USED CAR PRICE PREDICTION USING MACHINE LEARNING

2021 ◽  
Vol 1 (10) ◽  
pp. 246-251
Author(s):  
Himanshu Dahiya ◽  
Chetan Aggarwal ◽  
Shubh Goyal ◽  
Mini Agarwal
Keyword(s):  
Machine Learning ◽  
Price Prediction ◽  
Whole Process ◽  
Financial Conditions ◽  
Car Market ◽  
Car Sales ◽  
Used Car ◽  
Used Cars ◽  
Global Increase ◽  
The Many

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

Der Durchbruch des PKW zum Massenkonsumgut 1950-1964

2010 ◽  
Vol 51 (1) ◽  
Author(s):  
Christopher Kopper
Keyword(s):  
Additional Benefit ◽  
Price Regulation ◽  
Blue Collar ◽  
Car Ownership ◽  
Blue Collar Workers ◽  
Car Market ◽  
Car Sales ◽  
Used Car ◽  
Used Cars ◽  
Market Strategies

AbstractUntil now, research on the breakthrough of mass motorization has neglected the importance of the used car market. Empirical evidence proves that the used car market had a significant impact on the growth of car ownership and the purchase of cars among white and blue-collar workers. The transparency and flexibility of the used car market, the lack of price regulation and the degressive curve of used car prices facilitated car ownership among medium income Germans as early as the late 1950s. German car manufacturers recognized the potential of the used car market for the promotion of new car sales, but adopted different market strategies. US companies like Opel and Ford changed their models frequently to promote the sale of new cars and to accelerate the obsolescence of older models, whereas Volkswagen followed the strategy of incremental changes in order to create a higher value for used cars and to generate an additional benefit for new car customers.

scholarly journals Used Cars Price Prediction using Supervised Learning Techniques

2019 ◽  
Vol 9 (1S3) ◽  
pp. 216-223
Keyword(s):  
Machine Learning Algorithms ◽  
Lasso Regression ◽  
Passenger Cars ◽  
Price Prediction ◽  
The Past ◽  
Car Market ◽  
Learning Techniques ◽  
Used Car ◽  
Consumer Data ◽  
Used Cars

The production of cars has been steadily increasing in the past decade, with over 70 million passenger cars being produced in the year 2016. This has given rise to the used car market, which on its own has become a booming industry. The recent advent of online portals has facilitated the need for both the customer and the seller to be better informed about the trends and patterns that determine the value of a used car in the market. Using Machine Learning Algorithms such as Lasso Regression, Multiple Regression and Regression trees, we will try to develop a statistical model which will be able to predict the price of a used car, based on previous consumer data and a given set of features. We will also be comparing the prediction accuracy of these models to determine the optimal one.

scholarly journals Quality Uncertainty Erodes Trust in Science

2017 ◽  
Vol 3 (1) ◽  
Author(s):  
Simine Vazire
Keyword(s):  
Quality Uncertainty ◽  
Scientific Publications ◽  
Design Data ◽  
The Public ◽  
Car Market ◽  
Economic Markets ◽  
Used Car ◽  
The Difference ◽  
Used Cars ◽  
The Aesthetic

When consumers of science (readers and reviewers) lack relevant details about the study design, data, and analyses, they cannot adequately evaluate the strength of a scientific study. Lack of transparency is common in science, and is encouraged by journals that place more emphasis on the aesthetic appeal of a manuscript than the robustness of its scientific claims. In doing this, journals are implicitly encouraging authors to do whatever it takes to obtain eye-catching results. To achieve this, researchers can use common research practices that beautify results at the expense of the robustness of those results (e.g., p-hacking). The problem is not engaging in these practices, but failing to disclose them. A car whose carburetor is duct-taped to the rest of the car might work perfectly fine, but the buyer has a right to know about the duct-taping. Without high levels of transparency in scientific publications, consumers of scientific manuscripts are in a similar position as buyers of used cars – they cannot reliably tell the difference between lemons and high quality findings. This phenomenon – quality uncertainty – has been shown to erode trust in economic markets, such as the used car market. The same problem threatens to erode trust in science. The solution is to increase transparency and give consumers of scientific research the information they need to accurately evaluate research. Transparency would also encourage researchers to be more careful in how they conduct their studies and write up their results. To make this happen, we must tie journals’ reputations to their practices regarding transparency. Reviewers hold a great deal of power to make this happen, by demanding the transparency needed to rigorously evaluate scientific manuscripts. The public expects transparency from science, and appropriately so – we should be held to a higher standard than used car salespeople.

