The Netflix Movie Recommendation Model utilises TF-IDF (Term Frequency-Inverse Document Frequency) and analyse the content of movie titles, descriptions, and associated metadata. By inputting keywords, users can retrieve movies that align closely with their interests and preferences. The dataset includes more than 8800 movies on Netflix along with their details.

This is a Next.js application for a checkout page that integrates with Stripe for payment processing. The application includes a form for users to enter their email, credit card information, and billing address.

• Collects email, card details, and billing address

• Uses Stripe for secure payment processing

• Includes an option to save billing information for future use

This is a Next.js project for managing leads for a single tradie. The project includes an API for creating, listing, and updating the status of leads. The project uses Next.js, Prisma, and SQLite.

• POST /api/leads: Create a new lead.

• GET /api/leads: List all leads.

• PATCH /api/leads/[id]: Update the status of a specific lead.

The chatbot gives you book suggestions based on what theme do you want.

• Run the model: python train_model.py

• Run the app: python app.py

• ​Message Queuing: Implements basic message queuing using RabbitMQ.

• Retry Logic: Automatically retries message processing up to 5 times upon failure.

• Dead Letter Queue: Unprocessable messages after maximum retries are routed to a Dead Letter Queue for further inspection or handling.

• Error Handling: Robust error and exception handling mechanisms to manage processing failures.