🏎️ Automotive Sales Analytics & Customer Segmentation Portfolio

📊 Project Overview

This project provides a comprehensive data-driven analysis of a global sales dataset (Classic Cars, Planes, Ships). It demonstrates a full data pipeline-from cleaning and normalization to advanced SQL querying and predictive business insights.

Dataset: Sample Sales Data (Kaggle) - 2,823 transactions covering the period 2003-2005.

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🖼️ Executive Dashboard Preview

If the interactive charts do not load, you can view the static preview above.

👉 View the Interactive Python Notebook (Direct Render)

🛠️ Tech Stack & Skills

Tool	Application
Python	Data cleaning (Pandas), Visualization (Plotly, Matplotlib, Seaborn)
SQL (SQLite)	Relational database modeling, CTEs, Window Functions, Complex Joins
Analytics	RFM Segmentation, Cohort Analysis, Sales Forecasting

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🚀 Key Achievements & Business Insights

1. Data Architecture

• Relational Modeling: Normalized raw data into a structured SQLite database (Products, Customers, Orders).
• Data Integrity: Cleaned missing values and standardized categorical fields for accurate reporting.

2. Strategic Analysis (Answering Business Questions)

• Market Dominance: Identified Classic Cars as the primary revenue driver, particularly in the USA, Spain, and France.
• Seasonality: Detected significant sales peaks in Q4 annually, driven by year-end promotions.
• Top Performers: Isolated "Euro Shopping Channel" as the lead customer ($912K+ Revenue).
• Customer Behavior: Performed RFM (Recency, Frequency, Monetary) segmentation to categorize loyal vs. at-risk customers.
• Retention: Created a Cohort Analysis Heatmap to track customer lifecycles and repeat purchase rates.

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📁 Repository Structure

• 📝 Sales_Dashboard.ipynb → Full Python code, data cleaning, and visualizations.
• 📊 sales_data_cleaned.csv → The processed and cleaned dataset used for analysis.
• 🗄️ sales_database.db → Normalized relational database file.
• 🖼️ monthly_sales_trend.png → Sales performance over time.
• 🖼️ cohort_analysis_heatmap.png → Customer retention visualization.
• 🖼️ dashboard.png → Screenshot of the final interactive dashboard.

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⚙️ How to Run

1. Clone this repository to your local machine.
2. Open Sales_Dashboard.ipynb in Google Colab or Jupyter Notebook.
3. Ensure the sales_data_cleaned.csv file is in the same directory.
4. Run all cells sequentially to generate the interactive visualizations.