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1. DEPARTMENT OF MECHANICAL
Engineering
Vehicle system
Aldel Education Trust’s
St. John College of Engineering and Management, Palghar
NAAC Accredited with Grade A (2017-2022)
Aditya Patel | 46
Arpita Pitroda|

2. Introduction
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• Price is the most effective attribute of marketing and business. All the
costumers are first worried and thinks “If he would be able to purchase
something with given specifications or not”
• Different type of feature selection algorithms are available to select only
best features and minimize dataset . Many of the features are very
important to be considered to estimate price of mobile.
• There is list of many features based upon those, mobile price is decided.
So we will use many of the mentioned features to classify whether the
mobile would be very economical, economical, and expensive or very
expensive .

3. Motivation
•When any customer buys a mobile they always think that whether they are
paying the right price considering the features of the mobile
•The customer always want to know whether that particular mobile is
overrated or underrated
•These problems faced by the customer motivated us to
do this project
3

4. Problem Statement
• The very first question of customer is about the price of items. All the
customers are first worried and thinks “If he would be able to purchase
something with given specifications or not”.
• To determine the best features that affect the price of mobiles
• To develop an cost effective and easy to use solution such that even non
technical background people can use it.
4

5. Objectives
• To get the dataset consisting of all the specifications of the mobile such as
battery timing, camera, ram, video quality etc.
• To perform operations on the dataset using one of the feature selection
algorithms such as Decision tree, Random forest ,select k best
• To perform operations on the selected attributes in the dataset using
classification algorithms such as random forest, decision tree etc.
• To show that what is the price of the mobile considering the various
features of the mobile.
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6. Scope
• Cost prediction is the very important factor of marketing and business. To
predict the cost same procedure can be performed for all types of products
for example Cars, Foods, Laptops etc.
• So products can be compared in terms of their specifications, cost,
manufacturing company etc. 7.3 By specifying economic range a good
product can be suggested to a costumer.
• More sophisticated artificial intelligence techniques can be used to
maximized the accuracy and predict the accurate price of the products. 8.2
Software or Mobile app can be developed that will predict the market price
of any new launched product
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7. Review of Existing Literature
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o
Sr.No Sr.No Sr.No
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o
Sr.No Sr.No Sr.No
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o
Sr.No Sr.No Sr.No

8. Proposed System
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Fig. 1: Block diagram of the proposed system
Preprocessing Data
Tree Logistic
Regression
Gradient
Classifier
KNN Classifier
Random
Forest and
Decision tree
Feature Selection
Predicited Price

9. Implementation
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We have implemented these projects using following algorithms
•1)Feature selection algorithm
•It consists of three methods
•i)Filter based
•ii)Wrapper based
•Iii)Embedded
•We will select the best method which will give us the relevant features
•For classification we will use KNN algorithm

10. Technologies Used
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• Software Requirements
– Python 3.0 or above
– Windows 7 or above
• Hardware Requirements
– Processor: Intel Core 2 duo
– Memory: 1GB RAM
– Hard Disk: no such requiremnts

11. Results and Discussion
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• Pending……

12. Conclusion and Future Work
12
• Pending…

13. References
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1.Mobile Price prediction using Machine Learning Techniques B.Balakumar1 ,
P.Raviraj2 , V.Gowsalya3 1Assistant Professor, Centre for Information Technology
and Engineering, Manonmaniam Sundaranar University, Tirunelveli, India.1
balakumarmsu@gmail.com
1.Mobile Price Class prediction using Machine Learning Techniques by Muhammad
Asim UET Lahore Pakistan Zafar Khan UET Lahore Pakistan

14. Thank You !!!
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15. Q & A
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