Problem 1: Linear Regression Problem Statement: You are a part of an investing firm and your work is to do research about these 759 firms. You are provided with the dataset containing the sales and other attributes of these 759 firms. Predict the sales of these firms on the bases of the details given in the dataset so as to help your company in investing consciously. Also, provide them with 5 attributes that are most important. 1.1 Read the data and do exploratory data analysis. Describe the data briefly. (Check the null values, data types, shape, EDA). Perform Univariate and Bivariate Analysis. (8 marks) 1.2 Impute null values if present? Do you think scaling is necessary in this case? (8 marks) 1.3 Encode the data (having string values) for Modelling. Data Split: Split the data into test and train (70:30). Apply Linear regression. Performance Metrics: Check the performance of Predictions on Train and Test sets using quuare, RMSE. (8 marks) 1.4 Inference: Based on these predictions, what are the business insights and recommendations. (6 marks) Data Dictionary for Firm_level_data= sales: Sales (in millions of dollars). capital: Net stock of property, plant. and equipment. patents: Granted patents. randdi R&D stock (in millions of dollars). employment: Employment (in 10003). sp500= Membership of firms in the S&P 500 index. 8&P. is a stock market index that measures the stock performance of 500 large companies listed on stock exchanges in the United States 7. tobinq: Tobin's q (also known as q ratio and Kaldor's v) is the ratio between a physical asset's market value and its replacement value. value: Stock market value. institutions: Proportion of stock owned by institutions. ewswwe 599 Drnnrinfnru manhunt. Grpaf learning. All Right: Rpcnrvpd. llnnllthnrhpd mu: nr dictrihu'l'inn nrnhihifpd