Amazon Sales Data Analysis

This project provides an end-to-end exploratory and analytical workflow on simulated Amazon sales data, combining Python-based data analysis with interactive dashboards and comprehensive reports.

Contents

• Analyzing_Amazon_Sales_data.ipynb: Main Jupyter Notebook containing code for data loading, cleaning, analysis, and visualization.
• Analyzing_Amazon_Sales_data.html/Analyzing_Amazon_Sales_data.pdf: Rendered and exportable versions of the notebook for easy sharing or offline viewing.
• Amazon Sales data.csv: The core dataset (100 rows × 14 columns) containing sales records, regional sales, item categories, channels, pricing, revenues, costs, profits, and order dates.
• Amazon Sales dashboard.pbix: Power BI report offering an interactive sales dashboard based on the CSV data.
• Amazon Sales dashboard pdf.pdf: Exported PDF of the Power BI dashboard summarizing sales KPIs.

Workflow & Analysis Steps

1. Data Import & Cleaning

   • Loads the sales CSV.
   • Converts columns (dates, numerics) to appropriate types.
   • Detects and fills missing values in Total Cost.
   • Ensures dataset integrity for analysis.

2. Exploratory Data Analysis (EDA)

   • Inspects data head, types, and summary statistics.
   • Generates heatmaps to verify data completeness.
   • Describes distribution of regions, channels, item types, and priorities.

3. Business Analytics & Insights

   • Highest/lowest revenue, profit, and units sold by region, country, and item type.
   • Detects top/bottom-performing categories and geographies.
   • Analyzes impact of sales channels on order priorities.
   • Measures order processing times and their variation by channel/country.
   • Calculates monthly trends and seasonality in revenue.

4. Advanced Analysis

   • Correlation between unit price and total profit.
   • Outlier detection in cost using boxplots and IQR.
   • Relationship exploration between units sold and profit.

5. Visualization

   • Multiple chart types: line plots, bar plots, pie charts, heatmaps, boxplots, scatter plots.
   • Power BI dashboard for interactive exploration and executive reporting.

Getting Started

• Open the Jupyter notebook to reproduce and modify the Python analysis.
• Launch the PBIX file in Power BI Desktop for advanced dashboarding.
• Use the PDF/HTML summary files for sharing insights.

Key Technologies

• Python: pandas, matplotlib, seaborn (for EDA & visualization)
• Power BI: Interactive dashboards & PDF exports
• Jupyter Notebook: Analysis scripting & documentation

Example Insights

• Top Region by Revenue: Sub-Saharan Africa
• Top Country by Profit: Djibouti
• Highest Selling Item Type: Cosmetics
• Fastest Order Processing (Channel): Offline vs Online comparison
• Order Priority Patterns: Distribution across regions and channels

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Note: This project is for educational and analytical demonstration, utilizing a simulated dataset.

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