Tesla Stock Price Classification

Overview

This repository contains Python script and Jupyter notebook used to classify the daily change of Tesla (TSLA) stock closing price with a threshold 5%.

Project Structure

• Dataset

  • The dataset was obtained from https://www.kaggle.com/datasets/varpit94/tesla-stock-data-updated-till-28jun2021.

• notebook

  • Jupyter notebooks for data exploration, feature engineering, model selection, and evaluation.
  • 1.0-Data-Exploration.ipynb: Initial exploration of the dataset.
  • 2.0-Feature-Engineering.ipynb: Feature engineering including exponential moving averages, stochastic oscillator, and relative strength index.
  • 3.0-Model-Selection-Evaluation.ipynb: Model selection using logistic regression with cross-validation.

• script

  • Python script classify_tesla_stocks.py containing the ClassifyTeslaStocks class.

• README.md

  • This file providing an overview of the project, its structure, usage instructions, results, authorship, and licensing information.

Requirements

• Python 3.10
• Libraries: pandas, numpy, scikit-learn

Usage

1. Clone the repository:

   git clone https://github.com/yavuzemine/tesla-stock-classification.git
   

2. Install the required libraries:

   pip install -r requirements.txt
   

3. Execute the classification process:

   python src/classify_tesla_stocks.py
   

Results

The chosen model, logistic regression, achieved an ROC AUC score of approximately 0.95 on the test data. Feature importance analysis identified key indicators influencing the classification.

Author : Emine Yavuz

Contact: [email protected]

License : This project is licensed under the GNU General Public License v3.0. See the LICENSE file for details.