Project: AI-Driven Stock Market Forecasting #
The Challenge #
Predicting stock trends is like reading tea leaves—volatile and unpredictable!
The Solution #
I built a machine learning-powered dashboard that:
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📊 Visualizes real-time data with interactive candlestick charts
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🔮 Predicts trends using Linear Regression
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📈 Identifies optimal buy/sell points with RSI & Bollinger Bands
Key Insights #
- Strong Correlation: Google & Amazon stocks move together (0.95 correlation).
- Amazon’s Volatility: 12% daily swings require careful timing.
- ML Predictions: $1 Google ↑ → $0.95 Amazon ↑.
Tech Stack #
- Python, yfinance, Plotly, Scikit-learn
- Live updates via Yahoo Finance API
Explore the code and datasets on GitHub
The Data Alchemist: Transforming Raw Market Data into Actionable Insights #
Act 1: Hunting Live Data #
Live stock market data flows like a roaring river! Using the yfinance library:
- Extracted 3 years of historical data for Google (GOOG), Apple (AAPL), and Amazon (AMZN).
- First Insight: Amazon showed 12% higher volatility compared to others!
Act 2: Technical Enchantments #
I crafted two powerful spells to decode stock secrets:
Relative Strength Index (RSI): #
RSI > 70 → Overbought signal
RSI < 30 → Oversold signal
Bollinger Bands: #
Price touches upper band → Likely price drop
Price touches lower band → Likely price rally
Act 3: The Interactive Crystal Ball #
With Plotly, I built a magical dashboard:
Live candlestick charts with zoom/pan functionality.
Bollinger Bands in bold colors.
RSI displayed on a secondary axis.
Act 4: Unlocking Hidden Relationships #
The correlation matrix revealed:
Google & Apple: 0.87 correlation
Google & Amazon: 0.95 correlation → Twin trends!
Amazon& Apple: 0.83 correlation
Act 5: Machine Learning Prophecy #
With linear regression, I predicted:
$1 increase in Google → $0.95 rise in Amazon !
Multivariate model achieved 82% accuracy in forecasting Apple’s trends.
