Networking While Long-Term Unemployed: Why Long-Term Unemployment Will Likely Persist Long after the COVID-19 Pandemic Recedes

By early 2021, due to the economic downturn accompanying the COVID-19 pandemic, over four million Americans were long-term unemployed (LTU). Getting rehired requires overcoming employer stigmas about LTU workers, which most LTU workers believe is most plausible with the help of referrals from social ties. While research on the structures and effects of networks abound, this paper examines the far less studied process of networking among LTU jobseekers. Exploring the networking process is imperative for understanding the emotional toll and structural obstacles facing LTU workers in the post–COVID-19 pandemic period. Going beyond individualizing explanations, I use in-depth interviews to uncover the structural conditions that make networking challenging for all LTU jobseekers. Contrary to static conceptualizations of ties as social capital, a metaphor implying that ties are static resources, this paper argues that more attention needs to be paid to the processes and structural conditions that facilitate or hinder the activation or formation of socialties. Specifically, it shows that, under conditions of precarity and stigma, networking can undermine workers’ identities as valued and moral, leaving them feeling discouraged and ethically challenged in a way similar to used car salespeople.

Used Car Price Estimation: Moving from Linear Regression towards a New S-Curve Model

A simple linear regression is commonly used as a practical predictive model on a used car price. It is a useful model which carry smaller prediction errors around its central mean. Practically, real data will hardly produce a linear relationship. A non-linear model has been observed to better forecast any price appreciation and manage prediction errors in real-life phenomena. In this paper, an S-curve model shall be proposed as an alternative non-linear model in estimating the price of used cars. A dynamic S-shaped Membership Function (SMF) is used as a basis to build an S-curve pricing model in this research study. Real used car price data has been collected from a popular website. Comparisons against linear regression and cubic regression are made. An S-curve model has produced smaller error than linear regression while its residual is closer to a cubic regression. Overall, an S-curve model is anticipated to provide a better and more practical estimate on used car prices in Malaysia.

USED CAR PRICE PREDICTION

Cars have become a necessity in this modern world. Every middle class family needs a vehicle or a mode of transport in order to move from one place to another. Not everyone is able to afford a new vehicle as they are costly and there’s an added cost of taxes and various other expenses by both the provider/company of the car as well as the government. Moreover, not every customer is sure of spending a sum of their wealth on a certain car. The product might not meet their needs. The solution to this problem of having a car despite not being able to afford one is met by buying and selling second hand cars. It has become its own market now. There are already numerous companies and websites and app based services that serve as a mediator or a platform for the dealing of second hand or used cars and other vehicles. Establishment of such places is easy but there is another problem that still remains- How to price the used car appropriately at a price comfortable for both the seller and the buyer? Luckily, the Used Car Price Prediction systems exist and can be developed. Users might think that it’s easy to determine the price of a used car, and whether there is even a need to have such a system. In truth, there are a lot of factors that are important in determining the price of a second hand vehicle. The quality of a vehicle deteriorates with age1 of course but that is not all. Every single vehicle is different even when it is manufactured and sold as a new product and even more so when the same vehicle is used over time. Different people may use their vehicles more or less depending on their everyday activity, making kilometers driven as one of the important factors for the price prediction. It is obvious that a vehicle which is driven for 2000 kilometers in 1 year would be priced less than a vehicle which has been driven for only 500 kilometers in 2 years. This is just one of the factors that determine the price of a used car. In our Car Price Prediction System, we have used the Year of Manufacturing (used to determine the age of the vehicle by subtracting this from the date of selling), the original maximum retail price of the vehicle (the price at which the vehicle was sold at from the manufacturing company/garage), the fuel type of the vehicle (Petrol, Diesel, CNG, Electric ; This affects the pricing severely as different fuel type engines have different prime performance periods and different rates of deterioration), Seller Type (Individual or Dealership), Transmission (Manual or Automatic), Number of past owners of the vehicle. Using all these factors2, we are going to determine which model is best to determine a price for the used vehicle. For the Car Price Prediction System, Regression models3are used since these models give the results as a continuous curve instead of a categorized value as a result. Due to this, we can use the continuous curve to determine an accurate price for each and every scenario which won’t be possible if the results obtained were in the form of a range. The final model of the system will implement the best suited algorithm and have a UI (User Interface) which make it possible for a user to be able to enter the values of these deciding factors and the system will predict the price for them. Keywords: Car price prediction, machine learning, regression analysis, linear regression, correlation analysis

