Sales Analysis and Optimizing Business Strategy with Power BI

🚀 Project Overview

This project leverages data collection, SQL preprocessing, and Power BI to deliver in-depth sales analytics and performance optimization. It includes web-scraped news data, sales trends, and insights into Power BI performance using the Performance Analyzer.

🎯 Objectives

1. Data Integration: Combine dataset-based and web-scraped data sources.
2. Data Cleaning & Preprocessing: Perform preprocessing directly in SQL for structured analysis.
3. Sales Analysis: Explore sales trends across categories, segments, regions.
4. Power BI Performance Tuning: Use Performance Analyzer to optimize report speed and responsiveness.
5. Interactive Dashboards: Build visually rich and interactive Power BI dashboards.
6. Comprehensive Documentation: Maintain clear documentation for every step of the process.

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📁 Project Structure

1. Collecting the Data/

• Collected Data from:
  • Historical Sales Dataset
  • Web-scraped news articles relevant to market trends
• Converted raw data into structured format using Pandas
• Uploaded structured data to SQL database

2. Cleaning and Preprocessing/

• Conducted data cleaning and transformation in SQL.
• Removed missing values, standardized data types, and normalized features.
• Exported cleaned data for downstream analytics.

3. Mining and Analysing/

• Built Power BI visualizations:
  • Sales by Region, Segment, Category
  • Trend over time (monthly, yearly)
  • Profit and Quantity distribution
  • Heatmaps and Tree Maps for profitability
• Power BI .pbix file is stored here.

4. Collecting the Power BI Performance through Performance Analyzer/

• Used Performance Analyzer in Power BI to:
  • Log load time per visual
  • Optimize slow-performing visuals
  • Improve report responsiveness
• Logs and results stored here for reference.

5. Documentation of the Project/

• Complete project documentation, including:
  • Data flow
  • Tools and technologies
  • Key visual insights
  • Screenshots of dashboards
  • Performance tuning strategies

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🪠 Setup & Installation

✅ Prerequisites

• Python 3.8+
• MySQL Server
• Power BI Desktop (Download here)
• Python libraries:

  pip install pandas requests beautifulsoup4 mysql-connector-python

📦 Clone the Repository

git clone https://github.com/NukaNarendra/SalesAnalysisAndOptimisingPowerBI.git
cd SalesAnalysisAndOptimisingPowerBI

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💡 How to Run the Project

Step 1: Collect the Data

# Inside 'Collecting the Data' folder
python collect_data.py

• Uses pandas, requests, and BeautifulSoup to scrape news articles and merge them with sales dataset.
• Converts and uploads data to SQL.

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Step 2: Clean and Preprocess in SQL

• SQL scripts are executed manually or via Python.
• Cleaned data is saved in SQL and exported to CSV for analysis.

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Step 3: Analyze in Power BI

• Open Mining and Analysing/SalesAnalysis.pbix in Power BI Desktop.
• Click Refresh to load the latest cleaned data.
• Explore dashboards with interactive filters.

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Step 4: Collect Power BI Performance Logs

• Navigate to View → Performance Analyzer in Power BI.
• Start recording and export logs.
• Logs are stored in Collecting the Power BI Performance through Performance Analyzer/.

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Step 5: Read Documentation

• Go to Documentation of the Project/ for:
  • Process flow diagrams
  • Screenshots of each dashboard section
  • Performance analysis notes
  • Model explanation and improvement steps

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📊 Key Visuals in Power BI

• Sales Overview: Total Sales, Profit, Quantity
• Time Series: Monthly and Yearly breakdown
• Profit Heatmap: Region-wise performance
• Top N Analysis: Best-selling categories and products
• Performance Analyzer Results: Load times and optimizations

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✅ Expected Deliverables

✔ Combined dataset and news insights.
✔ Cleaned and processed data in SQL.
✔ Power BI dashboards showing sales intelligence.
✔ Performance optimization using Power BI analyzer.
✔ Complete project documentation with visuals.

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👥 Contributors

• Contributions are welcome!
  Feel free to open issues or pull requests to improve the data pipeline, dashboard, or documentation.