Machine Learning notebooks for refreshing concepts.
-
Updated
Aug 24, 2021 - Jupyter Notebook
Machine Learning notebooks for refreshing concepts.
Classifying badminton strokes based on accelorometer and gyroscope sensor data attached to player's wrist. An end-to-end Machine Learning project, from data collection and preprocessing to final model evaluation.
This repository contains a Jupyter Notebook demonstrating a practical example of data science and machine learning for heart disease classification.
Jupyter notebooks for uni course "Introduction to Machine Learning" 🦾
A ready-to-use Jupyter Notebook template for machine learning projects.
A collection of machine learning and AI projects implemented in Jupyter notebooks, covering regression, classification, and neural networks
Code for classifying breast cancer tumors using machine learning. Includes preprocessing, visualizations, and models like Logistic Regression, Decision Tree, and Random Forest. Evaluated with accuracy, precision, recall, and F1-score. Clone, install dependencies, and run the Jupyter notebook for full analysis.
A comprehensive analysis of the Fashion MNIST dataset using PyTorch. Covers data preparation, EDA, baseline modeling, and fine-tuning CNNs like ResNet. Includes modular folders for data, notebooks, and results. Features CSV exports, visualizations, metrics comparison, and a requirements.txt for easy setup. Ideal for ML workflow exploration.
A repository for Kaggle competition code related to Janestreet - a platform for automated trading by market professionals. Contains scripts, notebooks, and models for predicting financial market trends and optimizing trading strategies.
This repository contains different machine learning classification algorithms based on different data. All notebooks include Data Preparation (data fetch, filling missing values, feature engineering), Exploratory Data Analysis (using various visualization techniques), Pre-Processing, Building a Machine Learning Model, Model Evaluation.
In this series of notebooks, we will dive into each step of the data analysis process of a data set with some information about a list of cars and several attibutes, including their prices. So essentially we will develop a model to predict cars price.
Build machine learning model to predict whether a house will sell or not based on a set of features. The results will be presented in the form of interactive widgets in jupyter notebook for technical audience that can be used to make informed decision about selling their properties.
Obese-tree is a GitHub repository showcasing the application of a Support Vector Machine (SVM) model to estimate obesity levels based on eating habits and physical condition. Explore the code, data, and Jupyter notebooks to learn how SVM can be used for predictive modeling in the context of health and wellness.
Add a description, image, and links to the model-evaluation topic page so that developers can more easily learn about it.
To associate your repository with the model-evaluation topic, visit your repo's landing page and select "manage topics."