• Disha Vilas Gaikwad
• Jayaprabha M.Kanase

The nature of stock request movement has always been nebulous for investors because of colorful influential factors. This study aims to significantly reduce the threat of trend vaticination with machine literacy and deep literacy algorithms. Three stock request groups, videlicet, NIFTY FIN SERVICE, NIFTY IT, NIFTY METAL, are chosen for experimental evaluations. This study compares nine machine literacy models( Decision Tree, Random Forest, Adaptive Boosting( Adaboost), eXtreme Gradient Boosting( XGBoost), Support Vector Classifier( SVC), Naive Bayes, K- Nearest Neighbors( KNN), Logistic Retrogression and Artificial Neural Network( ANN)) and two important deep literacy styles intermittent Neural Network( RNN) and Long short- term memory( LSTM). Ten specialized pointers from ten times of literal data are our input values, and two ways are supposed for employing them. originally, calculating the pointers by stock trading values as nonstop data, and secondly converting pointers to double data before using. Each vaticination model is estimated by three criteria grounded on the input ways. The evaluation results indicate that for the nonstop data, RNN and LSTM outperform other vaticination models with a considerable difference. Also, results show that in the double data evaluation, those deep literacy styles are the stylish; still, the difference becomes lower because of the conspicuous enhancement of models' performance in the alternate way.

Machine Learning, Stock Market, stock price, LSTM, RNN, Support vector machine, Random Forest, Linear Retrogression.

"Review on Stock Price Prediction Using LSTM and RNN Models", International Journal of Emerging Technologies and Innovative Research (www.jetir.org), ISSN:2349-5162, Vol.12, Issue 4, page no.l664-l675, April-2025, Available :http://www.jetir.org/papers/JETIR2504B83.pdf

"Review on Stock Price Prediction Using LSTM and RNN Models", International Journal of Emerging Technologies and Innovative Research (www.jetir.org | UGC and issn Approved), ISSN:2349-5162, Vol.12, Issue 4, page no. ppl664-l675, April-2025, Available at : http://www.jetir.org/papers/JETIR2504B83.pdf