😺 Usage, discussion and related information.
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Updated
May 29, 2024
😺 Usage, discussion and related information.
🎨 Business collaboration using design in advertising.
Virtual whiteboard for sketching hand-drawn like diagrams
I developed a popularity-based book recommendation system using Google Colab, leveraging libraries like pandas, numpy, and matplotlib. The dataset was obtained from Kaggle.com, consisting of three CSV files: books.csv, ratings.csv, and users.csv.
The Jupyter Notebook (ipynb file) contained within this repository serves as the primary codebase for this project. It demonstrates how to preprocess the input data, train the logistic regression classifier, evaluate its performance, and save the trained model for future use.
Google Colab Notebooks Collection
Folks are welcome to share about themselves, even a little introduction is appreciated.
💥 Combine custom GPTs for collaborations.
This repository hosts a machine learning-based project focused on predicting diabetes risk among individuals. Utilizing advanced algorithms and data analysis techniques, our project aims to provide accurate predictions and insights into potential diabetes occurrences based on various health parameters.
This repository hosts a machine learning-based project focused on predicting diabetes risk among individuals. Utilizing advanced algorithms and data analysis techniques, our project aims to provide accurate predictions and insights into potential diabetes occurrences based on various health parameters.
The objective of this project is to develop a machine learning model that can effectively classify emails as spam or non-spam (ham) based on their content.
[submitted] Our entry for #cyberpunkjam 2014
Ludwig Ahgren made a Christmas Album in 2020. I just thought of a project cause I was bored where you can play all the songs by a click of a button which you can already do from his timestamps but what makes it special is that it has lyrics of those songs too and a bunch of other features.
Computer Vision Tasks
Testing out LLAMA2.
Notes, examples, and Python demos for the 2nd edition of the textbook "Machine Learning Refined" (published by Cambridge University Press).
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