📈 StockSense — Stock & Sales Analytics Dashboard

💹 StockSense is a powerful Power BI analytics dashboard designed to deliver deep financial intelligence on stock performance, sales trends, company financials, and customer behavior. Built around the Financial Intelligence Hub template, it transforms raw multi-source business data into interactive, decision-ready visualizations.

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📌 Overview

💡 StockSense gives businesses and analysts a centralized hub to monitor stock price history, track sales performance, analyze company financials, and understand customer spending patterns — all through a beautifully crafted Power BI dashboard powered by 4 real-world datasets.

🔗 GitHub Repository: tanishcode-12/StockSense

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✨ Features

• 📊 Interactive Charts — Drill-down visualizations for stock, sales, and company trends
• 💰 Financial Intelligence Hub — All-in-one Power BI template for financial analytics
• 🗃️ 4 Real-World Datasets — Stock prices, company financials, sales orders, and customer data
• 🔍 Filters & Slicers — Dynamic filtering by date, ticker, region, category, and segment
• 📸 Screenshots Included — Preview the dashboard before opening in Power BI
• 🧩 Reusable .pbit Template — Easily connect to your own data sources
• 📉 Sales Performance Tracking — Monitor revenue, quantity, discount, and profit
• 🏢 Company Financials — Analyze balance sheets, income statements, and key ratios
• 👥 Customer Insights — Segment customers by spending, credit, and payment behavior
• 📈 Stock Price History — Track OHLCV data across multiple tickers over time

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🗂️ Project Structure

📈 StockSense/
├── 📁 stocksense/
│   ├── 📊 Financial Intelligence Hub.pbit   # Power BI Template file
│   ├── 📁 data/
│   │   ├── 📄 company_data.csv              # Company financial statements
│   │   ├── 🗜️ compressed_data.csv.gz        # Historical stock price data (OHLCV)
│   │   ├── 📄 customer_data.csv             # Credit card customer behaviour data
│   │   └── 📄 sales_data.csv                # Retail sales orders data
│   └── 📁 screenshots/                      # Dashboard preview images
└── 📝 README.md

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⚙️ Setup

🧰 Prerequisites

• 🖥️ Power BI Desktop — Download here (Free)

🪜 Steps

1. 📥 Clone the repository

git clone https://github.com/tanishcode-12/StockSense.git
cd StockSense/stocksense

2. 🗜️ Extract the compressed dataset first

   ⚠️ Do this before opening Power BI! The stock price dataset is compressed and must be extracted first.

   gunzip data/compressed_data.csv.gz

   📂 This will extract compressed_data.csv into the data/ folder. Power BI will not load correctly without this step.

3. 📂 Open the Power BI Template

   • Launch Power BI Desktop
   • Go to File → Open → Select Financial Intelligence Hub.pbit
   • The template will load and prompt you to connect your data sources

4. 📊 Connect your data

   • Point Power BI to the files inside the data/ folder
   • Or replace with your own files matching the schema below
   • Click Load and the dashboard will populate automatically

5. 🔍 Explore the Dashboard

   • Use slicers to filter by ticker, date, region, segment, or category
   • Hover over charts for detailed tooltips
   • Export reports or visuals as needed

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📁 Data Files

📄 company_data.csv — Company Financial Statements

Annual financial data for publicly listed companies by ticker symbol.

📋 Column	📝 Description
Ticker Symbol	🏷️ Stock ticker (e.g. AAL, AAPL)
Period Ending	📅 Fiscal year end date
Total Revenue	💰 Total revenue earned
Cost of Revenue	🏭 Direct cost of goods/services sold
Gross Profit	📈 Revenue minus cost of revenue
Net Income	💵 Bottom-line profit after all expenses
Earnings Before Tax	📊 Profit before tax deductions
Earnings Per Share	🔢 Net income per outstanding share
Total Assets	🏦 Total company assets
Total Equity	🏛️ Shareholders' equity
Total Liabilities	📉 Total company liabilities
Long-Term Debt	🏗️ Debt due after one year
Cash and Cash Equivalents	💳 Liquid assets on hand
Capital Expenditures	🔧 Investment in fixed assets
Gross Margin	📐 Gross profit as % of revenue
Operating Margin	📐 Operating income as % of revenue
Profit Margin	📐 Net income as % of revenue
Current Ratio	⚖️ Liquidity ratio (current assets / liabilities)
Quick Ratio	⚖️ Stricter liquidity ratio
After Tax ROE	📊 Return on equity after tax
For Year	📅 Fiscal year of the record
Estimated Shares Outstanding	🔢 Total shares in the market