Used Cars Price Prediction using Machine Learning with Optimal Features

2021 ◽  
Vol 4 (2) ◽  
pp. 113-119
Author(s):  
Muhammad Asghar ◽  
Khalid Mehmood ◽  
Samina Yasin ◽  
Zimal Mehboob Khan
Keyword(s):  
Machine Learning ◽  
Learning Algorithm ◽  
Model Performance ◽  
Market Value ◽  
Statistical Test ◽  
Difficult Decision ◽  
Price Prediction ◽  
Proposed Model ◽  
Design Value ◽  
Used Cars

We all are needed the personal vehicle that could help us to travel from home to office and travel to vocations means we need the personal vehicle for traveling for this we purchase the new vehicle or used vehicle this is some time take so much to take decision for purchasing the new one and most difficult decision is to take how to sale the old one that is already we have keep using if we sale and what is best price we can get or gives us more benefits. More over the purchasing power of the customers is low due to the prices of the new cars. There are different methods to predict the price of the car according to market value. Our proposed method helps the both the purchase and seller for to purchase and sale their vehicle and they can predict the best for their vehicle and make their decision good for personal and business. Our proposed model performance shows that the proposed study is productive and efficient. In the proposed study the machine learning algorithm Regression helps in the outperform. Here we use the Statistical test to get the design value of P and get the optimal features and using the linear regression. First, we find the RFE and then apply the statistical test for VIF for the OLS Regression. Prediction results shows the study is efficient and effective.

USED CAR PRICE PREDICTION

2021 ◽  
Vol 1 (10) ◽  
pp. 256-262
Author(s):  
Vaibhav Gupta ◽  
Sharma M.L ◽  
Tripathi K.C
Keyword(s):  
Modern World ◽  
Prediction System ◽  
Family Needs ◽  
Fuel Type ◽  
Continuous Curve ◽  
Everyday Activity ◽  
Price Prediction ◽  
Used Car ◽  
Used Cars ◽  
The Government

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

scholarly journals Analysis on Used Car Dataset using Machine Learning Models

2020 ◽  
Vol 8 (6S) ◽  
pp. 164-166
Keyword(s):  
Machine Learning ◽  
Research Paper ◽  
Learning Models ◽  
Fuel Type ◽  
Used Car ◽  
Used Cars ◽  
Type Power ◽  
Engine Type ◽  
Machine Learning Models

The research paper focuses on study of used cars of different models based on different fuel types, owner types and years all at different locations and also other factors like Mileage, Engine type, Power consumed and number of seats available. Data is visualized on the basis of Kilometers driven, Fuel Type and Owner Type.

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

2021 ◽  
Author(s):  
Chetna Longani ◽  
Sai Prasad Potharaju ◽  
Sandhya Deore
Keyword(s):  
Machine Learning ◽  
Random Forest ◽  
Mean Squared Error ◽  
Machine Learning Techniques ◽  
Random Forest Algorithm ◽  
Fair Price ◽  
Ensemble Machine Learning ◽  
Comparable Performance ◽  
Used Car ◽  
Used Cars

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.

scholarly journals Decision Support System for Purchase of Used Cars using Electre Method

2019 ◽  
Vol 1 (2) ◽  
pp. 45-59
Author(s):  
Novendra Adisaputra Sinaga ◽  
Arifin Tua Purba
Keyword(s):  
Decision Support ◽  
Decision Support System ◽  
Support System ◽  
Electre Method ◽  
Used Car ◽  
Used Cars ◽  
The Many ◽  
The Right ◽  
Selection Of

- This study aims to build a decision support system that can help facilitate the selection of the right used car for potential buyers. To be able to buy a used car that suits the needs and funds owned by consumers, buyers must consider the many criteria and factors of each used car which consists of various brands that exist today. This study uses the ELECTRE (Elimination and Choice Translation Reality) method. The criteria in the comparison used in this decision support system are documents, cylinder volume, year of production and car price. The ELECTRE method is used in conditions where alternatives that do not meet the criteria are eliminated, and suitable alternatives can be generated. Of the 8 samples of used cars studied, the best recommendation was Ayla (A4) with a total aggregate of 4 and an alternative that was not recommended was Agya (A3) with a total aggregate of 0.

scholarly journals Crop price prediction using supervised machine learning algorithms

2021 ◽  
Vol 1916 (1) ◽  
pp. 012042
Author(s):  
Ranjani Dhanapal ◽  
A AjanRaj ◽  
S Balavinayagapragathish ◽  
J Balaji
Keyword(s):  
Machine Learning ◽  
Learning Algorithms ◽  
Machine Learning Algorithms ◽  
Supervised Machine Learning ◽  
Price Prediction
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