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 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.

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

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 LOBBYING IN THE SYSTEM OF SOCIO-ECONOMIC PRIORITIES OF UKRAINE

2020 ◽  
pp. 53-69
Author(s):  
Olena GOLOVNYA
Keyword(s):  
Regional Integration ◽  
Trade Unions ◽  
High Sensitivity ◽  
Developed Countries ◽  
Civil Society Organizations ◽  
Modern World ◽  
Business And Society ◽  
Electoral Campaigns ◽  
The Usa ◽  
The Government

The article is devoted to the research of lobbying activities as an important component of the process of forming the state policy of socio-economic development. The author emphasizes that the Ukrainian economy has a high dependence on the external environment, as well as high sensitivity to global economic fluctuations. In turn, the openness of the country's economy is a significant factor in its involvement in modern value chains, global and regional integration. It is determined that securing a full-fledged public-private partnership in the modern world requires lobbying as a deliberate influence on the public by the authorities in order to make a number of economic decisions. The study found that the structure of the phenomenon of "lobbying" includes three main components: object, subject, technology. Thus, lobbying in the modern world appears to be a complex process of purposeful influence on the government in order to obtain the desired solution. The purpose of lobbying structures activities is often a series of decisions, in which the adoption of laws concerning economic activity and investment, innovation, and customs policies is of great importance. It is revealed that lobbying requires an extensive network of institutions and organizations - from trade unions and business owners to the media and civil society organizations. Significant influence on the advancement of national socio-economic priorities is exercised by consulting firms, mass media, non-state think tanks, various industry associations. Lobbying in developed countries is a tool for cooperation and communication that leads to progressive decisions that are beneficial to both business and society. This confirms the progressive experience of the USA and the EU. The study draws attention to the fact that Ukraine mainly practices shadow lobbying, since the activities of domestic lobbyists are not regulated by any legislative acts. This is explained by the fact that our business and political leaders benefit from such interaction when large financial and industry groups sponsor, large decision-makers and expensive electoral campaigns.

scholarly journals Dampak Program Keluarga Harapan (PKH) terhadap Kesejahteraan Keluarga di Desa Padang Seurahet Kecamatan Johan Pahlawan Kabupaten Aceh Barat

2021 ◽  
Vol 7 (2) ◽  
pp. 92
Author(s):  
Lia Muliana ◽  
Mursyidin Mursyidin ◽  
Muharriyanti Siregar
Keyword(s):  
Low Income ◽  
Family Welfare ◽  
Transfer Program ◽  
Family Needs ◽  
Social Significance ◽  
Study Results ◽  
The Family ◽  
Low Income Families ◽  
The Government ◽  
The Impact

The Family Hope Program (PKH) is a conditional cash transfer program for low-income families. The requirement is to be actively involved in education and health. The Family Hope Program in Indonesia was implemented in 2007 to alleviate poverty and prosper low-income families. The research and writing of this journal aim to determine the impact of PKH on family welfare and see if there is a reduction in poverty after the government realizes the Family Hope Program. This study uses a qualitative method with a descriptive approach. The study results indicate that the impact of PKH on beneficiary families, including the cash provided, can meet consumption or family needs and help the economy of PKH recipient families. The implementation of the program can reduce poverty in Aceh. The percentage of the poverty rate fell to 0.02%. The limitation of the research is the impact of PKH on family welfare and wants to examine whether there is a decrease in the percentage of poverty in Aceh or Aceh Barat. The practical implication of this research is to provide information to the government that PKH can improve the welfare of low-income families. The social significance is to give the government and the general public that the implementation of social assistance programs, one of which is the Family Hope Program, can reduce poverty rates in Aceh or West Aceh. The originality of the research is supported by previous research related to the author’s research study.

scholarly journals The main problems of interrelations' mutual conditionality of the national human capital, defense industry complex and aerospace defense

2013 ◽  
pp. 79-85
Author(s):  
D. O. Salyukov
Keyword(s):  
Human Capital ◽  
Defense Industry ◽  
Current Situation ◽  
Modern World ◽  
Modern State ◽  
National Human ◽  
Key Concepts ◽  
The Government

