A list of compatible datasets, noting other major repositories containing popular real-world datasets, along with sample code for a range of recommendation tasks.
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Updated
Aug 13, 2021
A list of compatible datasets, noting other major repositories containing popular real-world datasets, along with sample code for a range of recommendation tasks.
NBA History & analysis of: Player of the week, Head coaches, players statistics per season
This repository contains machine learning programs in the Python programming language.
This repository contains my machine learning models implementation code using streamlit in the Python programming language.
This repository contains programs in the Python programming language using Module Streamlit.
Kaggle Dataset Participation Code
Movie Recommender System is the python Based Project To Create Content Based Recommender System using TMDB 5000 movie dataset from Kaggle
This is my first project on Github
This project allows users to thoroughly test their HuggingFace AI models with comparison and saving functionalities.
Feature engineering for INGV data
Computer hardware performance which has been recorded for Asus GL553VD and is open for free usage. Licensed under the MIT License & CC-BY-4.
An initial phase segmentation using LinkNet on the skin lesion dataset managed by VISION AND IMAGE PROCESSING LAB, University of Waterloo. Public dataset on Kaggle at https://www.kaggle.com/datasets/mahmudulhasantasin/university-of-waterloo-skin-cancer-db-80-10-10/.
In-depth analysis of NYC Yellow Taxi data, exploring trip patterns, fare amounts, demand hotspots, and correlations using PySpark and Databricks.
Statistical data analysis report on Kaggle dataset Student Performance made as a personal project.
A solution for identifying and recognizing landmarks from images, addressing key challenges and leveraging both algorithmic and human expertise to achieve high accuracy and reliability.
Exploratory Data Analysis and Random Forest Survival Prediction
AniSearchModel leverages Sentence-BERT (SBERT) models to generate embeddings for synopses, enabling the calculation of semantic similarities between descriptions. This allows users to find the most similar anime or manga based on a given description.
Here I am presenting machine learning notebooks, which were used during various analysis, e.g. kaggle competitions: "Titanic - Machine Learning from Disaster", Spaceship Titanic", "House Prices - Advanced Regression Techniques" and "Digit Recognizer") and other assays.
This repository contains notebooks in which I have implemented ML Kaggle Exercises for academic and self-learning purposes. In my notebooks, I have implemented some basic processes involved in ML Data Processing like How to take care of Missing Values, Handling Categorical Variables, and operations like mapping, 'Grouping', 'Sorting', 'Renaming …
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