This repository contains my machine learning models implementation code using streamlit in the Python programming language.
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Updated
Jul 11, 2024 - Jupyter Notebook
This repository contains my machine learning models implementation code using streamlit in the Python programming language.
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.
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.
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.
Computer hardware performance which has been recorded for Asus GL553VD and is open for free usage. Licensed under the MIT License & CC-BY-4.
Movie Recommender System is the python Based Project To Create Content Based Recommender System using TMDB 5000 movie dataset from Kaggle
Statistical data analysis report on Kaggle dataset Student Performance made as a personal project.
Exploratory Data Analysis and Random Forest Survival Prediction
This is my first project on Github
This Series contains Data Analysis projects performed on different Kaggle datasets and providing valuable insights into the data by making use of Python libraries.
A list of compatible datasets, noting other major repositories containing popular real-world datasets, along with sample code for a range of recommendation tasks.
Kaggle Dataset Participation Code
Feature engineering for INGV data
NBA History & analysis of: Player of the week, Head coaches, players statistics per season
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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