This repository contains the code and analysis for my data analysis project on stock price analysis and forecasting for my Internal attachment at Jomo Kenyatta University of Agriculture and Technology. The project analyzes historical stock price data, visualizes trends, and develops a forecasting model using Python and data science techniques.
The project is organized as follows:
Stock_price_analysis.ipynb: Jupyter Notebook containing the entire data analysis process, including data loading, data wrangling, exploratory data analysis (EDA), visualization, and stock price correlation.report.pdf: PDF file containing the report for the project for the IT and Computing department at Jomo Kenyatta University of Agriculture and Technology.data: Directory containing the stock price data used in the project.
To run the Jupyter Notebook and reproduce the analysis, you'll need the following:
- Python 3.x
- Jupyter Notebook
- Required Python libraries (pandas, numpy, matplotlib, seaborn, glob, etc.)
You can install the required libraries using the following command:
pip install -r requirements.txt- Clone this repository to your local machine:
git clone https://github.com/your-username/data-analysis-project.git
- Navigate to the project directory:
cd Stock_Price_Data_Analysis
- Launch the Jupyter Notebook
jupyter notebook Stock_price_analysis.ipynb
Follow the step-by-step instructions in the notebook to reproduce the data analysis process, including loading the dataset, performing data wrangling, conducting exploratory data analysis, and showing the stock price correlation visualization.
The dataset used in this project is sourced from the ALPHA VANTAGE API (https://www.alphavantage.co/) and comprises historical stock price data for companies listed on the New York Stock Exchange. The dataset spans from January 1, 2013, to December 31, 2018, providing a rich daily stock price information repository.
This project is licensed under the MIT License. See the LICENSE file for details.