https://github.com/batthulavinay/apple-stock
This repository contains a Jupyter Notebook for Exploratory Data Analysis (EDA) on Apple stock data. The analysis leverages various Python libraries for data manipulation, visualization, and machine learning.
https://github.com/batthulavinay/apple-stock
Last synced: about 2 months ago
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This repository contains a Jupyter Notebook for Exploratory Data Analysis (EDA) on Apple stock data. The analysis leverages various Python libraries for data manipulation, visualization, and machine learning.
- Host: GitHub
- URL: https://github.com/batthulavinay/apple-stock
- Owner: BatthulaVinay
- Created: 2025-01-17T10:39:04.000Z (4 months ago)
- Default Branch: main
- Last Pushed: 2025-04-02T07:39:08.000Z (about 2 months ago)
- Last Synced: 2025-04-02T08:31:05.194Z (about 2 months ago)
- Language: Jupyter Notebook
- Homepage:
- Size: 2.43 MB
- Stars: 1
- Watchers: 1
- Forks: 0
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
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README
# Apple Stock Analysis
This repository contains a Jupyter Notebook for Exploratory Data Analysis (EDA) on Apple stock data. The analysis leverages various Python libraries for data manipulation, visualization, and machine learning.
## Features
- Comprehensive data exploration and visualization.
- Insights into stock trends and patterns.
- Application of machine learning models for predictive analysis.
## Getting Started
### Prerequisites
Could you make sure you have installed Python 3.8 or later? The required libraries are:
- `numpy`
- `pandas`
- `matplotlib`
- `seaborn`
- `xgboost`
- `sklearn`
You can install these dependencies with the following command:
```bash
pip install numpy pandas matplotlib seaborn xgboost scikit-learn
```
### Installation
1. Clone the repository:
```bash
git clone https://github.com/your-username/apple-stock-analysis.git
```
2. Navigate to the project directory:
```bash
cd apple-stock-analysis
```
3. Open the Jupyter Notebook:
```bash
Jupiter notebook "Apple stock.ipynb"
```
## Usage
Follow the steps in the notebook to:
1. Load and preprocess the data.
2. Visualize key trends using Matplotlib and Seaborn.
3. Apply machine learning models like SVM, Random Forest, and XGBoost.
4. Evaluate model performance using metrics provided by `sklearn`.
## Project Structure
- `Apple stock.ipynb`: Main notebook for analysis.
## Contributing
Contributions are welcome! Feel free to open an issue or submit a pull request.
## License
This project is licensed under the MIT License. See the `LICENSE` file for details