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Readme for Tesla and GameStop Stock Analysis with Python

This repository contains a Jupyter notebook analyzing the historical performance of Tesla (TSLA) and GameStop (GME) stocks, completed as part of IBM's Python for Data Science course.

Caution : Download The Final Assignment.ipnby and follow the below mentioned steps to see the final outputs(graphs)

If not then the graphs in .png format are provided in the repo

Project Overview

This project explores the following aspects of both companies:

  • Data acquisition: Retrieving historical stock price and market data using Python libraries.
  • Data cleaning and wrangling: Preparing the data for analysis by handling missing values and outliers.
  • Exploratory data analysis (EDA): Investigating trends, patterns, and relationships within the data using visualizations and statistical measures.

Jupyter Notebook

The main analysis is contained in the stock_analysis.ipynb Jupyter notebook. The notebook is divided into sections explaining each step of the workflow:

  • Import libraries and data acquisition: Importing necessary libraries and retrieving historical data from a reliable source.
  • Data cleaning and preparation: Cleaning and manipulating the data to ensure its quality for analysis.
  • Exploratory data analysis: Utilizing various visualizations and statistical tests to understand the data.
  • Comparative analysis: Comparing the performance of both stocks across different metrics.

Requirements

To run the Jupyter notebook, you will need:

  • Python 3.x
  • Jupyter Notebook
  • Required libraries: pandas, numpy, matplotlib, seaborn, yfinance (or similar data source library)

Installation and Usage

  • Clone this repository to your local machine.
  • Install the required libraries using pip install -r requirements.txt (if not already installed).
  • Open the Final Assignment.ipynb notebook in Jupyter Notebook.
  • Follow the instructions and code cells within the notebook to run the analysis.

Contributing

Contributions and feedback are welcome! Feel free to create pull requests or open issues to suggest improvements, share additional insights, or report any problems.

Additional Notes

  • Please note that this analysis is for educational purposes only and should not be considered financial advice.
  • The data used in this project is historical and may not reflect future performance.
  • Feel free to adapt and expand the analysis as you please, using this as a starting point for your own research.