Article
STOCK PRICE PREDICTION USING MACHINE LEARNING TECHNIQUES
Stock price prediction has become an important research area in financial analytics due to the increasing availability of historical market data and advancements in machine learning techniques. The objective of this project is to develop an intelligent system that analyzes past stock market trends and predicts future price movements with improved accuracy. The proposed approach utilizes data preprocessing, feature engineering, and predictive modeling algorithms such as Linear Regression, Random Forest, and Long Short-Term Memory (LSTM) networks to capture both statistical patterns and temporal dependencies in stock data. Historical price data, including open, high, low, close, and trading volume, are used as input features to train the model. The system aims to assist investors and financial analysts in making informed decisions by providing trend forecasts and risk insights. Performance evaluation is carried out using metrics such as Mean Squared Error (MSE) and accuracy comparisons across different models. The results demonstrate that machine learning–based methods can effectively identify hidden patterns in financial markets, offering a scalable and data-driven solution for stock price prediction while reducing manual analysis effort.
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