12.2336476	3820
73.9294718	0.63939831	11.650111	4338
76.8904718	0.60374211	12.2881413	4725
78.7939718	0.68390353	12.7304025	4694
77.8070282	0.72914343	14.0101624	3805
75.551	0.7216449	12.2934114	4153
76.6085	0.62469849	10.4604334	5191
77.7364718	0.71035928	12.9920082	3873
76.1150282	0.5668982	12.8666917	4758
71.8144718	0.46336782	9.57090342	5895
74.5639718	0.51989524	10.7339054	5130
75.4805282	0.75258997	13.3262317	3542
76.82	0.70538503	11.0769213	4661
75.128	0.9487075	18.2635669	1115
77.4171914	0.61524987	13.4054042	4334
71.4620282	0.60852954	11.6750878	4634
71.6735282	0.56134222	10.4498082	5204
73.1540282	0.62001252	10.6925613	5058
73.013	0.57531004	10.232255	5115
72.0259718	0.6148951	10.2961424	4727
74.2115282	0.63868819	11.3326259	4484
77.5955282	0.6025172	10.7190624	4940
74.5639718	0.76607543	13.4589995	3351
63.284	0.65241741	12.1507318	2710
68.2895282	0.94385246	14.4894379	1996
71.2290398	0.93289167	15.217853	1842
72.59	0.77718776	11.8622279	3544
73.436	0.54360307	8.9385317	5345
72.8719718	0.59905262	9.81637928	5046
72.1118408	0.64909139	10.6008491	4713
72.6604718	0.6155605	10.4371807	4763
74.5639718	0.61593773	10.8495729	4785
66.4565	0.61105409	11.8598196	3659
57.2915282	0.44942122	8.51962173	4760
59.1950282	0.48348871	8.50064993	4511
60.5345	0.53318718	9.46297204	4274
64.0595282	0.55182225	9.53801546	4539
65.1170282	0.68435502	10.3895164	3641
67.937	0.64786061	9.13531006	4352
70.7569718	0.60115025	8.61966332	4795
69.1355282	0.91421156	13.3201382	2395
68.9240282	0.53569244	7.13079812	5423
71.2505282	0.62748744	8.63599684	5010
72.5195282	0.64996184	9.71791732	4630
71.4620282	0.69822802	10.4231436	4120
71.321	0.7044372	11.1564947	3907
69.7700282	0.55815161	9.82976004	4839
65.3285282	0.44810369	8.55980928	5202
52.286	0.6101068	11.7566374	2429
47.7740282	0.54804384	9.5252455	2918
50.1005282	0.57600249	8.68071873	3570
58.5605282	0.4740012	8.57183119	4456
63.1429718	0.49124046	9.01958079	4826
59.4065282	0.4879131	8.67282004	4765
60.8164718	0.54429849	7.96026784	4985
61.7330282	0.48643927	7.0284163	5409
63.3544718	0.4786855	6.89867313	5511
65.8924718	0.56597077	8.22435852	5117
65.5400282	0.56893302	8.88131608	4563
63.5659718	0.7511062	12.7363418	2416
67.4435282	0.79372673	12.3766035	2913
64.2004718	0.61978937	11.427897	3644
60.4640282	0.36595355	8.83947261	5217
60.8870282	0.37739268	9.2971502	5041
62.7905282	0.52072206	10.0458529	4570
62.6495	0.53764099	8.71289629	4748
63.4311194	0.75175263	12.774339	2424
57.8554718	0.36537587	9.58467595	4195
53.7665	0.43565259	9.03986943	4304
53.3435	0.51069844	8.54846363	4308
53.2730282	0.49668313	7.54335498	4381
56.7979718	0.54386677	8.40979367	4187
57.5030282	0.45906328	8.48720415	4687
58.5605282	0.5793043	9.78691511	3894
57.362	0.69049279	11.7249932	2659
45.5884718	0.41865894	8.73244243	3747
39.1025282	0.66657426	12.5303434	627
44.6015282	0.49331556	9.22844921	3331
46.364	0.51233305	8.18991131	3669
51.5104718	0.49574836	8.36199421	4068
49.5365	0.49460283	7.47084877	4186
52.1449718	0.5129636	8.55972375	3974
51.7219718	0.41620652	8.90190039	4046
45.2360282	0.4179443	8.74664109	3926
47.0689718	0.5306771	8.13549462	3649
51.017	0.54348256	7.79558534	4035
52.1449718	0.52623582	7.88475015	4205
51.44	0.53540576	7.70625136	4109
49.748	0.58619447	9.69391831	2933
45.0245282	0.40739415	10.5266224	3368
47.7740282	0.41137699	8.64041542	4067
54.8944718	0.48245566	11.4481478	3717
62.438	0.4463771	10.1190162	4486
62.438	0.54881186	10.0400785	4195
56.2340282	0.82170478	12.5919455	1817
46.5050282	0.46702547	10.5694035	3053
40.7945282	0.45292826	9.96127662	3392
45.4475282	0.43078785	9.51561613	3663
56.7979718	0.58517835	10.5864009	3520
55.4585	0.69539805	10.4177424	2765
52.8500282	0.82537225	12.2141229	1607
54.8944718	0.59201949	11.8901334	2566
49.1839718	0.77283057	14.8384136	1495
49.325	0.65537942	11.1876153	2792
49.3954718	0.6406485	10.2873138	3068
56.4455282	0.62324221	11.4525272	3071
60.1942388	0.60292612	9.98257355	3867
56.3749718	0.61161824	10.8258661	2914
45.095	0.404596	8.7475403	3613
44.0375	0.41151452	8.94408732	3727
44.1785282	0.46040507	7.73572068	3940
42.9095282	0.49140499	8.34695643	3614
45.5884718	0.54137423	7.93715151	3485
50.2414718	0.560538	7.66541041	3811
56.7275	0.67070076	10.7526738	2594
52.286	0.84059708	14.125472	705
40.0894718	0.41031919	8.74231656	3322
42.2044718	0.49175072	7.59383086	3620
40.865	0.43866612	9.72137154	3190
36.2824718	0.55197076	10.0359257	2743
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50.1005282	0.57228537	8.40334004	3228
54.4714718	0.35426105	4.59849756	5170
47.4919718	0.17829585	4.97092239	5501
42.7685	0.23645716	4.95085558	5319
42.6274718	0.20164542	4.82154078	5532
41.4290282	0.20649712	3.26451067	5611
45.0245282	0.32773098	5.16593237	5047
48.2675	0.52016902	7.27455348	3786
50.8759718	0.44829272	5.41455856	4585
52.3564718	0.26588342	5.22374894	5557
45.7295	0.25922581	4.97362445	5267
45.518	0.43069609	7.240072	4128
45.2360282	0.34080844	9.16724012	3623
40.0894718	0.50222694	13.3782262	1749
38.3974718	0.65059653	12.434657	1787
37.1683184	0.78076349	12.172466	920
42.2443184	0.76293996	13.3066018	1013
38.1859718	0.70216543	13.224708	441
39.1025282	0.48249344	10.801229	2114
39.0319718	0.48043341	8.99630095	3095
39.0319718	0.71772989	11.8294252	1341
39.2434718	0.52303895	12.8053145	1796
35.8594718	0.4948075	9.34684984	2729