Stock Price Prediction for Stock Market Forecasting using Machine Learning

Prediction of stock market returns is difficult because pricing changes often. The market is unpredictable and does not follow a clear pattern. This study builds a machine learning model to predict stock prices. The model analyzes past data to find patterns. It develops a model which helps forecast future stock movements. The model uses Support Vector Machines (SVM). It also uses Random Forest. After training, the model is tested for accuracy. Mean Absolute Percentage Error (MAPE) measures accuracy. Root Mean Squared Error (RMSE) is another measure to check accuracy. Time series analysis is also done in the research study along with machine learning algorithms. Low values of these two indicators shows that the models work well. They accurately predicts stock closing prices. A simple interface lets users view results easily. This research helps investors make better choices and reduce risks. All things considered, it is expected that the research will help develop accurate and reliable stock prediction models, which will help investors maximize their returns and reduce the risk associated with stock market investments.