/Boxify_Sales_Analysis_and_Inventory_Insights---using_PYTHON

Sales data analysis using Excel and Python to identify key performance metrics and optimize business operations.

Primary LanguageJupyter Notebook

Boxify Sales Analysis and Inventory Insights Project

This project involves analyzing the Boxify dataset to gain insights into sales trends and inventory performance. The analysis aims to provide actionable recommendations for optimizing inventory management practices.

Table of Contents

Introduction

The Boxify Sales Analysis and Inventory Insights Project focuses on leveraging data analysis techniques to extract valuable insights from sales data. By understanding sales trends and inventory performance, businesses can optimize their inventory management practices to reduce costs and enhance operational efficiency.

Dataset

The dataset used for this project is stored in the Boxify Dataset - Data Analyst Bootcamp.csv file. It contains information about orders, products, sales performance, and inventory levels.

Project Files

  • Boxify.docx: This Word document provides detailed project requirements, objectives, and tasks. It outlines the scope of the analysis and the expected deliverables.
  • Boxify_Sales Analysis.ipynb: This Jupyter Notebook contains code and analysis related to the Boxify sales dataset. It includes data preprocessing, exploratory data analysis (EDA), and visualization techniques used to gain insights into sales trends and inventory performance.

Analysis Overview

The analysis consists of the following steps:

  1. Data Collection and Preprocessing: Reading the dataset and cleaning the data to handle missing values and inconsistencies.
  2. Exploratory Data Analysis (EDA): Analyzing sales trends, top-selling products, categories, and stock levels to identify insights into sales patterns and inventory performance.
  3. Inventory Metrics Calculation: Calculating inventory turnover, stock-to-sales ratio, and reorder point to assess inventory performance.
  4. Visualization: Creating visualizations such as line charts, bar plots, and histograms to present the analysis findings visually.

Usage

To run the analysis:

  1. Ensure you have Python installed on your system.
  2. Install the required dependencies by running: 
    pip install -r requirements.txt
  3. Run the Jupyter Notebook Boxify_Sales Analysis.ipynb to execute the analysis and visualize the results.

Contributing

Contributions to this project are welcome. If you find any bugs, have suggestions for improvements, or want to contribute new features, please open an issue or submit a pull request on GitHub.