Exploratory Data Analysis

I've performed exploratory data analysis (EDA) on Black Friday Sales CSV files. I inspected the structure, calculated statistics, and visualized trends. This process aids in informed decision-making and strategy optimization.

Table of Contents

1. Introduction
2. Dataset Overview
3. Exploratory Data Analysis (EDA)
4. Conclusion

Introduction

This repository contains the Exploratory Data Analysis (EDA) conducted on Black Friday Sales data from one CSV dataset: train. The analysis aims to gain insights into pruchase patterns, trends, and factors influencing sales performance.

Dataset Overview

• Dataset Name: Black Friday Sales EDA
• Data Source: Kaggle
• Data Description:
  • Train Dataset: Historical pruchase data including Product ID, Gender, Age Group, Occupation, City, Number of Years in Current City, Marital Status, Product Categories and Purchase Amount.

Exploratory Data Analysis (EDA)

• Data Inspection: Check dataset structure, data types, and missing values.
• Summary Statistics: Calculate descriptive statistics for numerical variables.
• Data Visualization: Utilize visualizations like histograms, box plots, and time series plots to explore data distributions and trends.
• Feature Engineering: Create new features or transform existing ones to extract meaningful insights.
• Correlation Analysis: Examine relationships between variables.

Conclusion

The EDA provides valuable insights into Black Friday Sales data, including trends, patterns, and factors influencing sales performance. The findings can inform data-driven decision-making and optimization of business strategies to enhance sales efficiency and profitability.

For detailed analysis and code implementation, please refer to the Jupyter Notebook provided in this repository.