Amazon Sales Data Visualization

This project focuses on analyzing and visualizing Amazon sales data to uncover insights through various data visualization techniques. The dataset contains 1,426,337 rows with a size of 18,542,381. Using libraries like matplotlib and seaborn, we performed extensive data analysis and visualizations, providing insights into different aspects of the sales data.

Project Overview

The project involves the following key steps:

1. Exploratory Data Analysis (EDA): Initial exploration of the dataset to understand its structure and identify missing values, outliers, and key features.
2. Pandas Profiling: Utilized the pandas profiling library to perform automated exploratory data analysis and generate a comprehensive report.
3. Data Visualization: Implemented various visualizations to present insights into:
   • Descriptive Analysis: Overview of key statistics of the data.
   • Comparative Analysis: Comparison of sales data across different categories.
   • Category-wise Insights: Insights into the sales performance of different product categories.
   • Monthly Analysis: Examination of sales trends over different months.
   • Top-N Items: Identifying top-selling items based on various metrics.
   • Correlation: Analyzing the relationships between different numerical variables.

Libraries Used

• matplotlib: For creating static, animated, and interactive visualizations.
• seaborn: For making attractive and informative statistical graphics.
• pandas: For data manipulation and analysis.
• pandas_profiling: For generating an EDA report.

Data Source

Kaggle - https://www.kaggle.com/datasets/asaniczka/amazon-products-dataset-2023-1-4m-products

Installation

To get started with the project, clone the repository and install the required dependencies:

git clone https://github.com/BhaveshBhakta/Amazon-Sales-Data-Visualization.git
cd Amazon-Sales-Data-Visualization

Results and Insights

The project provides valuable insights into the sales patterns and trends of products sold on Amazon, helping to understand:

• Which categories are performing the best.
• Sales trends over different months.
• The correlation between different features such as price, sales volume, and category.

Contributing

Feel free to fork this repository, contribute, or open issues for suggestions and improvements!