The Problem of the Perpetuation of Site-Specific Installation Art

In the summer of 1961, Allan Kaprow (1927–2006) installed dozens of used car tyres in the courtyard of the Martha Jackson Townhouse Gallery in New York City. The artist had collected these tyres from a nearby garage and invited his friends and fellow artists to participate in the Happening called Yard.2 There was no audience except for the participants who jumped over the heaps of tyres and moved them around. Photographs of Yard show Kaprow arranging the tyres within the small space of the courtyard, which was officially the sculpture garden of the gallery. Apart from the photographs, accounts of the event are scarce, and the press hardly paid any attention to it. And yet, Yard became one of Kaprow’s seminal Happenings. The work has been acquired for many museum collections and was re-executed on numerous occasions, both by Kaprow and others, at different places and with other participants.

Price Prediction for Pre-Owned Cars Using Ensemble Machine Learning Techniques

The Pre-owned cars or so-called used cars have capacious markets across the globe. Before acquiring a used car, the buyer should be able to decide whether the price affixed for the car is genuine. Several facets including mileage, year, model, make, run and many more are needed to be considered before getting a hold of any pre-owned car. Both the seller and the buyer should have a fair deal. This paper presents a system that has been implemented to predict a fair price for any pre-owned car. The system works well to anticipate the price of used cars for the Mumbai region. Ensemble techniques in machine learning namely Random Forest Algorithm, eXtreme Gradient Boost are deployed to develop models that can predict an appropriate price for the used cars. The techniques are compared so as to determine an optimal one. Both the methods provided comparable performance wherein eXtreme Boost outperformed the random forest algorithm. Root Mean Squared Error of random forest recorded 3.44 whereas eXtreme Boost displayed 0.53.

USED CAR PRICE PREDICTION USING MACHINE LEARNING

Cars are an important asset and their importance has increased exponentially in our life. With the increase in the demand and growing needs, the production of cars has also increased. But due to inflation in the prices of new cars, there are people who still can only afford a used car due to their financial conditions. This whole process has given rise to the used car market, which is outperforming many other industries and is rising every day. The rising market for the used car has also resulted in a great increment in sales of Used Cars. Used Car Sales are on a global increase. But, determining the appropriate listing price of a used car is a challenging task, due to the many factors that drive prices of a used vehicle in the market. And that is why there is an urgent need for a system which can accurately predict the price of a used car. considering all the factors that affect the price of a used car. Keywords: Used Car Price Prediction, Linear Regression, XGBoost, Decision Tree

Object detection and used car price predicting analysis system (ucpas) using machine learning technique

The highly interesting research area that noticed in the last few years is object detection and find out the prediction based on the features that can be benefited to consumers and the industry. In this paper, we understand the concept of object detection like the car detection, to look into the price of a second-hand car using automatic machine learning methods. We also understand the concept of object detection categories. Nowadays, the most challenging task is to determine what is the listed price of a used car on the market, Possibility of various factors that can drive a used car price. The main objective of this paper is to develop machine learning models which make it possible to accurately predict the price of a second-hand car according to its parameter or characteristics. In this paper, implementation techniques and evaluation methods are used on a Car dataset consisting of the selling prices of various models of car across different cities of India. The outcome of this experiment shows that clustering with linear regression and Random Forest model yield the best accuracy outcome. The machine learning model produces a satisfactory result within a short duration of time compared to the aforementioned self.