This project develops a machine learning solution using linear regression to predict used car prices based on attributes like make, model, year, mileage, and fuel type. The model achieved an R-squared score of 0.81, indicating it explains 81% of the variability in car prices, thus aiding both buyers and sellers in the used car market. The project includes data collection, preprocessing, and model evaluation and aims to provide reliable price estimates.

1. MACHINE
LEARNING
USED CAR PRICE
PREDICTION
SUBMITTED BY:
G.Karthik(B191910)
K.Ravi(B192687)

2. ABSTRACT
The project aims to develop and implement a
machine learning-based solution for accurately
predicting prices of used cars. Leveraging a
diverse dataset encompassing key attributes
such as car make, model, manufacturing year,
mileage, and fuel type, the project focuses on
creating a robust predictive model using Linear
Regression.

3. WHY THIS PROJECT ?
problem
In used car market the prime concern is
about the accurate price of selling
through dealer
solution
By creating a machine learning model
it predicts the accurate selling price
based on car condition parameters,which
benefits both buyers and sellers

4. • The data was collected from a online car
selling platform Ex: Quikr
Data preprocessing steps:
• Cleaning, handling missing values, data
encoding
DATA SET
OVERVIEW
SOURCE OF DATA

5. MODEL
DEVELOPMENT
CHOICE OF ALGORITHM : LINEAR REGRESSION
• Linear Regression, employed in this project, is a supervised
machine learning algorithm used for predicting numerical
values, such as used car prices.
• It establishes a linear relationship between
independent variables like car make, year,
mileage, and the dependent variable, car
price,
• Later encoding categorical features,and
splitting the data into training and testing
sets

6. FLOW
Data collection Data cleaning training & test models
Linear Regression
model evaluation
user interface

7. MODEL
EVALUATION
Evaluation is done by R-squared Score
• This model achieved a strong R-squared score of 0.81 on the
test dataset.
• This score indicates that 81% of the
variability in used car prices can be
explained by the model.
• A higher R-squared score indicates a
better fit of the model to the data

8. USER INTERFACE A user interface can be designed

9. ADVANTAGES
• Empowers buyers and sellers in the used
car market by providing reliable price
estimations based on key car attributes
• Utilizes machine learning to accurately
estimate used car prices

10. • Empowers buyers and sellers in the used
car market by providing reliable price
estimations based on key car attributes
Utilizes machine learning to accurately
estimate used car prices
CONCLUSION

11. REFERENCES
• A Comparative Analysis of Machine
Learning Models for Used Car Price
Prediction IEEE research paper - by C Jin
• Scikit-learn documentation for details on
linear regression implementation

12. THANK YOU