The article gives the review and highlights the importance of the system consisting of such broad and key concepts for the government, as the national human capital, defense industry complex and aerospace defense. The article also gives the analysis of the correlation of elements of this system and their unconditional necessity in the modern state as obligatory attributes of safety of the country in the modern world. The problems of the system and the influence of "soft forces" are exemplified by the current situation in the Russia Federation.

scholarly journals Prediction System for the Spread of Corona Virus in Central Java with K-Nearest Neighbor (KNN) Method

2021 ◽  
Vol 2 (3) ◽  
pp. 80-85
Author(s):  
Farid Fitriyadi ◽  
Muqorobin Muqorobin
Keyword(s):  
Nearest Neighbor ◽  
Current Data ◽  
Problem Analysis ◽  
Prediction System ◽  
K Nearest Neighbor ◽  
Religious Activities ◽  
Literature Study ◽  
Central Java ◽  
Corona Virus ◽  
The Government

Abstract—Corona Virus is currently spreading very rapidly in many parts of Indonesia, including Central Java Province. According to the current data of corona database in Central Java, today on 17th of August 2021, the number of confirmed cases is; Confirmed in Treatment (Active Cases): 16.344, Confirmed Recovered: 408.697, and Confirmed Dead: 29.148. Therefore, the total number of cases is 454.189, obtained from the sum of the number of being treated, recovered, and dead. Corona Virus is a collection of viruses that can infect the respiratory system, generally mild, such as common cold, although, some forms of diseases like; SARS, MERS, and COVID-19 are more deadly. In anticipating this case, the government has created some policies which include; limiting activities outside the house, having school activities done from home, working from home, and even having religious activities done from home too. The purpose of this study was to predict the possible rate of new cases in one of Central Java areas with confirmed cases of corona virus. Thus, it can be used as information material for the public to anticipate early. The research method applied in this research is problem analysis and literature study, data collection and implementation. The application of the K-Nearest Neighbor (KNN) method is expected to be able to predict the level of spread of COVID-19 in Central Java. The results of the research on testing the prediction system for the new cases level were tested in the Sragen area. Testing is carried out by taking samples for new cases, namely Kudu Regency/City, Confirmed: 17,599, Treated: 89, Recovered: 18,303, Died: 1,721, Suspected: 87 and Discarded Suspected: 1,711. After doing the prediction with K-NN algorithm, it showed the Condition: High.

scholarly journals The skill assessment of ENSO prediction issued by JMA ensemble prediction system and CFSv2

2021 ◽  
Vol 893 (1) ◽  
pp. 012047
Author(s):  
R Rahmat ◽  
A M Setiawan ◽  
Supari
Keyword(s):  
Southern Oscillation ◽  
Skill Score ◽  
Skill Assessment ◽  
Japan Meteorological Agency ◽  
Ensemble Prediction ◽  
In Situ Observation ◽  
Prediction System ◽  
Ensemble Prediction System ◽  
Enso Prediction ◽  
The Government

Abstract Indonesian climate is strongly affected by El Niño-Southern Oscillation (ENSO) as one of climate-driven factor. ENSO prediction during the upcoming months or year is crucial for the government in order to design the further strategic policy. Besides producing its own ENSO prediction, BMKG also regularly releases the status and ENSO prediction collected from other climate centers, such as Japan Meteorological Agency (JMA) and National Oceanic and Atmospheric Administration (NOAA). However, the skill of these products is not well known yet. The aim of this study is to conduct a simple assessment on the skill of JMA Ensemble Prediction System (EPS) and NOAA Climate Forecast System version 2 (CFSv2) ENSO prediction using World Meteorological Organization (WMO) Standard Verification System for Long Range Forecast (SVS-LRF) method. Both ENSO prediction results also compared each other using Student's t-test. The ENSO predictions data were obtained from the ENSO JMA and ENSO NCEP forecast archive files, while observed Nino 3.4 were calculated from Centennial in situ Observation-Based Estimates (COBE) Sea Surface Temperature Anomaly (SSTA). Both ENSO prediction issued by JMA and NCEP has a good skill on 1 to 3 months lead time, indicated by high correlation coefficient and positive value of Mean Square Skill Score (MSSS). However, the skill of both skills significantly reduced for May-August target month. Further careful interpretation is needed for ENSO prediction issued on this mentioned period.

