🔱 Some recognized algorithms[Decision Tree, Adaboost, Perceptron, Clustering, Neural network etc. ] of machine learning and pattern recognition are implemented from scratch using python. Data sets are also included to test the algorithms.

Проект создания рекомендательной системы для библиотеки

Are you confused about what to binge-watch next? Welcome to the Precog-Movie-Recommender. Just enter ratings about 5 movies which we will show you, and we will recommend you what to view next.

A model to recommend movies based on collaborative filtering (using ALS algorithm) and perform various analysis on the data.

Restaurants Recommendation System

Exploring Recommendation Systems: Code examples comparing implementations using PySpark (ALS) and TensorFlow Recommenders. Dived into the practical examples and Youtube's case study provided in the README.

Collection of my ML projects using PySpark

May - June 2022

This project is a web application for recommending restaurants using Google Maps data and AI-based recommendations. It allows users to view, rate, and review restaurants, and provides personalized recommendations based on user preferences and reviews.

A powerful recommendation system for Steam games, combining Content-Based and Collaborative Filtering techniques. Built with Python, Scikit-learn, and Streamlit to deliver accurate, real-time game recommendations. Perfect for gamers and data scientists interested in building intelligent recommendation engines.