• J.Saiteja
• Ch.Bhavana
• D.saiteja
• P.Vinaya sree

Underwater Mine usage by the Naval Defense System provides great Security but also possesses a threat to the marine life and submarine vessels as the mines can be easily mistaken for rocks. So, we need a much more accurate system to predict the object as it is very dangerous if a mistake is made. To have a great accuracy we need more accurate data to generate accurate results. Our idea presents a method for prediction of underwater mines and rocks using Sonar Signals. Sonar Signals are used to record the various frequencies of underwater objects at 60 different angles. We constructed three binary classifier models according to their accuracy. Then, prediction models are used to predict the mine and rock categories. Python and Supervised Machine Learning Classification algorithms are used to construct these prediction models.

"PREDICTING SONAR ROCKS AGAINST MINES USING MACHINE LEARNING", International Journal of Emerging Technologies and Innovative Research (www.jetir.org), ISSN:2349-5162, Vol.9, Issue 11, page no.e344-e347, November-2022, Available :http://www.jetir.org/papers/JETIR2211457.pdf

"PREDICTING SONAR ROCKS AGAINST MINES USING MACHINE LEARNING", International Journal of Emerging Technologies and Innovative Research (www.jetir.org | UGC and issn Approved), ISSN:2349-5162, Vol.9, Issue 11, page no. ppe344-e347, November-2022, Available at : http://www.jetir.org/papers/JETIR2211457.pdf