👗 StylePredict – Clothing Store Sales Prediction Model

ML-powered web app to analyze and forecast e-commerce clothing sales with smart dashboards.

---

🔗 Live Demo

🌐 https://fashionforecast-h008.onrender.com/

---

💻 Tech Stack Used

• Frontend: HTML, CSS, JavaScript
• Backend: Python (Flask)
• Machine Learning: XGBoost, Scikit-learn, Pandas, NumPy
• Visualization: Matplotlib, Seaborn, Plotly
• Deployment: WSGI + Gunicorn

---

📌 Project Description

This project is a machine learning-powered dashboard built to analyze and forecast e-commerce clothing store sales.
It includes detailed visualizations of trends, payment methods, and predicted sales based on historical transaction data.

✅ Why I built it:
To combine my knowledge of machine learning and web development into a full-stack project that solves a real-world business problem — helping businesses forecast future sales with clarity and interactivity.

---

🖼️ Screenshots

Home Page	Prediction Page

Data Insights Page

How It Works

Contact Us	Privacy Policy

About Us

---

⭐ Features

• 📂 Preloaded Dataset Analysis
  App uses a built-in historical transaction dataset — no upload required.

• 📊 Insights Page for EDA (Exploratory Data Analysis)
  A dedicated “Insights” page shows visualizations such as category-wise sales, payment distributions, and trends — all rendered from backend-generated images.

• 🤖 XGBoost Model-Based Prediction
  Sales forecasting is powered by a pre-trained XGBoost model built using realistic, real-world purchase behavior.

• 🎯 Filter-Based Custom Predictions
  Users can dynamically adjust inputs like:

  • Product category
  • Age group
  • Gender
  • Location
  • Payment method
  • Future time horizon (in months)

• 📈 Forecast Future Sales
  App predicts total future sales (1 to 12 months ahead) based on filtered criteria and displays results with clear visual styling.

• 🌐 Multiple Web Pages
  Fully structured frontend with the following routes/pages:

  • Home
  • Prediction
  • Insights (EDA)
  • How It Works
  • About Us
  • Contact Us
  • Privacy Policy

• 📱 Responsive UI
  The app is mobile-friendly on most pages (except Insights, currently desktop-optimized).

---

🛠 Installation / Usage Instructions

# Clone the repository
git clone https://github.com/HawaleShailesh004/Style-Predict.git
cd Style-Predict

# (Optional) Create and activate a virtual environment
python -m venv env
source env/bin/activate  # Windows: env\Scripts\activate

# Install dependencies
pip install -r requirements.txt

# Run the application
python app.py

Then open http://localhost:5000 in your browser.

🚧 Future Improvements / What I Learned 🔧

Future Plans:

• Improve the responsiveness of the Insights page for a better user experience.

What I Learned:

• End-to-End ML Pipeline Integration: Gained hands-on experience integrating the machine learning pipeline within a web app.
• XGBoost Model: Trained and deployed an XGBoost model to make accurate predictions.
• Backend Image Rendering for EDA: Implemented image rendering in the backend to support exploratory data analysis (EDA) effectively.
• Frontend/Backend Interaction: Facilitated seamless communication between the frontend and backend using Flask templates.
• Dynamic Predictions with Filters: Utilized practical filter implementation to create dynamic, user-tailored predictions.
• Clean, Scalable Folder Structures: Built a maintainable and scalable folder structure for the web app to enhance collaboration and project longevity.

License

This project is licensed under the MIT License.

🙋‍♂️ Author

Shailesh Hawale

Contributors

Shresha Sinha Shravani Rodge Aditi Bhoir

• 📧 Email: shaileshhawale004@gmail.com
• 🐙 GitHub: ShaileshHawale