📊 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.

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✅ 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.

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🧠 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.

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📈 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.

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📘 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.

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🔗 GitHub Repository

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

🔧 GitHub Repository Link

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