Select all the tasks where linear regression algorithm can be applied.Note: Multiple options can be correct.You have a data set of BMI (body mass index) and fat percentage of the customers of a fitness centre. The fitness centre wants to predict the fat percentage of a new customer, given his BMI.You have collected data from a house rental website like commonfloor.com. The data includes the rental prices of apartments and customer ratings as HIGH or LOW. You want to predict the customer rating, given the rental price of a new house.You want to predict the sales of a retail store based on its size, given the data set of sales of retail stores and their sizes.You want to predict whether a customer is likely to leave the telecom network.
Question
Select all the tasks where linear regression algorithm can be applied.Note: Multiple options can be correct.You have a data set of BMI (body mass index) and fat percentage of the customers of a fitness centre. The fitness centre wants to predict the fat percentage of a new customer, given his BMI.You have collected data from a house rental website like commonfloor.com. The data includes the rental prices of apartments and customer ratings as HIGH or LOW. You want to predict the customer rating, given the rental price of a new house.You want to predict the sales of a retail store based on its size, given the data set of sales of retail stores and their sizes.You want to predict whether a customer is likely to leave the telecom network.
Solution
-
The task of predicting the fat percentage of a new customer, given his BMI, at a fitness centre can be solved using a linear regression algorithm. This is because both BMI and fat percentage are continuous variables, and linear regression is used to predict a continuous outcome variable (dependent variable) based on one or more predictor variables (independent variables).
-
The task of predicting the customer rating, given the rental price of a new house, cannot be solved using a linear regression algorithm. This is because the customer rating is a categorical variable (HIGH or LOW), not a continuous one. For such tasks, classification algorithms are more suitable.
-
The task of predicting the sales of a retail store based on its size can be solved using a linear regression algorithm. This is because both the sales of a retail store and its size are continuous variables, making this a suitable task for linear regression.
-
The task of predicting whether a customer is likely to leave the telecom network cannot be solved using a linear regression algorithm. This is because the outcome to be predicted (whether a customer will leave or not) is a binary categorical variable, not a continuous one. For such tasks, classification algorithms are more suitable.
Similar Questions
Question 3In which of the following would you use Multiple Linear Regression?1 pointPredict whether a customer is likely to repay a loan based on age and income.Predict CO2 emission of a car based on engine size.Recommend products to customers based on their demographic characteristics.Predicting the production of apples in an orchard based on temperature and rainfall.
You want to create a model to predict the cost of heating an office building based on its size in square feet and the number of employees working there. What kind of machine learning problem is this? RegressionClassificationClustering
I predict house prices based on multiple features like area, bedrooms, and bathrooms. Which regression method am I?
Situation: The Ipod Touch has been out for many years now and a lot of data has been collected.Relevant Relationship:There is a functional relationship between Price of an IPod Touch,𝑝 and Weekly Demand,𝑠.Below is a table of data that have been collectedPrice,𝑝,($) Weekly Demand,𝑠,(1,000s)150 208170 199190 200210 184230 182250 176A. Find the linear model that best fits this data using regression and enter the model below(for entry round the linear parameter value to nearest 0.01 and constant parameter to nearest 1)𝑠= B. The squared correlation coefficient was 0.95(note: values less than 0.95 MAY mean the model is not appropriate for making predictions)Now answer these two questions using the UNROUNDED model parametersC. What does the model predict will be the weekly demand if the price of an ipod touch is $246 ? (nearest 100)D. According to the model at what should the price be set in order to have a weekly demand of 179,800 ipod Touches? $ (nearest $1)
Select Any One Of the Following Options: If you want to predict the price of an apartment, which of the following ML techniques you may consider?RegressionClusteringAny of the these optionsClassification
Upgrade your grade with Knowee
Get personalized homework help. Review tough concepts in more detail, or go deeper into your topic by exploring other relevant questions.