Stock Price Prediction using Machine Learning

This repository contains code for predicting stock prices using machine learning techniques, focusing on Tesla's stock as an example.

Overview

The project aims to predict future stock prices by leveraging historical stock data and machine learning algorithms. It includes data preprocessing, model training, evaluation, and visualization.

Getting Started

Prerequisites

Before running the code, make sure you have Python installed on your system. You can download and install Python from the official website: Python Downloads.

Installation

1. Clone the repository to your local machine:

   git clone https://github.com/your-username/stock-price-prediction.git

2. Navigate to the project directory:

   cd stock-price-prediction

3. Install the required Python dependencies:

   pip install -r requirements.txt

Usage

1. Data Preprocessing: Run the stock_prediction.ipynb Jupyter Notebook to preprocess the data, including cleaning, feature engineering, and formatting.

2. Model Training: Train machine learning models using the prepared data. The notebook includes code for training a linear regression model.

3. Visualization: Visualize historical stock prices, predicted values, and model performance using the stock_prediction.ipynb notebook.

Files

• tesla.csv: Historical stock data for Tesla.
• stock_prediction.ipynb: Jupyter Notebook containing the code for data preprocessing, model training, and visualization.
• README.md: This file.

Technologies Used

• Python
• Pandas
• NumPy
• Matplotlib
• Plotly
• scikit-learn

Contributing

Contributions to this project are welcome! If you find any issues or have suggestions for improvements, please feel free to open an issue or create a pull request.

License

This project is licensed under the MIT License - see the LICENSE file for details.