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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

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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 Rsquare, RMSE. (8 marks) 1.4 Inference: Based on these predictions, what are the busine ss insights and recommendations? (6 marks) 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 meas ures. 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 A nalysis. 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 Inferen ce: Based on these predictions, what are the insights and recommendations. (6 marks)