Skip to content

This Flask-based application recommends similar products based on user preferences such as price, favorites, and reviews. Ideal for users searching for personalized baby product recommendations on Etsy.

Notifications You must be signed in to change notification settings

Suniksha12/Etsy_baby_recommender_Trend

Folders and files

NameName
Last commit message
Last commit date

Latest commit

 

History

15 Commits
 
 
 
 
 
 
 
 
 
 
 
 

Repository files navigation

🛒 Personalized Product Recommendation System for Etsy Baby Items 🎁

This Flask-based application recommends similar products based on user preferences such as price, favorites, and reviews. Ideal for users searching for personalized baby product recommendations on Etsy.

🚀 Features

  • Custom Preferences: Filter products by price range, popularity, and number of reviews.
  • Data Analysis: Uses cleaned dataset for accurate recommendations.
  • Machine Learning Models: Random Forest, Gradient Boosting, and SVC models applied for precise predictions.

📁 Project Structure

Flask/
│
├── app.py                   # Main Flask application
├── Cleaned_data.csv          # Dataset used for recommendations
├── templates/
│   ├── index.html            # Homepage with preference input
│   ├── recommendations.html  # Displays recommendations
└── static/
    ├── background.jpg        # Background image

⚙️ Installation

  1. Clone the repository:
    git clone <repository-link>
  2. Navigate to the project directory:
    cd flask
  3. Install the required packages:
    pip install -r requirements.txt
  4. Run the application:
    python app.py

💻 Usage

  • Visit http://127.0.0.1:5000/ in your browser.
  • Enter preferences such as price, number of favorites, and reviews.
  • View the list of recommended products.

🔍 Exploratory Data Analysis

  • Price Distribution: Price Distribution

  • Correlation Heatmap: Heatmap

📊 Model Performance

  • Random Forest achieved 100% accuracy.
  • Gradient Boosting and SVC yielded great results with accuracy and feature importance.

📸 Screenshots

Homepage

Homepage

Recommendations

Recommendations