Problem 2: Logistic Regression and LDA You are hired by Government to do analysis on 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 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. 2.1 Data Ingestion: Read the dataset. Do the descriptive statistics and do null value condition check, write an inference on it. Perform Univariate and Bivariate Analysis. Do exploratory data analysis. (8 marks) 2.2 Encode the data (having string values) for Modelling. Data Split: Split the data into train and test (70:30). Apply Logistic Regression and LDA (linear discriminant analysis). (8 marks) 2.3 Performance Metrics: Check the performance of Predictions on Train and Test sets using Accuracy, Confusion Matrix, Plot ROC curve and get ROC_AUC score for each model. Compare both the models and write inferences, which model is best/optimized. (8 marks) 2.4 Inference: Based on these predictions, what are the insights and recommendations. (6 marks) 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) for further information go to this link: https://en.wikipedia.org/wiki/Inverse_probability_weighting 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 . 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