🪑 Furniture Sales Prediction Web App

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🚀 Overview

This web application predicts how many units of a furniture product will be sold based on features like product title, price, original price, and shipping tag. It uses a trained machine learning model and also provides visual insights and a downloadable PDF report.

🖼️ UI Preview

🎯 Prediction Output Screenshot

Here’s a sample output after submitting product details:

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🧩 Features

🔮 Prediction Engine

• Inputs: Product Title, Price, Original Price, Shipping Tag
• Automatically calculates discount percentage
• Uses a TF-IDF + Random Forest model to predict sales

📊 Graphical Insights

Interactive graphs shown:

• Predicted vs Actual Sales
• Price vs Predicted Sales
• Feature Importance
• Distribution of Sold Items
• Sales by Shipping Tag
• Sales by Discount Percentage
• Price Distribution
• Tag Breakdown, and more!

📄 PDF Report

• Downloadable report with:
  • Prediction result
  • Embedded analysis graphs
  • Copyright

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🧠 Tech Stack

Layer	Tech
Backend	Python, Flask
ML/Processing	scikit-learn, pandas, NumPy
Text Features	TF-IDF Vectorization
Visualization	matplotlib, seaborn
PDF Reports	ReportLab
Frontend	HTML, CSS (custom styles)

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🗂️ Project Structure

ECOM_FURNITURE/
│
├── app.py
├── train_model.py
├── requirements.txt
├── README.md
│
├── data/
│   └── ecommerce_furniture_dataset_2024.csv
│
├── model/
│   ├── model.pkl
│   └── tfidf.pkl
│
├── static/
│   ├── styles.css
│   └── graphs/
│       └── *.png
│
├── templates/
│   ├── index.html
│   └── result.html

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🛠️ How to Run

1. Install dependencies:

   pip install -r requirements.txt

2. Create these folders and files:

   create model folder
   create model.pkl & tfidf.pkl
   keep both files empty
   (req to save trained models)

3. Train the model:

   python train_model.py

4. Run the Flask app:

   python app.py

5. Open browser at:

   http://localhost:5000
   

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🧪 Example Prediction Flow

1. Fill the form on homepage
2. Click Predict
3. View result and insights
4. Click Download as PDF to save the report

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👤 Developer

Made with ❤️ by Aditya Arora
© 2025 Aditya Arora. All rights reserved.

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