Please find below the Predictive Modelling project instructions: You have to submit 2 files : Answer Report: In this, you need to submit all the answers to all the questions in a sequential manner. Your answer should include detailed explanations & inferences to all the questions. Your report should not be filled with codes. You will be evaluated based on the business report. Note: In the business report, there should be a proper interpretation of all the tasks performed along with actionable insights. Only the presence of interpretation of the models is not sufficient to be eligible for full marks in each of the criteria mentioned in the rubric. Marks will be deducted wherever inferences are not clearly mentioned. THE REPORT HAS TO BE STRICTLY SUBMITTED IN A PDF/DOC FORMAT. ANY OTHER FORMAT WILL NOT BE CONSIDERED FOR GRADING. 6 Marks are allotted for the "Quality of Business Report". Problem 1: Linear Regression You are hired by a company Gem Stones co ltd, which is a cubic zirconia manufacturer. You are provided with the dataset containing the prices and other attributes of almost 27,000 cubic zirconia (which is an inexpensive diamond alternative with many of the same qualities as a diamond). The company is earning different profits on different prize slots. You have to help the company in predicting the price for the stone on the bases of the details given in the dataset so it can distinguish between higher profitable stones and lower profitable stones so as to have better profit share. Also, provide them with the best 5 attributes that are most important. Data Dictionary: Variable Name Description Carat Carat weight of the cubic zirconia. Cut Describe the cut quality of the cubic zirconia. Quality is increasing order Fair, Good, Very Good, Premium, Ideal. Color Colour of the cubic zirconia.With D being the best and J the worst. Clarity Clarity refers to the absence of the Inclusions and Blemishes. (In order from Best to Worst in terms of avg price) IF, VVS1, VVS2, VS1, VS2, Sl1, Sl2, l1 Depth The Height of cubic zirconia, measured from the Culet to the table, divided by its average Girdle Diameter. Table The Width of the cubic zirconia's Table expressed as a Percentage of its Average Diameter. Price the Price of the cubic zirconia. X Length of the cubic zirconia in mm. Y Width of the cubic zirconia in mm. Z Height of the cubic zirconia in mm. Dataset for Problem 1: cubic_zirconia.csv Predictive modelling project 1.1. Read the data and do exploratory data analysis. Describe the data briefly. (Check the null values, Data types, shape, EDA, duplicate values). Perform Univariate and Bivariate Analysis. 1.2 Impute null values if present, also check for the values which are equal to zero. Do they have any meaning or do we need to change them or drop them? Check for the possibility of combining the sub levels of a ordinal variables and take actions accordingly. Explain why you are combining these sub levels with appropriate reasoning. 1.3 Encode the data (having string values) for Modelling. Split the data into train and test (70:30). Apply Linear regression using scikit learn. Perform checks for significant variables using appropriate method from statsmodel. Create multiple models and check the performance of Predictions on Train and Test sets using Rsquare, RMSE & Adj Rsquare. Compare these models and select the best one with appropriate reasoning. 1.4 Inference: Basis on these predictions, what are the business insights and recommendations. Please explain and summarise the various steps performed in this project. There should be proper business interpretation and actionable insights present.

Dataset for Problem 1: cubic_zirconia.csv (some attached below)

	carat	cut	color	clarity	depth	table	x	y	z	price
1	0.3	Ideal	E	SI1	62.1	58	4.27	4.29	2.66	499
2	0.33	Premium	G	IF	60.8	58	4.42	4.46	2.7	984
3	0.9	Very Good	E	VVS2	62.2	60	6.04	6.12	3.78	6289
4	0.42	Ideal	F	VS1	61.6	56	4.82	4.8	2.96	1082
5	0.31	Ideal	F	VVS1	60.4	59	4.35	4.43	2.65	779
6	1.02	Ideal	D	VS2	61.5	56	6.46	6.49	3.99	9502
7	1.01	Good	H	SI1	63.7	60	6.35	6.3	4.03	4836
8	0.5	Premium	E	SI1	61.5	62	5.09	5.06	3.12	1415
9	1.21	Good	H	SI1	63.8	64	6.72	6.63	4.26	5407
10	0.35	Ideal	F	VS2	60.5	57	4.52	4.6	2.76	706