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 my machine learning models implementation code using streamlit in the Python programming language.
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 repository contains machine learning programs in the Python programming language.
Computer hardware performance which has been recorded and is open for free usage. Licensed under the MIT License & CC-BY-4.
This is my first project on Github
This repository contains programs in the Python programming language using Module Streamlit.
This project allows users to thoroughly test their HuggingFace AI models with comparison and saving functionalities.
Feature engineering for INGV data
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
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.
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 …
This Series contains Data Analysis projects performed on different Kaggle datasets and providing valuable insights into the data by making use of Python libraries.
How to use the Kaggle API to upload data from a server to Kaggle as a dataset?
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