Retail Chain Data Analysis

This repository contains code and resources for analyzing retail chain data to gain insights and prepare it for further analysis and modeling. The main file in this repository, data_cleaning_preparation.ipynb, contains data cleaning and preprocessing steps for preparing the data for analysis.

Repository Contents

• data_cleaning_preparation.ipynb: Jupyter Notebook that performs data cleaning and preparation on the retail chain dataset.
• README.md: Guide for setting up the environment and running the notebook.

Prerequisites

To run this notebook, you'll need:

• Python (preferably 3.7 or later)
• Jupyter Notebook or JupyterLab
• The following Python packages (install them using pip if needed):
  • pandas
  • numpy
  • matplotlib
  • seaborn
  • scikit-learn (optional, if additional modeling is included)

You can install these packages with the following command:

pip install pandas numpy matplotlib seaborn scikit-learn

Running the Notebook
Clone the repository:

Clone this repository to your local machine:

bash
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git clone https://github.com/yourusername/retail-chain-data-analysis.git
cd retail-chain-data-analysis
Launch Jupyter Notebook:

Open Jupyter Notebook by running the following command in your terminal:

bash
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jupyter notebook
Open data_cleaning_preparation.ipynb:

In Jupyter Notebook, navigate to the cloned repository folder and open the data_cleaning_preparation.ipynb file.
Run the Notebook:

Step through each cell in the notebook to execute the data cleaning and preparation steps. Each section is documented to guide you through the process.