Write a computer code to do linear regression analysis of a given data set to find the relation between two variables which gives the least sum of squares error. Using Excel or Matlab. Include a copy of the script, a sample input, and a sample output from your codes. Sample output must include model fit parameters and the sum of the squared errors for the fit. Please also run the code for the following data set to find and report the relation between y and x: x y 25 30 40 80 120 150 75 80 150 200 300 350 270 240 400 320 450 470 575 583

Please provide steps to answer this question: Your task is simple: use machine learning to create a model that predicts which passengers survived the Titanic shipwreck. Implement and train a classification model for the Titanic dataset (the dataset can be found here: https://www.kaggle.com/c/titanic). Please ignore the test set (i.e., test.csv) and consider the given train set (i.e., train.csv) as the dataset. What you need to do: 1. Data cleansing 2. Split the dataset (i.e., train.csv) into a training set (80% samples) and a testing set (20% samples) 3. Train your model (see details below) 4. Report the overall classification accuracies on the training and testing sets 5. Report the precision, recall, and F-measure scores on the testing set

Question Study the dataset given in file ‘x03.csv’, read it in as a data frame. Use the linear regression method to find a possible linear relation between blood pressure and age. First plot the given sample points, then plot the linear model graph as only 2 variables; blood pressure and age are in the dataset. Finally, plot the residuals points and counts of the residuals, and evaluate the normality assumption for the dataset. x03.csv x03.csv Index Systolic age bp 1 1 39 144 2 1 47 220 3 1 45 138 4 1 47 145 5 1 65 162 6 1 46 142 7 1 67 170 8 1 42 124 9 1 67 158 10 1 56 154 11 1 64 162 12 1 56 150 13 1 59 140 14 1 34 110 15 1 42 128 16 1 48 130 17 1 45 135 18 1 17 114 19 1 20 116 20 1 19 124 21 1 36 136 22 1 50 142 23 1 39 120 24 1 21 120 25 1 44 160 26 1 53 158 27 1 63 144 28 1 29 130 29 1 25 125 30 1 69 175

Write a scikit-learn based application to predict the secondary school student performance using a logistic regression model. The dataset is present in file student.cleaned.data.csv. The features to be taken into account are traveltime, studytime, failures, famrel, freetime, gout, health. The target should be G3. In G3 column, assume the values less than 10 to be 0, and the values equal to or more than 10 to be 1. Evaluate the accuracy of the model.

Assume the following simple regression model, Y = β0 + β1X + ε ε ∼ N(0, σ^2 ) Now run the following R-code to generate values of σ^2 = sig2, β1 = beta1 and β0 = beta0. Simulate the parameters using the following codes: Code: # Simulation ## set.seed("12345") beta0 <- rnorm(1, mean = 0, sd = 1) ## The true beta0 beta1 <- runif(n = 1, min = 1, max = 3) ## The true beta1 sig2 <- rchisq(n = 1, df = 25) ## The true value of the error variance sigmaˆ2 ## Multiple simulation will require loops ## nsample <- 10 ## Sample size n.sim <- 100 ## The number of simulations sigX <- 0.2 ## The variances of X # # Simulate the predictor variable ## X <- rnorm(nsample, mean = 0, sd = sqrt(sigX)) Q1 Fix the sample size nsample = 10 . Here, the values of X are fixed. You just need to generate ε and Y . Execute 100 simulations (i.e., n.sim = 100). For each simulation, estimate the regression coefficients (β0, β1) and the error variance (σ 2 ). Calculate the mean of...

You have four various machine learning algorithms to pick from for supervised learning on a dataset. Describe any four factors you would use to help you decide which one to employ for a certain task.