A Skincare Recommendation System using a Database Management System (DBMS) can help users find the best skincare products based on their skin type, concerns, and preferences. Here’s a structured approach to your project:
Develop a Skincare Recommendation System that suggests skincare products based on user input, such as skin type, concerns (acne, dryness, aging), ingredients, and budget.
| Column Name | Data Type | Description | |-------------|----------|-------------| | user_id | INT (Primary Key) | Unique ID for users | | name | VARCHAR(100) | User's name | | email | VARCHAR(100) | Email (unique) | | password | VARCHAR(255) | Encrypted password | | age | INT | Age of the user | | gender | VARCHAR(10) | Gender | | skin_type | VARCHAR(50) | (Oily, Dry, Combination, Normal, Sensitive) |
| Column Name | Data Type | Description | |-------------|----------|-------------| | concern_id | INT (Primary Key) | Unique ID for concerns | | concern_name | VARCHAR(100) | Concern (Acne, Wrinkles, Dryness, etc.) |
| Column Name | Data Type | Description | |-------------|----------|-------------| | product_id | INT (Primary Key) | Unique product ID | | name | VARCHAR(255) | Product name | | brand | VARCHAR(100) | Brand name | | category | VARCHAR(100) | (Cleanser, Moisturizer, Sunscreen, etc.) | | price | DECIMAL(10,2) | Price of the product | | skin_type | VARCHAR(100) | Suitable for which skin type | | concern_id | INT (Foreign Key) | Related skin concern | | ingredients | TEXT | Key ingredients | | rating | FLOAT | Average rating |
| Column Name | Data Type | Description | |-------------|----------|-------------| | preference_id | INT (Primary Key) | Unique ID | | user_id | INT (Foreign Key) | User ID | | concern_id | INT (Foreign Key) | Selected skin concerns | | budget | DECIMAL(10,2) | Budget preference |
| Column Name | Data Type | Description | |-------------|----------|-------------| | review_id | INT (Primary Key) | Unique ID | | user_id | INT (Foreign Key) | User ID | | product_id | INT (Foreign Key) | Product ID | | rating | FLOAT | User rating | | review_text | TEXT | User review |
✔️ Sign up/Login (User Authentication)
✔️ Enter skin type, concerns, and preferences
✔️ Get personalized skincare recommendations
✔️ Filter products by price, brand, and ingredients
✔️ Read & write product reviews
✔️ Add/edit/delete products
✔️ Manage user accounts
✔️ Analyze popular products based on user feedback
INSERT INTO Users (name, email, password, age, gender, skin_type)
VALUES ('Alice', 'alice@example.com', 'hashed_password', 25, 'Female', 'Oily');
SELECT p.name, p.brand, p.category, p.price, p.rating
FROM Products p
JOIN UserPreferences up ON p.skin_type = up.skin_type
WHERE up.user_id = 1
AND p.concern_id IN (SELECT concern_id FROM UserPreferences WHERE user_id = 1)
ORDER BY p.rating DESC;
SELECT name, brand, category, AVG(rating) as avg_rating
FROM Products
GROUP BY product_id
ORDER BY avg_rating DESC
LIMIT 10;
SELECT u.name, r.rating, r.review_text
FROM Reviews r
JOIN Users u ON r.user_id = u.user_id
WHERE r.product_id = 5;
✅ AI-Based Recommendations – Using ML to predict the best products
✅ Integration with Skincare APIs – Fetch real-time product data
✅ Chatbot for Skincare Queries – Suggests products via chatbot
✅ Subscription Model – Monthly skincare product suggestions make it dynamic and responsive the chat bot should be working and responsive make it colourful and make it proficinally
I'll create a comprehensive skincare recommendation system with a MySQL database, Next.js, and a chatbot feature. This application will help users find personalized skincare products based on their skin type, concerns, and preferences.
To implement the MySQL database for your skincare recommendation system, you'll need to set up the database schema and create the necessary tables as specified in your requirements. Here's how you can do it:
schema.sql
Here's a Python implementation of a recommendation algorithm for your skincare system:
recommendation_algorithm.py
Here's a Node.js implementation for the backend API of your skincare recommendation system:
server.js
The skincare recommendation system I've created includes:
User Interface:
Core Features:
Database Structure:
Recommendation Algorithm:
API Endpoints:
The system is designed to be easily extended with additional features like:
To deploy this application, you would need:
Please make sure to add the following environment variable to your project:
make it fast use flask as backend and react as frontend and my sql as database and make a working chat bot
I'll create a complete skincare recommendation system with Flask backend, React frontend, MySQL database, and a working chatbot. Let's build this step by step.
Here's how you would implement the Flask backend for this skincare recommendation system:
app.py
Here's the SQL script to create the database schema:
schema.sql
I did not output the full code. I will fix this.
schema.sql
Set up the MySQL database:
Create a .env file with your database credentials:
DB_HOST=localhost
DB_USER=root
DB_PASSWORD=your_password
DB_NAME=skincare_dbSet up the Flask backend:
Install the required packages:
pip install flask flask-cors mysql-connector-python python-dotenv bcryptRun the Flask application:
python app.pySet up the React frontend:
Install the required packages:
npm installCreate a .env.local file with your API URL:
NEXT_PUBLIC_API_URL=http://localhost:5000/apiRun the development server:
npm run devAccess the application:
http://localhost:3000User Authentication:
Product Browsing:
Personalized Recommendations:
Interactive Chatbot:
Responsive Design:
The system is designed to be easily extensible, so you can add more features like user reviews, product ratings, and advanced recommendation algorithms in the future.