🏷️ TextileBiz Analytics: Smart Pricing & Market Analysis

TextileBiz Analytics is an advanced data-driven pricing optimization solution designed for the textile industry. This project leverages real-time price scraping, competitive market analysis, dynamic pricing strategies, and machine learning to help businesses make informed pricing decisions.

🚀 Features

🔹 Data Preprocessing & Cleaning

• Standardizes and prepares raw textile pricing data for analysis.
• Removes missing values and normalizes column formats.

🔹 Competitor Price Scraping

• Extracts real-time pricing data from Amazon & Google.
• Uses automated web scraping techniques with proxy support.

🔹 Pricing Analysis & Classification

• Identifies overpriced, underpriced, and competitive items.
• Generates detailed pricing analysis reports.

🔹 Dynamic Pricing Optimization

• Applies business rules to adjust pricing based on competitors.
• Ensures minimum profit margins and strategic price reductions.

🔹 Machine Learning Model for Price Prediction

• Trains a Linear Regression Model to predict optimized prices.
• Factors in cost price, competitor price, seasonality, and location.

🔹 Published Power BI Dashboard for Insights

• Provides interactive visual analytics for pricing trends.
• Showcases competitor benchmarking and optimal price recommendations.
• Access the published dashboard here.

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📂 Folder Structure

TextileBiz-Analytics-Smart-Pricing-Market-Analysis/
│── data/
│   ├── Textile_data2.txt.csv  # Original raw dataset
│   ├── Enhanced_Textile_Dataset.csv  # Cleaned dataset
│   ├── Pricing_Analysis_Report.csv  # Pricing classification results
│   ├── Optimized_Pricing_Dataset.csv  # Adjusted pricing after rules
│   ├── Final_ML_Pricing_Dataset.csv  # ML-predicted optimal prices
│── models/
│   ├── trained_model.pkl  # Trained Machine Learning model
│── scripts/
│   ├── cleaning_dataset.py  # Data cleaning & preprocessing
│   ├── price_scraper.py  # Web scraping (Amazon & Google)
│   ├── price_analysis.py  # Competitive pricing classification
│   ├── optimize_price.py  # Dynamic pricing strategy implementation
│   ├── train_model.py  # Machine Learning model training
│── notebooks/
│   ├── TextileBiz_Analytics_Analysis.ipynb  # Jupyter Notebook with full pipeline
│── dashboards/
│   ├── screenshots/
│       ├── dashboard1.png  # dashboard view
│       ├── dashboard2.png  # dashboard view
│       ├── dashboard3.png  # dashboard view
│   ├── Textile_Pricing_Dashboard.pbix  # Power BI Dashboard file
│── templates/
│   ├── dashboard.html  # Frontend to access the published Power BI dashboard
│── app.py  # Flask application to serve dashboard
│── README.md  # Project documentation
│── requirements.txt  # Python dependencies

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📊 Power BI Dashboard

📌 The Textile_Pricing_Dashboard.pbix file in the dashboards/ folder provides:
✔ Market Trends | ✔ Price Competitiveness | ✔ Optimized Pricing Insights

To use:

• Open Power BI Desktop.
• Load dashboards/Textile_Pricing_Dashboard.pbix.
• Analyze pricing trends, competitor insights, and ML-based pricing predictions.

Alternatively, access the published version directly from the web here.
Note: you have to login through your organisation domain to access through web

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⚙️ Configuration

The config/config.yaml file stores key parameters, including:
🔹 Pricing adjustment rules
🔹 Web scraping settings
🔹 Machine learning hyperparameters

Modify this file to customize your pricing strategy.

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🛠️ Technologies Used

• Backend: Flask
• Data Processing: Pandas, NumPy
• Web Scraping: BeautifulSoup, Requests, Fake-UserAgent
• Machine Learning: Scikit-Learn (Linear Regression)
• Visualization: Power BI
• Development Tools: Python, Jupyter Notebook

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📝 Testing & Validation

• Data Quality Checks – Ensures clean and structured data.
• Model Evaluation – Validates accuracy of pricing predictions.
• Competitor Benchmarking – Verifies scraped data against actual market prices.

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📝 Running the Flask Application

🔧 Installation & Setup

1️⃣ Install dependencies:

pip install -r requirements.txt

2️⃣ Run the Flask app:

python app.py

3️⃣ Open the browser and go to:

http://127.0.0.1:5000/

This will render the dashboard.html template with a button linking to the published Power BI dashboard.

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📬 Contact & Support

Developed by: Saideep Rangoni

• Email: saideeprangoni634@gmail.com
• LinkedIn: My Profile

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🚀 Transform Your Textile Business with Smart Pricing Strategies!