Business analytics programming. R programming 1. Import HouseData.csv with stringsAsFactors = FALSE to a data frame named house. Then, factor() Parking and City_Category vectors.
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- Programming in R 1. Import HouseData.csv with stringsAsFactors = FALSE to a data frame named house, then use factor() to encode Parking and City_Category vectors as factors.web design Construct a Movie class in Model architecture. Subsequently, write a method called queryMovie() to query and return all movie records that are active from movie table belowcreate multiple graphs with single_call to plot, these statements plot three related functions of x: t1= 2sinc(x), t2= sinc(x), and t3= 0.5sinc(x), in the interval Osxs2n. The graphs must be contain desired title, axis labels, and annotations, with symbols for color, style, and marker respectively.
- javascript only: You have been assigned to work with an undersea explorer who is attempting to identify and map undersea trenches. The explorer has provided you with several data sets. Depending on the scan, the provided matrix may be larger or smaller, but it will always be rectangular. Your task is to determine if a given data set contains a trench by comparing each node and their neighbors and determining if there is a pattern that matches the defined properties of a trench. Neighbors are considered to be nodes that are directly above, below, or to the side. No diagonals! A trench has the following three properties: It has a length of three or more nodes that are neighbors. Each node in the trench must be deeper than -5. Trenches may not branch into (any form of) a "T" shape. A node with more than two neighbors will result in branching "T" shape. // Example 1 sonar = [ [-5,-5,-5,-5,-5], [-5,-8,-8,-9,-7], [-5,-5,-5,-5,-8], [-5,-5,-5,-5,-5] ]Experiment No. 1: Write a program in Python Visualize the 'penguin' dataset in seaborn library by plotting:A. SwarmplotB. BoxplotC. SubplotCreate a matrix with this template in java.
- Remove first and last element from likedlist JAVACreate a matrix calculator with this template.Fill the blank : 1- From the output given below identify...................for Business Department Engineering Department© Electrical Section© Electronics Section© Mechanical Section TT Department© Software Engineering© Database© Network Security Business Department1, Account Department2. Finance Department3, Information System Department General Departments1, Admin2, Registration html element is used
- Interesting, Intersecting def squares_intersect (s1, s2): An axis-aligned square on the two-dimensional plane can be defined as a tuple (x, y, r) where (x, y) are the coordinates of its bottom left corner and r is the length of the side of the square. Given two squares as tuples (x1, yl, r1) and (x2, y2, r2), this function should determine whether these two squares intersect by having at least one point in common, even if that one point is the shared corner point of two squares placed kitty corner (The intersection of two squares can have zero area, if the intersection consists of parts of the one-dimensional edges.) This function should not contain any loops or list comprehensions of any kind, but should compute the result using only integer comparisons and conditional statements. This problem showcases an idea that comes up with some problems of this nature; it is actually far easier to determine that the two axis-aligned squares do not intersect, and negate that answer! Two squares…Please fill out the remaining codes in #TO-DO for Python: import numpy as npimport pandas as pd from sklearn.datasets import load_bostonfrom sklearn.model_selection import train_test_splitfrom sklearn.metrics import mean_squared_errorfrom sklearn.linear_model import LinearRegression class MyLinearRegression:theta = None def fit(self, X, y, option, alpha, epoch):X = np.concatenate((np.array(X), np.ones((X.shape[0], 1), dtype=np.float64)), axis=1)y = np.array(y)if option.lower() in ['bgd', 'gd']:# Run batch gradient descent.self.theta = self.batchGradientDescent(X, y, alpha, epoch)elif option.lower() in ['sgd']:# Run stochastic gradient descent.self.theta = self.stocGradientDescent(X, y, alpha, epoch)else:# Run solving the normal equation.self.theta = self.normalEquation(X, y)def predict(self, X):X = np.concatenate((np.array(X), np.ones((X.shape[0], 1), dtype=np.float64)), axis=1)if isinstance(self.theta, np.ndarray):# TO-DO: Implement predict(). return y_predreturn None def…Different approaches to express patterns exist. Please discuss and explain each of the following five methods of depicting patterns: Provide concrete instances to back up your argument.