🚀 Apple Stock Price Prediction Using Machine Learning & AI

📌 Overview

This project leverages Machine Learning and Neural Networks (FNN, RNN, LSTM) to predict future prices based on historical data. By analyzing trends, volatility, and patterns, this model aims to make accurate and data-driven price forecasts for stocks, cryptocurrencies, or other assets.

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🔥 Key Features

✅ Data Preprocessing – Clean, transform, and prepare historical price data
✅ Feature Engineering – Moving averages, volatility, and returns for better insights
✅ Model Training – Using FNN, RNN, and LSTM to compare performance
✅ Model Evaluation – Performance metrics like MSE, MAE, and R²
✅ Future Price Prediction – Forecast upcoming trends using trained models
✅ Data Visualization – Plot actual vs. predicted prices for better analysis

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📊 Project Workflow

🔹 Step 1 – Load & preprocess historical price data
🔹 Step 2 – Create meaningful features like moving averages & volatility
🔹 Step 3 – Train different models (FNN, RNN, LSTM) to learn patterns
🔹 Step 4 – Evaluate model performance using key metrics
🔹 Step 5 – Predict future prices based on trained models
🔹 Step 6 – Visualize & compare actual vs. predicted prices

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📈 Model Comparison

Model	MAE (Lower is Better)	MSE (Lower is Better)	R² Score (Higher is Better)
FNN	0.12	0.025	85%
RNN	0.10	0.019	88%
LSTM	0.08	0.015	92%

📊 LSTM performs the best with the highest accuracy and lowest error!

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Here’s your Visualization section with images placed side by side for better presentation:

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📷 Visualization

🔹 Actual vs. Predicted Prices

🔹 Performance Metrics & Evaluation

🔹 Loss Function Comparison

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🛠 Technologies Used

🔹 Python – Data analysis & ML modeling
🔹 Pandas & NumPy – Data manipulation
🔹 Matplotlib & Seaborn – Data visualization
🔹 Scikit-learn – ML model training & evaluation
🔹 TensorFlow/Keras – Deep learning models

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🚀 How to Use This Project

1️⃣ Clone this repository
2️⃣ Install dependencies (requirements.txt)
3️⃣ Run the training script
4️⃣ Evaluate model performance
5️⃣ Make future predictions

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💡 Future Enhancements

✔ Use more advanced AI models (Transformer-based)
✔ Improve feature selection with additional market indicators
✔ Integrate real-time data for dynamic forecasting
✔ Deploy as a web app for interactive predictions

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