Skip to content

README.md

Latest commit

 

History

History
78 lines (50 loc) · 2.99 KB

README.md

File metadata and controls

78 lines (50 loc) · 2.99 KB

📊 Sales Performance Analysis — Data Analytics & Visualization Project

This project presents a comprehensive analysis of 2023 sales data, demonstrating key skills in data analytics, business intelligence (BI) reporting, and interactive visualization using Python.

✅ Project Objectives

  • Analyze monthly sales performance and trends.
  • Identify best-selling products across countries.
  • Enable dynamic, interactive exploration of sales insights.
  • Deliver actionable insights for data-driven decisions.

🧠 Skills Demonstrated

🧹 Data Analytics & Processing

  • Data Import & Inspection

    • Loaded CSV datasets using pandas and reviewed structure via .head() and .info().

  • Data Cleaning & Transformation

    • Converted Date columns to datetime format using pd.to_datetime() for time-based operations.

  • Aggregation & Grouping

    • Aggregated monthly sales using .groupby() and .sum() on relevant fields.
    • Grouped by Country and Product ID to evaluate market-specific product performance.

  • Feature Engineering

    • Mapped numeric month values to full month names for clarity in visualizations.

  • Sorting & Ranking

    • Identified Top 5 Products per Country using grouped sorting and filtering operations.

📈 Data Visualization & BI Reporting

  • Visualization Libraries Used

    • Matplotlib, Seaborn, Plotly Express, Plotly Graph Objects

  • Interactive Time-Series Plots

    • Visualized monthly trends in sales, tax, and quantity ordered using interactive line charts.

  • Stacked Bar Charts

    • Compared product performance across countries with stacked bar visualizations.

  • Dropdown-Based Dynamic Filtering

    • Enabled region-specific insights via dropdown menus using Plotly.

  • Automated Per-Country Charts

    • Created dynamic bar charts per country to visualize top products.

  • Customization for Clarity

    • Applied layout adjustments (titles, axis labels, legends, plot sizing) for presentation-readiness.

📘 Business Intelligence Insights

Insight Area Description
📆 Seasonal Trends Revealed peaks in sales activity mid-year, indicating seasonality patterns.
🌍 Regional Preferences Certain products performed significantly better in specific countries.
🏆 Top Products Identified top-selling items per country to aid in targeted strategies.
🎛 Interactive Dashboards Dynamic filters enabled granular analysis by stakeholders.
📊 Data Storytelling Logical structure from overview to detailed insights ensured clarity.

🔗 GitHub Repository

Explore the full notebook, source code, and interactive visualizations:

🔧 GitHub Repository Link