Improve our project: https://github.com/Kfirul/NBA-Hall-Of-Fame.git with new models we learn ( XGBoost, Adaboost, Hard Voting, Soft Voting) and using t -SNE fir visualization.
Fashion MNIST is a popular dataset used in the field of machine learning and computer vision.
The Fashion MNIST dataset consists of 70,000 grayscale images, each of size 28x28 pixels. The images are associated with 10 different fashion categories, making it a multi-class classification problem.
Here are the 10 fashion categories in Fashion MNIST:
- T-shirt/top
- Trouser/pants
- Pullover shirt
- Dress
- Coat
- Sandal
- Shirt
- Sneaker
- Bag
- Ankle boot
Each image is labeled with the corresponding category it belongs to. The goal in Fashion MNIST is to train a machine learning model that can accurately classify the images into one of these categories based on their pixel values.
The dataset is widely used as a benchmark for testing and comparing the performance of various machine learning algorithms and deep learning models. Since it represents real-world fashion items, it provides a more practical and relevant challenge. We will lower our amount of dimensions with PCA (while maintaining the maximum classification capabilities of the models) to help the models run faster.
"Dogs vs. Cats" is popular dataset used in the field of computer vision and machine learning. It is a binary classification dataset, meaning the task is to classify images into one of two classes: "Dog" or "Cat." The dataset typically consists of a large number of images of dogs and cats that have been labeled accordingly.
The "Dogs vs. Cats" dataset is used for various purposes, including benchmarking and evaluating the performance of image classification algorithms and deep learning models. It represents a classic binary classification problem in the context of computer vision, where the goal is to build a model that can accurately distinguish between images of dogs and images of cats.
The dataset usually contains a diverse set of images, including dogs and cats in various poses, backgrounds, and lighting conditions. Some images may also include multiple animals or other objects, making the classification task more challenging. We will lower our amount of dimensions with PCA (while maintaining the maximum classification capabilities of the models) to help the models run faster.
We were given data from children's drawings in which they try to draw a shape that is presented to them. We will try to create algorithms that predict several questions:
- Number of lines
- Line length and average pressure
- Number of pen levels
- Is the shape closed or open