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Retail Analytics Deep Dive

Retail Analytics Deep Dive is a comprehensive, end-to-end data analytics project that leverages real-world e-commerce data to extract actionable business insights. The project combines advanced Python data processing and exploratory data analysis techniques with dynamic, interactive Tableau dashboards to visualize customer segmentation, sales performance, and product trends. It demonstrates expertise in cleaning and transforming data, performing sophisticated customer segmentation using RFM analysis, and creating executive-level dashboards tailored for business decision-making.

This project highlights the ability to translate complex datasets into clear, impactful visual stories that drive strategic business outcomes.

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  • Python (Pandas, NumPy, Matplotlib, Seaborn): For data cleaning, transformation, and exploratory data analysis.

  • Google Colab: Cloud-based environment for interactive Python coding and sharing notebooks.

  • Tableau: For designing and publishing advanced interactive dashboards that provide multi-dimensional insights into customer behavior and product performance.

  • GitHub: Version control and project documentation.

Tools & Technologies Used

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Role & Responsibilities

  • Led the end-to-end data analytics process including data cleaning, preprocessing, and feature engineering using Python in Google Colab.

  • Conducted comprehensive exploratory data analysis (EDA) to identify sales trends, customer behavior, and product performance patterns.

  • Developed customer segmentation using Recency, Frequency, and Monetary (RFM) analysis to categorize customers for targeted marketing strategies.

  • Designed and built interactive Tableau dashboards to visualize key business metrics and enable data-driven decision making.

  • Managed project version control and documentation using GitHub to ensure reproducibility and transparency.

Key Learnings

  • Strengthened skills in advanced data manipulation and analysis with Pandas and NumPy.

  • Gained practical experience in customer segmentation techniques and their business applications.

  • Mastered dashboard design principles and best practices to communicate insights effectively in Tableau.

  • Enhanced ability to translate complex data into clear, actionable visual narratives for stakeholders.

  • Improved project organization and collaborative coding workflows using Google Colab and GitHub.

Links

Github: Retail-Analytics-Deep-Dive

Tableau: Analytics Dashboards

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