women-perfume-sales-analysis

This repository contains a data analysis and visualization project using Python (Pandas) and Tableau. It explores sales trends, product availability, and key insights from an eCommerce dataset. The project includes data cleaning, EDA, and interactive dashboards for better business insights.

eCommerce Sales Analysis & Tableau Dashboards

Project Overview

This project focuses on analyzing sales data using Python (Pandas) for data cleaning and Tableau for visualization. The dataset includes product details, availability, pricing, and sales trends.

Steps in the Project

1. Data Cleaning & Preprocessing (Handled in Jupyter Notebook / Google Colab)

   • Removed null values
   • Converted lastUpdated column to datetime format
   • Processed price values and extracted useful insights

2. Exploratory Data Analysis (EDA)

   • Checked data distributions
   • Identified trends in product availability and sales

3. Visualization in Tableau

   • Sales Trend Over Time (Line Chart)
   • Top-Selling Products (Bar Chart)
   • Availability vs. Sales Performance (Heatmap)
   • Geographical Sales Insights (Map Visualization)

4. Dashboard Creation

   • Combined multiple visualizations into an interactive Tableau dashboard

How to Use This Repository

• The dataset and cleaned version are provided in the data/ folder
• Python scripts for data preprocessing are in the notebooks/ folder
• Tableau workbook (.twbx) file is included for visualization

Tech Stack

🔹 Python (Pandas, Matplotlib, Seaborn) – Data Cleaning & Analysis
🔹 Tableau – Data Visualization
🔹 Git & GitHub – Version Control

Setup Instructions

To run the Jupyter notebook:

git clone https://github.com/YOUR_GITHUB_USERNAME/YOUR_REPO.git
cd YOUR_REPO
pip install pandas matplotlib seaborn
jupyter notebook