https://github.com/sohomm/apple-inc.-stock-price-analysis-updated-
Apple Inc. stock price analysis , python , updated
https://github.com/sohomm/apple-inc.-stock-price-analysis-updated-
algorithm analysis epoch lstm model neural-network pattern prediction python yfinance
Last synced: 5 months ago
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Apple Inc. stock price analysis , python , updated
- Host: GitHub
- URL: https://github.com/sohomm/apple-inc.-stock-price-analysis-updated-
- Owner: SohomM
- Created: 2025-04-29T17:08:39.000Z (7 months ago)
- Default Branch: main
- Last Pushed: 2025-05-15T14:40:34.000Z (7 months ago)
- Last Synced: 2025-05-15T15:51:30.160Z (7 months ago)
- Topics: algorithm, analysis, epoch, lstm, model, neural-network, pattern, prediction, python, yfinance
- Language: Jupyter Notebook
- Homepage:
- Size: 148 KB
- Stars: 0
- Watchers: 1
- Forks: 0
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
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README
# Apple Inc. Stock Price Analysis
This project involves analyzing the historical stock prices of Apple Inc. (AAPL) using Python-based data analysis and visualization tools. It aims to uncover trends, perform statistical analyses, and provide insights into the stock’s performance over time.
## Project Objectives
- Collect and process historical stock data for Apple Inc.
- Perform exploratory data analysis (EDA)
- Visualize trends, patterns, and key metrics
- Compute moving averages and volatility indicators
- Conduct statistical summaries and comparisons over selected timeframes
- Optionally apply predictive modeling or time series forecasting
## Dataset
The historical stock price data for Apple Inc. is sourced from:
- Yahoo Finance (using `yfinance` or downloaded CSV)
Typical fields included:
- Date
- Open
- High
- Low
- Close
- Volume
- Adjusted Close
## Technologies Used
- Python
- Pandas for data manipulation
- Matplotlib and Seaborn for visualization
- NumPy for numerical computations
- yfinance or CSV files for data access
- Optional: scikit-learn or statsmodels for regression/forecasting
## Features
- Line plots of daily closing prices
- 20-day and 50-day moving averages
- Volatility calculation using standard deviation
- Volume trend analysis
- Yearly or quarterly return summaries
- Optional: Forecasting using ARIMA or Linear Regression
## How to Run
1. Clone the repository or download the code files.
2. Install the required packages:
pip install -r requirements.txt
3. Run the main analysis script:
python apple_stock_analysis.py
Make sure the data file (if using CSV) is placed in the specified path or that your internet connection is active if using `yfinance`.
## Folder Structure
- apple_stock_analysis.py - Main script
- data/ - Contains CSV files (if any)
- plots/ - Saved visualizations
- requirements.txt - List of dependencies
- README.md - Project documentation
## Future Enhancements
- Add interactive dashboards using Plotly or Streamlit
- Compare with competitor stocks (e.g., Microsoft, Google)
- Include sentiment analysis from news or social media
- Integrate real-time stock tracking
## License
This project is open for academic and personal use. Please check `LICENSE` file if included.