Parsa Beigzadeh's Projects
Booki is a book recommendation system designed to provide personalized book recommendations based on user preferences and interests. This project utilizes various technologies, including C#, ASP .NET Core, Python, Spacy, Ontology, and knowledge base graphs
In this project, I illustrate and create book list via Asp .core 3.1 and use Razor page for it
It has been designed for library and it has been created by Asp core 3.1 MVC
this app was written by C# and it is an assistant for cafe and it provide customer club and some features to help owners for manage their cafe.I used N-Layer Architecture for this app and sms panel is provided.
The Restaurant Recommendation Chatbot is a Python-based project that utilizes natural language processing (NLP) techniques, TensorFlow, and logistic regression to provide personalized restaurant recommendations. This project aims to assist users in finding suitable dining options based on their preferences and requirements.
Research project developing a component recommendation system to support software developers in selecting open source libraries and frameworks. Leverages metadata extraction and machine learning techniques to provide personalized component suggestions. Created as part of a university thesis project.
This repository contains the implementation of various data mining algorithms using Python. The algorithms included in this project are logistic regression, decision trees, random forest,, and naive Bayes. These algorithms are widely used in data mining and machine learning for classification tasks.
This project implement end to end piplie based on ML
Repo for Instagram analysis: Dive into user trends, engagement metrics, and content performance. Uncover insights to optimize strategies. #DataDriven
Object detection using YOLOv7 by Pytorch
Convert paddle model to onnx
This project about package recommendation system. it focuses on popular package repos.
Product Recommendation System This repository contains the code and resources for a product recommendation system implemented using collaborative filtering techniques. The system provides personalized recommendations to users based on their interactions with products.
This project focuses on training a deep learning model to detect and classify numbers represented in the form of seven-segment displays. The model is built using TensorFlow and Keras libraries and leverages convolutional neural networks (CNNs) for accurate classification.
Research project developing a component recommendation system to support software developers in selecting open source libraries and frameworks. Leverages metadata extraction and machine learning techniques to provide personalized component suggestions. Created as part of a university thesis project.
Explore LSTM-based Apple stock price prediction: leveraging historical data, LSTM networks forecast future trends. Join us to innovate in financial forecasting, empowering informed investment decisions.
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