Stock Predictor is a machine learning model designed to forecast the future price of a selected stock based on its historical data. This project utilizes advanced techniques from the field of machine learning to analyze and predict short-term price movements in the stock market.
Historical Data Analysis: The Stock Predictor model leverages a comprehensive dataset containing ten years of historical stock market data. It processes this information to identify patterns, trends, and relationships that can be used to make accurate predictions.
Machine Learning Algorithms: The model employs state-of-the-art machine learning algorithms, such as recurrent neural networks (RNNs) or long short-term memory (LSTM) networks, to learn from historical stock data and capture intricate temporal dependencies. These algorithms are well-suited for time series prediction tasks and can effectively capture the inherent complexities of stock price dynamics.
Feature Engineering: Stock Predictor incorporates various techniques for feature engineering to enhance the predictive power of the model. These techniques may include calculating technical indicators (e.g., moving averages, relative strength index), extracting relevant financial news sentiment, or incorporating macroeconomic indicators.
Model Training and Validation: The Stock Predictor model undergoes a robust training and validation process to optimize its performance. The dataset is split into training and validation sets, allowing the model to learn from historical data while being evaluated on unseen examples to ensure its predictive capabilities.