How can machine learning models predict future stock prices?

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Supervised and Unsupervised Learning are two primary types of machine learning, differing mainly in hThe primary goal of a data science project is to extract actionable insights from data to support better decision-making, predictions, or automation—ultimately solving a specific business or real-world problem.

Machine learning (ML) models predict future stock prices by analyzing historical data, identifying patterns, and making probabilistic forecasts based on various factors. While no model can predict stock prices with complete certainty due to market volatility and external influences, ML enhances predictive accuracy by processing complex datasets. Here’s how ML models achieve this, within a 1500-character limit, connecting to the analytical skills of business analysts (BAs) and the collaborative environment fostered by Scrum Masters:

1. Data Collection: ML models use historical stock data (prices, volumes), economic indicators (GDP, interest rates), and alternative data (news sentiment, social media like X posts). BAs often help define relevant data sources.
2. Feature Engineering: Models extract features like moving averages, volatility, or technical indicators (RSI, MACD). BAs with analytical skills may assist in selecting features that align with market trends.
3. Model Selection: Common ML models include:
   • Regression Models (e.g., Linear Regression, Random Forests) predict price values.
   • Time-Series Models (e.g., ARIMA, LSTM) capture temporal patterns.
   • Deep Learning (e.g., Neural Networks) handles complex, non-linear relationships. Scrum Masters ensure teams, including data scientists and BAs, collaborate efficiently to select models.
4. Training and Testing: Models are trained on historical data, with a portion reserved for testing accuracy. BAs may validate outputs against business goals.
5. Sentiment Analysis: Natural Language Processing (NLP) analyzes news or X posts to gauge market sentiment, improving predictions. Scrum Masters resolve data access issues.
6. Risk Assessment: Models incorporate risk factors (e.g., market volatility). BAs help interpret outputs for stakeholders.
7. Continuous Learning: Models are retrained with new data to adapt to market changes, with Scrum Masters facilitating iterative sprints.

Despite their power, ML models face challenges like overfitting or unexpected market events. They provide probabilities, not certainties, and work best for short-term predictions. BAs and Scrum Masters support by ensuring data quality and team coordination.

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