**Used Car Price Prediction** Used car price prediction involves using data-driven models and machine learning techniques to estimate the market value of a used vehicle based on a variety of factors. This process analyzes historical car price data, vehicle specifications, market trends, and other relevant attributes to provide an accurate price forecast. The key factors influencing the price include: 1. **Make and Model**: Different car brands and models have distinct market values. Luxury or high-demand brands tend to have higher resale prices. 2. **Age of the Car**: Generally, newer cars have higher prices. The depreciation rate of a car is a crucial factor in price prediction. 3. **Mileage**: The total distance a car has traveled impacts its price. Lower mileage typically indicates less wear and tear, increasing the car’s value. 4. **Condition of the Car**: Physical and mechanical conditions such as the exterior appearance, interior, and engine health significantly influence the car's resale value. 5. **Location**: The geographical region where the car is being sold can affect the price. Market trends, climate, and demand can vary by location. 6. **Vehicle History**: Whether the car has been in any accidents, has a clean title, or has a history of service can all play a role in the pricing. 7. **Additional Features**: Cars with upgraded features like sunroofs, leather seats, or advanced technology packages typically fetch higher prices. Machine learning models like regression analysis, decision trees, and neural networks are often used to predict the prices, incorporating all these variables into an algorithm that can provide an estimated value based on input data. These predictive models are valuable for both buyers and sellers, helping them understand the fair market value of used cars and make informed decisions in the car-buying or selling process.

1. USED CAR PRICE PREDICTION USING
LINEAR REGRESSION MODELLING
K.V. BHUVANESH
URK23CS5075

2. INTRODUCTION OBJECTIVE
MOTIVATION AND CHALLENGES
LITERATURE REVIEW
TECHNOLOGY USED
SYSTEM DESIGN
METHODOLOGY EXPERIMENT
AND RESULTS CONCLUSION

3. 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 re­selling
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.

4. •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.

5. players and several retailers. The multinational players are mainly
-,
•The automotive industry is composed of a few top global multinational
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.
•
•
'-'

6. •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.

7. •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 on­road 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.

8. • 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

9. • 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.

10. 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
Input
Used Car Price
Prediction
User
Output

11. 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.
ll'ain Set
Data
(ft
;
ML
Algorithm
Output
Evaluation
I
Model
Data
Preprocessing
I
·Test Set
•
Inputs
'

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

13. from matplotlib import
style
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style .use('ggplot')
fig = plt.figure(figsize=(lS,5))
fig.suptitle('Visualizing categorical data
columns ') plt .subplot(l,3,1)
plt .bar(fuel_type,selling_price,
color='royalblue ') plt .xlabel("Fuel Type")
plt .ylabel("selling Price")
plt .subplot(l,3,2)
plt .bar(seller_type, selling_price, color=
'red') plt .xlabel("Seller Type")
plt .subplot(l,3,3)
plt .bar(transmission_type, selling_price,
color= 'purple ') plt .xlabel ('Transmission
type')
plt .show()
Vlsualtzino cato00r1ca1 data
columns
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Dl...i
fuc.-1 'Type
tNC. ...,,.,.. hltom.o:ic
lronm1s1on type>

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Actual vs predicted price
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Predict
ed
Price
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15. f rom sklearn .metrics import mea n_absolute_error, mea n_squar,ed_error, r2 score
print ("MAE : ", (metrics .mean_absolute_error (pred , y_test ) ) )
print ("MSE : ",.
(metrics .mea11_sq ua red_er ror (pred , y_test ) ) )
print ("R2 score : ", (metrics.r2_score( pred ,. y_test ) ))
MAE :
l.2581404706473374
MSE: 3.4932860262251455
R2 score:
0.8294933369778 821

16. In this project , a Linear Regression Model was successfully implemented employing var10us 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 mISs1ng 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.

17. [1] M. G. Pattabiraman Venkatasubbu, "Used Cars Price Prediction using Supervised Leaming 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 Leaming 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 Leaming Techniques.," International Journal of Information &
Computation Technology, Computer Science and Engineering Department, University of Mauritius, Reduit, MAURJTJUS. , 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.