Welcome to the Machine Learning Basics repository! This repository is dedicated to showcasing my journey through learning the fundamentals of machine learning. You'll find various datasets, Jupyter notebooks, and source code that I have worked on.

In this project, we develop a fully functional web application for a library recommendation system. Users can register, create and manage profiles, write and manage blog reviews on their reading experiences, and share their writings with the world. The app includes a comprehensive UX/UI, encompassing all features from the original project vision.

Creation of a movie recommendation system during data scientist training at datascientest.com.

This app analyzes ratings to suggest ideal products for e-commerce platforms. Upload your data, explore user trends, and train a model to predict what your customers will love!

A simple Product Recommendation System.

This repository contains the source code and documentation for a Bachelor's thesis project that explores two different approaches to developing a movie recommendation system.

An overview of reccomendation systems in Python

Harnessing music's power for better mental health: genre recommendations and data-driven analysis of listeners' trends

🛍️ Amazon Recommender Study 🚀 A Python exploration into machine learning for e-commerce personalization, using Amazon's Electronics data. Investigates algorithms like SVD, KNNBaseline for predicting user preferences, offering insights into future shopping enhancements

Phase 4 project of the Flat Iron curriculum of Data Science in Moringa School

MovieLens recommended system project

Collaborative filtering based recommender system using the surprise library

울산 맛집,카페/명소 추천 시스템

Suprise-Python Wrapper for Persa.jl

The Hybrid Movie Recommender is a system that recommends movies using a combination of collaborative and content-based filtering techniques. The system is designed to address the cold start problem(new users) by using a popularity based approach. The dataset used for the system is obtained from Kaggle.

Grocery Recommendation on Instacart Data

A Collaborative filtering recommendation system

Recommender System built using Python, Angular, Firebase & MySQL

기본적인 Recommendation System을 갖춘 REST API 서버와, 이를 표현하기 위한 간단한 프론트엔드를 구현한 페이지입니다.

Built a collaborative filtering and content-based recommendation/recommender system specific to H&M using the Surprise library and cosine similarity to generate similarity and distance-based recommendations.