This project performs Exploratory Data Analysis (EDA) on the Superstore Sales Dataset to identify sales trends, profit patterns, and business insights.
The goal is to understand which factors drive revenue and profit — such as region, product category, and discounts — using Python data analysis tools.
- Source: Kaggle – Superstore Dataset
- Attributes include:
- Order Date, Ship Date, Sales, Quantity, Discount, Profit
- Customer, Region, Segment, Category, Sub-Category
- Python
- Pandas, NumPy – data cleaning & transformation
- Matplotlib, Seaborn – data visualization
- Jupyter Notebook – analysis & presentation
- Perform data cleaning and handle missing values
- Conduct descriptive analysis on sales and profit distribution
- Identify top-performing product categories and regions
- Analyze impact of discounts on profits
- Visualize sales and profit trends over time
- The West region contributed the highest overall profit.
- Technology category had the largest sales share.
- High discounts in Furniture led to lower profit margins.
- Monthly trends showed consistent spikes during year-end sales.
- 📦 Bar chart of top 10 profitable sub-categories
- 🌡️ Heatmap of correlation between sales, profit, and discounts
- 📈 Line plot of sales over time
- 🥧 Pie chart of sales share by region
- Clone the repository
- Install dependencies
- Open the notebook
- Data cleaning & transformation
- Exploratory data analysis
- Business intelligence insights
- Visualization & storytelling
- Build interactive dashboard using Plotly or Streamlit
- Automate regional sales reports
- Predict profit margins using regression models
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