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🗜️ compressed_data.csv.gz — Historical Stock Prices (OHLCV)

Daily stock price and volume data across multiple tickers. Extract before use.

📋 Column	📝 Description
date	📅 Trading date
open	🔓 Opening price of the day
high	📈 Highest price of the day
low	📉 Lowest price of the day
close	🔒 Closing price of the day
volume	📦 Number of shares traded
Name	🏷️ Stock ticker symbol (e.g. AAL)

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📄 customer_data.csv — Credit Card Customer Behaviour

Customer-level credit card usage and payment behaviour data.

📋 Column	📝 Description
CUST_ID	🆔 Unique customer identifier
BALANCE	💳 Current credit card balance
BALANCE_FREQUENCY	🔁 How often balance is updated (0–1)
PURCHASES	🛒 Total purchases made
ONEOFF_PURCHASES	🛍️ Single large purchase amounts
INSTALLMENTS_PURCHASES	📦 Purchases made in installments
CASH_ADVANCE	💸 Cash withdrawn against credit
PURCHASES_FREQUENCY	🔁 Frequency of purchases (0–1)
ONEOFF_PURCHASES_FREQUENCY	🔁 Frequency of one-off purchases
PURCHASES_INSTALLMENTS_FREQUENCY	🔁 Frequency of installment purchases
CASH_ADVANCE_FREQUENCY	🔁 Frequency of cash advances
CASH_ADVANCE_TRX	🔢 Number of cash advance transactions
PURCHASES_TRX	🔢 Number of purchase transactions
CREDIT_LIMIT	🏦 Customer's credit limit
PAYMENTS	💰 Total payments made
MINIMUM_PAYMENTS	💵 Minimum payment amount made
PRC_FULL_PAYMENT	📊 % of months with full payment
TENURE	📅 Months as a customer

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📄 sales_data.csv — Retail Sales Orders

Order-level retail sales data across regions, categories, and customer segments.

📋 Column	📝 Description
Row ID	🔢 Unique row identifier
Order ID	🆔 Unique order identifier
Order Date	📅 Date the order was placed
Ship Date	🚚 Date the order was shipped
Ship Mode	📦 Shipping method used
Customer ID	🆔 Unique customer identifier
Customer Name	👤 Name of the customer
Segment	👥 Customer segment (Consumer / Corporate / Home Office)
Country	🌍 Country of the order
City	🏙️ City of delivery
State	🗺️ State of delivery
Postal Code	📮 Postal/ZIP code
Region	🗺️ Region (South / West / East / Central)
Product ID	🆔 Unique product identifier
Category	🏷️ Product category (Furniture / Technology / Office Supplies)
Sub-Category	🏷️ Product sub-category (e.g. Chairs, Phones)
Product Name	📦 Full product name
Sales	💰 Revenue from the order
Quantity	🔢 Number of units ordered
Discount	🏷️ Discount applied (0–1)
Profit	📈 Profit from the order

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📸 Screenshots

🖼️ Preview images of the dashboard are available in the screenshots/ folder.

stocksense/screenshots/

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🤝 Contributing

🙌 Contributions are welcome! Here's how you can help:

1. 🍴 Fork the repository
2. 🌿 Create a new branch (git checkout -b feature/your-feature)
3. 💾 Make your changes and commit (git commit -m 'Add your feature')
4. 📤 Push to the branch (git push origin feature/your-feature)
5. 🔁 Open a Pull Request

✅ Please make sure your changes are well-documented.

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👤 Author

Tanish — @tanishcode-12

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⭐ If you found StockSense helpful, consider giving it a star on GitHub!