Chirag Suri

Aspiring Data Analyst - Passionate about insights, dashboards, and the game of cricket.

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A complete end-to-end data generation, cleaning, and visualization project comparing two business years (2023 vs 2024) through custom synthetic data. Built entirely in Python (Jupyter Notebook) and visualized using Power BI — combining storytelling with interactivity using dynamic slicers, maps, and custom tooltips.

🛠️ Tools & Technologies Used

  • Python (pandas, Faker, NumPy) – Generated and cleaned 2023 & 2024 datasets using random logic and libraries.
  • Power BI – Designed interactive dashboards with city/state maps, slicers, donut charts, KPI cards, tooltips, and bar graphs.
  • Generative AI (ChatGPT) – Used as a co-pilot to debug scripts, organize logic, rewrite markdown, and brainstorm layout/design enhancements.

🔗 Links

GitHub LinkedIn Portfolio

📁 Dataset Description

This project doesn’t use real-world business sales data. Instead:

🔹 I first generated synthetic sales data for 2023 and 2024 using Python’s Faker and NumPy libraries.
🔹 Then saved them into two datasets:

🎯 Problem Statements / Goals

This project wasn’t just about revenue per year — it was about answering questions like:

  • 🛒 Are certain product categories more dominant by sales or by total orders?
  • 🏙️ Which cities contributed the most to overall revenue?
  • 📈 How do trends change month-by-month across KPIs like Sales, Orders, Quantity Sold?
  • 👤 Who are the top customers and how much are they contributing to our growth?
  • 🧭 What is the business growth direction comparing 2023 and 2024?

🔄 Project Workflow

Python (Jupyter Notebook)

  • Used Faker, random, and NumPy to simulate customer/product/order behaviour.
  • Created logic blocks for 2023 and 2024 (product-category mappings, ID formats, etc.)
  • Saved final outputs as .csv files for Power BI import.
View Notebook

Power BI Dashboard

Built an interactive dashboard with 3 report pages covering:

📄 Page 1: KPI Dashboard

  • 📌 KPI cards: Sales, quantity sold, orders, customers
  • 📈 Line charts: Month-wise trends
  • 🎯 Gauges: Year-end vs target KPIs

KPI Dashboard

📄 Page 2: Comparison Analysis

  • 📊 Sales by product category (bar)
  • 🧮 Orders by product category (donut)
  • 🗺️ City-wise sales (map)
  • 🧑 Top 100 customers by sales and quantity

Comparison Analysis

📄 Page 3: Custom Tooltip View

  • Tooltip with Qtr-wise breakdown
  • Appears on visual hover in dashboards

Tooltip Summary

Power BI Dashboard

💡 Key Insights

  • 📈 2024 saw a spike in both total customers and sales.
  • 🧢 Clothing & Electronics dominated in sales and orders.
  • 🗺️ San Francisco, New York, Houston led city-wise performance.
  • 💎 Top 100 customers made up over 60% of revenue.
  • 🎯 Most KPIs surpassed their original 2024 targets.

🚀 Things I Learned

  • How to create realistic synthetic datasets with Python.
  • Balancing logic complexity with data readability.
  • How to format and clean data to be dashboard-ready.
  • Designing Power BI dashboards that tell stories visually.
  • Using AI as a fast-thinking assistant in exploratory and design stages.

📦 How to Explore This Project

  1. 📥 Download the datasets from the repo.
  2. 🐍 Explore the logic in the Jupyter Notebook.
  3. 📊 Open and analyze the Power BI .pbix file.
  4. 🧭 Use filters/slicers in Power BI to explore dynamic trends.

🙏 THANK YOU

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