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.
- Introduction
- Dataset Overview
- Exploratory Data Analysis (EDA)
- Conclusion
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 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.
- 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.
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.