In this mini-project you will use logistic regression to determine whether you would have survived the Titanic sinking. To find out, we will use the titanic dataset (titanic_data.csv), containing the following information of 887 passengers: 1) whether they survived or not (1 = survived, 0-deceased), 2) pasenger class, 3) gender (0 male,1 female), 4) age, 5) number of siblings/spouses aboard, 6) number of parents/children aboard, and 7) fare: Passenger 1 Passenger 2 Passenger 3Passenger 887 Survived Passenger Class Gender Age Siblings/Sp Parents/Children 38 7.25 71.2833 7.925 7.75 Our goal is to construct a classifier that determines/predicts whether an individual would survive or not. Let y e (0, 1) be the label indicating whether the ith individual survived, and let x R6 denote the feature vector of the lth individual containing all remaining variables). For example, 0 and Xi = 3 0 22 1 0 7.25]. Our goal is In this mini-project we will use logistic regression, whose classifier has the form: truct a classifier that given x determines y. and is by the coefficient vector E R6, which we air, to find by maximizing: (2.2) which is simply an other way to write (2.1) for N training samples In this mini-project you will use logistic regression to determine whether you would have survived the Titanic sinking. To find out, we will use the titanic dataset (titanic_data.csv), containing the following information of 887 passengers: 1) whether they survived or not (1 = survived, 0-deceased), 2) pasenger class, 3) gender (0 male,1 female), 4) age, 5) number of siblings/spouses aboard, 6) number of parents/children aboard, and 7) fare: Passenger 1 Passenger 2 Passenger 3Passenger 887 Survived Passenger Class Gender Age Siblings/Sp Parents/Children 38 7.25 71.2833 7.925 7.75 Our goal is to construct a classifier that determines/predicts whether an individual would survive or not. Let y e (0, 1) be the label indicating whether the ith individual survived, and let x R6 denote the feature vector of the lth individual containing all remaining variables). For example, 0 and Xi = 3 0 22 1 0 7.25]. Our goal is In this mini-project we will use logistic regression, whose classifier has the form: truct a classifier that given x determines y. and is by the coefficient vector E R6, which we air, to find by maximizing: (2.2) which is simply an other way to write (2.1) for N training samples