Business analytics. R programming 1. Import HouseData.csv with stringsAsFactors = FALSE to a home data frame. Factor() Parking and City_Category vectors.
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Business analytics. R
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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().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 belowTODO 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()
- create 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.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.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…
- Use elements of (0,0,0,0,0,1,1,0,2} and write a SciPy program for CSR Matrix.The length of vectors in Word2Vec model is 2|V], where |V| represents the length of unique vocabulary. True FalseFill 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
- 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…Data Exploration: The Iris dataset is used for data exploration, using Python. Understand your attributes and target. Make sure your measurement and visualization are showing comparison of attributes and target. 1. Construct boxplot and identify the presence of outlier (if any) 2. Compute correlation matrix and construct scatter matrix. Observe collinearity pattern.TODO: 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…