Stock Price Prediction using LSTM for Apple Inc. by Ariya BirjandiStock Price Prediction using LSTM for Apple Inc. by Ariya Birjandi

Stock Price Prediction using LSTM for Apple Inc.

Ariya Birjandi

Data Scientist

Matplotlib

Python

scikit-learn

Investment & Asset Management

Stock Price Prediction using LSTM

Description

This project predicts the stock price of Apple Inc. (AAPL) using a Long Short-Term Memory (LSTM) neural network. The model is trained on historical stock data fetched using the Yahoo Finance API.

Technologies Used

Python

Yahoo Finance (yfinance)

NumPy

Pandas

Matplotlib

Scikit-learn

TensorFlow/Keras

Features

Fetches historical stock price data using Yahoo Finance

Preprocesses and scales data for training

Uses LSTM layers to train a model for predicting stock prices

Visualizes real vs. predicted stock prices

Saves the trained model for future use

Installation

Clone the repository:

git clone https://github.com/yourusername/stock-price-prediction.git
cd stock-price-prediction

Install dependencies:

pip install -r requirements.txt

Usage

Run the script to fetch data, train the model, and visualize predictions:

python stock_price_prediction.py

File Structure

stock_price_prediction.py - Main script for fetching data, training the model, and plotting predictions.

stock_price_forecasting_model.h5 - Saved LSTM model after training.

requirements.txt - List of required Python libraries.

Results

The model predicts stock prices and plots real vs. predicted values for visualization.

License

This project is licensed under the MIT License.

Contribution

Feel free to fork, contribute, or suggest improvements!

Author