Software for business analytics. Coding in R Create a data frame called house and load HouseData.csv into it with stringsAsFactors set to False. The vectors Parking and City_Category should then be factored().
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Software for business analytics. Coding in R
Create a data frame called house and load HouseData.csv into it with stringsAsFactors set to False. The
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- Software for business analytics. Coding in RCreate a data frame called house and load HouseData.csv into it with stringsAsFactors set to False. The vectors Parking and City_Category should then be factored().The length of vectors in Word2Vec model is 2|V], where |V| represents the length of unique vocabulary. True FalseTODO 7 To start off, we can plot each feature against every other feature just to see if there are any trends between features. We can easily do so by using Pandas plotting. WARNING: Plotting the scatter matrix can take awhile Use Panda's scatter_matrix() function (docs) to plot a scatter matrix. Pass forestfire_df as input and figsize=(15, 15) as an additional argument. # TODO 7.1 plt.show()
- Examine the R expressionpairs(iris[,1:4], main = ‘‘Iris Data’’, pch = 20,col = unclass(iris$Species) + 2).Use R to create a similar expression to produce a scatter plot matrix of the variables mpg, disp, hp, drat,and qsec in the data frame mtcars. Use different colors to identify cars belonging to each of thecategories defined by the carsize variable.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] ]Step 3. Evaluate Model To assess the quality of the model, we will use the mAP metric defined as AP Area under the curve. To do this, you will need to calculate recall andprecision. from sklearn.metrics import auc def evaluate(model, test_loader, device): results = [] model.eval() nbr_boxes = 0 with torch.no_grad(): for batch, (images, targets_true) inenumerate(test_loader): images = list(image.to(device).float() for image in images) targets_pred = model(images) targets_true = [{k: v.cpu().float() for k, v in t.items()} for t in targets_true] targets_pred = [{k: v.cpu().float() for k, v in t.items()} for t in targets_pred] for i inrange(len(targets_true)): target_true = targets_true[i] target_pred = targets_pred[i] nbr_boxes += target_true['labels'].shape[0] results.extend(evaluate_sample(target_pred, target_true)) results = sorted(results, key=lambda k: k['score'], reverse=True) # compute precision…
- In this assignment, you will design the AddNode and AddEdge methods for the supplied graph data structure. The AddNode and AddEdge methods are to support the construction of undirected (bi-directional) graphs. That is if node A is connected to node B then node B is also connected to node A. In addition to the AddNode and AddEdge methods, create a method called BreadthFirstSearch that accepts a starting node and performs a Breadth First Search of the graph. The algorithm for the breadth first traversal is provided below 1. Add a node to the queue (starting node) 2. While the queue is not empty, dequeue a node 3. Add all unvisited nodes of the dequeued node from step 2 and add them to queue 4. End While Demonstrate your methods by creating the graph depicted in Figure 1 below and running the Breadth First Search on the graph using 0 as the starting node. (see image below) You may use C++, C#, to implement this program as long as the following requirements are met. A C++, or C#…Use elements of (0,0,0,0,0,1,1,0,2} and write a SciPy program for CSR Matrix.import numpy as np import pandas as pd from catboost import CatBoostRegressor from lightgbm import LGBMRegressor from sklearn.linear_model import Lasso from sklearn.metrics import mean_squared_error from sklearn.model_selection import train_test_split from xgboost import XGBRegressor df=pd.read_csv('data.csv') X = df.drop('shares', axis=1) y = df['shares'] from sklearn.model_selection import train_test_split X_train, X_test, y_train, y_test = train_test_split( X, y, test_size=0.40, random_state=13) from sklearn.model_selection import train_test_split X_train, X_test, y_train, y_test = train_test_split( X, y, test_size=0.25, random_state=13) Ans:- # code here Q- Now let's train our first model - XGBoost. A link to the documentation: https://xgboost.readthedocs.io/en/latest/ We will use Scikit-Learn Wrapper interface for XGBoost (and the same logic applies to the following LightGBM and CatBoost models). Here, we work on the regression task - hence we will use XGBRegressor. Read…
- 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 1. Required Model (100 pts): Implement and train a logistic regression as your classification model. • You have to use Sklearn deep learning library. • You may want to refer to this tutorial: https://bit.ly/37anOxiTODO: Lienar Regression with least Mean Squares (LMS) Optimize the model through gradient descent. *Please complete the TODOs. * !pip install wget import osimport randomimport tracebackfrom pdb import set_traceimport sysimport numpy as npfrom abc import ABC, abstractmethodimport traceback from util.timer import Timerfrom util.data import split_data, feature_label_split, Standardizationfrom util.metrics import msefrom datasets.HousingDataset import HousingDataset class BaseModel(ABC): """ Super class for ITCS Machine Learning Class""" @abstractmethod def fit(self, X, y): pass @abstractmethod def predict(self, X): pass class LinearModel(BaseModel): """ Abstract class for a linear model Attributes ========== w ndarray weight vector/matrix """ def __init__(self): """ weight vector w is initialized as None """ self.w = None # check if the matrix is 2-dimensional. if…A fingemark is to be matched against possible candidate fingerprints on file, using a minutiae-based matching method. Assume, for simplicity, 10 minutiae are compared between the images. After the images are translated and superimposed, the 10 minutiae pairs are identified and their differences are evaluated. The followving table shows the differences of the minutiae pairs when compared against each of eight candidates. Assume the threshold is set to 80%, which means that the two fingerprints will be considered as a match only if the differences of at least 8 out of the 10 pairs are at or below the acceptable difference Also assume the acceptable difference 0.05 or less. Which of the eight candidates will be considered a match? Select at least 1 answer. 1 2 3 4 5 6 7 89 10 Pair number Fingerprint (a) 0.06 0.01 0.05 0.01 0.07 0.04 0.02 0.07 0.08 0.02 Fingerprint (b) 0.06 0.03 0.02 0.00 0.08 0.04 0.10 0.03 0.02 0.09 Fingerprint (c) 0.07 0.03 0.06 0.04 0.01 0.05 0.07 0.07 0.08 0.06…