Problem 1:Linear Regression You are a part of an investment 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.

Data Dictionary for Firm_level_data:

1. sales: Sales (in millions of dollars). 2. capital: Net stock of property, plant, and equipment. 3. patents: Granted patents. 4. randd: R&D stock (in millions of dollars). 5. employment: Employment (in 1000s). 6. sp500: Membership of firms in the S&P 500 index. S&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. 8. value: Stock market value. 9. institutions: Proportion of stock owned by institutions.

Problem 2: Logistic Regression and Linear Discriminant Analysis

You are hired by the Government to do an analysis of car crashes. You are provided details of car crashes, among which some people survived and some didn't. You have to help the government in predicting whether a person will survive or not on the basis of the information given in the data set so as to provide insights that will help the government to make stronger laws for car manufacturers to ensure safety measures. Also, find out the important factors on the basis of which you made your predictions.

Data Dictionary for Car_Crash 1. dvcat: factor with levels (estimated impact speeds) 1-9km/h, 10-24, 25-39, 40-54, 55+ 2. weight: Observation weights, albeit of uncertain accuracy, designed to account for varying sampling probabilities. (The inverse probability weighting estimator can be used to demonstrate causality when the researcher cannot conduct a controlled experiment but has observed data to model) 3. Survived: factor with levels Survived or not_survived 4. airbag: a factor with levels none or airbag 5. seatbelt: a factor with levels none or belted 6. frontal: a numeric vector; 0 = non-frontal, 1=frontal impact 7. sex: a factor with levels f: Female or m: Male 8. ageOFocc: age of occupant in years 9. yearacc: year of accident 10. yearVeh: Year of model of vehicle; a numeric vector 11. abcat: Did one or more (driver or passenger) airbag(s) deploy? This factor has levels deploy, nodeploy and unavail 12. occRole: a factor with levels driver or pass: passenger 13. deploy: a numeric vector: 0 if an airbag was unavailable or did not deploy; 1 if one or more bags deployed. 14. injSeverity: a numeric vector; 0: none, 1: possible injury, 2: no incapacity, 3: incapacity, 4: killed; 5: unknown, 6: prior death 15. caseid: character, created by pasting together the population's sampling unit, the case number, and the vehicle number. Within each year, use this to uniquely identify the vehicle.

Please help me with the Python code, I'm attaching the questions and data file in drive

Drive link: https://drive.google.com/drive/u/3/folders/1fVeHGHbpJBk4ucVUcSqzER2fAjQrb5d2