A Survey of the Barriers Affecting the Career Practice and Promotions of Female Dentists in Saudi Arabia

2019 ◽  
Vol 4 (2) ◽  
pp. 187-195 ◽  
Author(s):  
M. Rajeh ◽  
B. Nicolau ◽  
A. Qutob ◽  
P. Pluye ◽  
S. Esfandiari
Keyword(s):  
Saudi Arabia ◽  
Regression Analysis ◽  
Gender Discrimination ◽  
Cultural Factors ◽  
Male Dominance ◽  
Family Needs ◽  
Cross Sectional Survey ◽  
Cross Sectional ◽  
The Government ◽  
Career Practice

Introduction: Over the last 40 y, the proportion of women in the profession of dentistry has been growing steadily. The extant literature, although limited, demonstrates that gender differences exist in choice of specialization, practice pattern, and professional attitudes, revealing that women are more likely to work in primary dental care and are less prominent in other dental specialties. Female Saudi dentists, working in the government sector, tend to occupy lower positions in the occupational hierarchy, are paid less, and are less likely to hold consultant positions as compared with men. Objectives: The objectives of this study were to identify barriers faced by female dentists practicing in Saudi Arabia in seeking professional advancement and to determine the variables that influenced respondents’ promotions. Methods: In February 2017, a web-based cross-sectional survey was emailed to all female dentists registered with the Saudi Dental Society ( N = 2,651). Completed questionnaires ( N = 130, response rate = 7.1%) were analyzed with simple summary statistics and a logistic regression analysis to evaluate the association between the dependent variable (promotion) and independent variables (family, environmental, interpersonal, and cultural factors). Results: Most female dentists believed that family, environmental, and cultural factors are challenges to their career practice and progression. Other factors included interpersonal challenges, such as gender discrimination and male dominance in the field of dentistry. Results of the regression analysis revealed that family and environmental factors were significant predictors of whether female dentists would be promoted. Conclusion: Saudi female dentists continue to face significant obstacles in their career practice and advancement. Their role in the workplace needs to be recognized. Factors that obstruct their career advancement should be well understood by dental institutions and efforts should be made to move more female dentists into leadership positions. Knowledge Transfer Statement: Policy makers can use the results of this study to develop strategies to overcome the barriers faced by female dentists in Saudi Arabia with respect to their professional and personal (family) needs. This study could lead to the development of employment incentives and a supportive workplace for female dentists.

scholarly journals Modelling House Price Using Ridge Regression and Lasso Regression

2018 ◽  
Vol 7 (4.30) ◽  
pp. 498 ◽  
Author(s):  
Seng Jia Xin ◽  
Kamil Khalid
Keyword(s):  
Multivariate Analysis ◽  
Regression Model ◽  
Ridge Regression ◽  
House Price ◽  
Lasso Regression ◽  
Price Prediction ◽  
Real Estate Sector ◽  
Finance Company ◽  
The Government ◽  
House Condition

House price prediction is important for the government, finance company, real estate sector and also the house owner.  The data of the house price at Ames, Iowa in United State which from the year 2006 to 2010 is used for multivariate analysis. However, multicollinearity is commonly occurred in the multivariate analysis and gives a serious effect to the model. Therefore, in this study investigates the performance of the Ridge regression model and Lasso regression model as both regressions can deal with multicollinearity. Ridge regression model and Lasso regression model are constructed and compared. The root mean square error (RMSE) and adjusted R-squared are used to evaluate the performance of the models. This comparative study found that the Lasso regression model is performing better compared to the Ridge regression model. Based on this analysis, the selected variables includes the aspect of  house size, age of house, condition of house and also the location of the house.

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