NEED TO USE PANDAS AND SCIKIT-LEARN LIBRARIES!!

Case Study: You are asked to help a real estate agent predict prices for home in the USA. It would be great if you could somehow create a model that allows them to put in a few features of a house and return an estimate of what the house would sell for. You are given information about US homes in the dataset: assignment1_usa_housing.csv (note that this is fabricated dataset). Assignment Tasks: The goal is to build a predictive model using housing dataset and evaluate your models.

3. Derive/calculate the p-value, coefficient for the independent variables and explain its significance- 20 points 4. Explain the metrics that were used to analyze model performance - 15 points

please use assignment1_usa_housing.csv as a input directory no real data is currenlty needed just need the python code.

Avg. Area Income	Avg. Area House Age	Avg. Area Number of Rooms	Avg. Area Number of Bedrooms	Area Population	Price	Address
79545.45857	5.682861322	7.009188143	4.09	23086.8005	1059033.558	208 Michael Ferry Apt. 674 Laurabury, NE 37010-5101
79248.64245	6.002899808	6.730821019	3.09	40173.07217	1505890.915	188 Johnson Views Suite 079 Lake Kathleen, CA 48958
61287.06718	5.86588984	8.51272743	5.13	36882.1594	1058987.988	9127 Elizabeth Stravenue Danieltown, WI 06482-3489
63345.24005	7.188236095	5.586728665	3.26	34310.24283	1260616.807	USS Barnett FPO AP 44820
59982.19723	5.040554523	7.839387785	4.23	26354.10947	630943.4893	USNS Raymond FPO AE 09386
80175.75416	4.988407758	6.104512439	4.04	26748.42842	1068138.074	06039 Jennifer Islands Apt. 443 Tracyport, KS 16077
64698.46343	6.025335907	8.147759585	3.41	60828.24909	1502055.817	4759 Daniel Shoals Suite 442 Nguyenburgh, CO 20247