Prediction of Mobile Model Price using Machine Learning Techniques
Kumuda S1, Vishal Karur2, Karthick Balaje S E.3

1Kumuda*, Department of Electronics and Communication, NIE Institute of Technology, Mysore (Karnataka), India.
2Vishal Karur, Department of Electronics and Communication, JSS Academy of Technical Education, Bengaluru (Karnataka), India.
3Karthick Balaje S E., Department of Electronics and Communication, Karunya Institute of Technology and Sciences, Coimbatore (Tamil Nadu), India.

Manuscript received on October 20, 2021. | Revised Manuscript received on October 30, 2021. | Manuscript published on October 30, 2021. | PP: 273-275 | Volume-11 Issue-1, October 2021. | Retrieval Number: 100.1/ijeat.A32191011121 | DOI: 10.35940/ijeat.A3219.1011121
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Abstract: Mobile phone has become a common commodity and usually the most common purchased item. Thousands of types of mobiles are released every year with new features and new specification and new designs. So the real question is prediction is that what is the real price of the mobile and to estimate the price of the mobile within the market for optimal marketing and successful launch of the product. Price has become a major factor for development of any product and its sustainability in the market. Mobile prices also impact the marketing of the mobile and also its popularity with other competitors. With the available specifications and desired designs, money is also an important factor to survive within the market. Customer usually sees that they are able to buy with the specification with the given estimated price or not. So to estimating the price is an important factor before releasing the mobile and also to know about the market and competitors. In this Prediction, Dataset is collected from the existing market and different algorithms are applied to reduce the complexity and also identify the major selection features and get the best comparison within the data. This Tool is used to find the best price with maximum specifications. 
Keywords: Machine Learning, Data Collection, Forward Selection, Backward Selection.
Scope of the Article: Machine Learning