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:

pip install numpy pandas matplotlib seaborn xgboost scikit-learn

Installation

1. Clone the repository:

   git clone https://github.com/your-username/apple-stock-analysis.git

2. Navigate to the project directory:

   cd apple-stock-analysis

3. Open the Jupyter Notebook:

   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