USED CAR PRICE PREDICTION USING LINEAR
REGRESSION MODELLING

AGENDA:
 INTRODUCTION
 OBJECTIVE
 MOTIVATION AND CHALLENGES
 LITERATURE REVIEW
 TECHNOLOGY USED
 SYSTEM DESIGN
 METHODOLOGY
 EXPERIMENT AND RESULTS
 CONCLUSION

INTRODUCTION
In this particular presentation we will be looking on:
 From a long time since being, a continuous paradigm of transactions of
commodities has been into existence. Earlier these transactions were in the form
of barter system which later was translated into a monetary system. And with
consideration into these, all changes that were brought about the pattern of re-
selling items was affected as well. There are two ways in which the re-selling of
the item is carried out. One is offline and the other being online.
 In offline transactions, there is a mediator present in between who is very
vulnerable to being corrupt and make overly profitable transactions. The second
option is online wherein there is a certain platform which lets the user find the
price he might get if he goes for selling.

OBJECTIVE
To build a supervised machine learning model for forecasting value of a
vehicle based on multiple attributes.
The system that is being built must be feature based i.e., feature wise
prediction must be possible.
Providing graphical comparisons to provide a better view.

MOTIVATION AND CHALLENGES
 The automotive industry is composed of a few top global multinational
players and several retailers. The multinational players are mainly
manufacturers by trade whereas the retail market features players who
deal in both new and used vehicles
 The used car market has demonstrated a significant growth in value
contributing to the larger share of the overall market. The used car
market in India accounts for nearly 3.4 million vehicles per year.

LITERATURE REVIEW
CARS24
Cars24 is a web platform where seller can sell their used car. It is an Indian Start-up
with a simplified user interface which asks seller parameters like car model, kilometers
traveled, year of registration and vehicle type (petrol, diesel). These allow the web model
to run certain algorithms on given parameters and predict the price.
GET VEHICLE PRICE
Get Vehicle Price is an android app which works on similar parameters as of Cars24.
This app predicts vehicle prices on various parameter like Fiscal power, horsepower,
kilometers traveled. This app uses a machine learning approach to predict the price of a
car, bike, electric vehicle and hybrid vehicle. This app can predict the price of any vehicle
because of the smartly optimized algorithm.

LITERATURE REVIEW
CARTRADE
CarTrade is web and Android platform where user can research New Cars in India by exploring Car Prices,
Car Specs, Images, Mileage, Reviews, and Car Comparisons. On this app one can Sell Used Car to genuine
buyers with ease. One can list their used car for sale along with the details like image, model, and year of
purchase and kilometers so that it is displayed to lakhs of interested car buyers in their city. User can read
user reviews and expert car reviews with images that help in finalizing a new car buying decision.
CARWALE
CarWale app is one of the top-rated car apps in India for new and used car research. It provides accurate on-
road prices of cars, genuine user and expert reviews. It can also compare different cars with the car
comparison tool. this app also helps you to connect with your nearest car dealers for the best offers
available.

TECHNOLOGY USED
 SCIPY
SciPy is a free and open-source Python library used for scientific computing and technical computing.
SciPy contains modules for optimization, linear algebra, integration, interpolation, special functions,
FFT, signal and image processing, ODE solvers and other tasks common in science and engineering.
 Matplotlib
Matplotlib is especially deployed for basic plotting. Bars, pies, lines, scatter plots and so on are part of
visualization using matplotlib. Matplotlib is a graphics package well integrated with NumPy and
Pandas. The MATLAB plotting commands are closely mirrored by the pyplot module

TECHNOLOGY USED
 Seaborn
Seaborn provides various visualization patterns. It has easy and interesting default themes and uses
fewer syntax. Statistics visualization is the speciality of seaborn and it is employed while
summarizing data in visuals and additionally depict the data distribution.
 Linear Regression
Regression is a method for predicting a dependent component with the help of independent variables.
The method is commonly used to predict and calculate correlations between independent and
dependent variables. The regression model establishes a linear or exponential connection between
independent and dependent variables.

SYSTEM DESIGN
A data flow diagram shows the way information flows through a process or system. It
includes data inputs and outputs; data stores and the various sub processes the data moves
through. DFDs are built using standardized symbols and notation to describe various entities
and their relationships. In this project there is one DFD

SYSTEM DESIGN
True data is noisy. Therefore, it is necessary to clean data so that the actual information from the
collected data can be acquired. Different processes are carried out to obtain the actual information
such as manual encoding and one hot encoding. Then feature extraction is performed to extract
necessary features. Linear regression modelling is used to train the dataset. User can give an input to
the detection model and it will provide an output.

Training Set Testing Set
Literature Review
Prediction
Dataset Collection
Data Pre-processing
Feature Extraction
Linear Regression Modelling
Error Calculation

EXPERIMENT AND RESULTS
The dataset is imported and read for understanding
car_data.head( )
car_data.isnull().sum()

VISUALIZING CATEGORICAL DATA COLUMNS

ACTUAL PRICE VS PREDICTED PRICE

ERROR CALCULATION

CONCLUSION
In this project, a Linear Regression Model was successfully implemented employing various prominent
algorithms from the python libraries and modules.
After the collection of data was done, further processing of data was done. The null entries and missing
datapoints were removed from the dataset and the categorical variables were also processed using One Hot
Encoding technique.
The r2 score of Linear Regression was 0.86 which is good and predictions were quite close to the original selling
prices.

REFERENCES
[1] M. G. Pattabiraman Venkatasubbu, “Used Cars Price Prediction using Supervised Learning Techniques.,” International Journal of
Engineering and Advanced Technology (IJEAT), 2019.
[2] B. I. D. K. Z. M. J. K. Enis Gegic, “Car Price Prediction using Machine Learning Techniques.,” International Burch University,
Sarajevo, Bosnia and Herzegovina, TEM Journal, 2019.
[3] A. W. D. A. K. D. M. V. Laveena D’Costa, “Predicting True Value of Used Car using Multiple Linear Regression Model.,”
International Journal of Recent Technology and Engineering, 2020.
[4] S. Peerun, “ Predicting the Price of Second-hand Cars using Artificial Neural Networks.,” Proceedings of the Second International
Conference, Nushrah Henna Chummun and Sameerchand Pudaruth, University of Mauritius, Reduit, Mauritius., 2014.
[5] S. Pudaruth, “Predicting the Price of Used Cars using Machine Learning Techniques.,” International Journal of Information &
Computation Technology, Computer Science and Engineering Department, University of Mauritius, Reduit, MAURITIUS. , 2014.
[6] D. S. S. S. G. S. K. S.E.Viswapriya, “Vehicle Price Prediction using SVM Techniques.,” International Journal of Innovative
Technology and Exploring Engineering, 2020.