Predict the prices of houses based on certain geographical and other factors.
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Updated
Dec 24, 2016 - R
Predict the prices of houses based on certain geographical and other factors.
Fast k-Nearest Neighbors Classifier for Large Datasets
Principal Component Analysis method of dimension reduction for feature vectors of higher space to a lower feature space
This is a solution to a Kaggle competition on predicting claim severity for Allstate Insurance using the Extreme Gradient Boosting (XgBoost) algorithm in R
Sparse Principal Component Analysis (SPCA) using Variable Projection
LDFR model
Python package for Dimensionality Reduction
An Efficient Gaussian Kernel Based Fuzzy-Rough Set Approach for Feature Selection
autoencoder implemented with tensorflow
Implementation of all basic algorithms needed in Machine Learning and Deep Learning
NTUEE 2018 spring course - Machine Learning (Pei-Yuan Wu, Hung-Yi Lee, Tsungnan Lin)
A comparison between some dimension reduction algorithms
Our aim is to apply PCA for dimension reduction so that we can easily visualize it in 2D. To study this we have use MNIST dataset consist of 42,000 data points and 785 features (labels included)
Simple function estimation methods implemented conveniently for benchmarking estimators
Deep learning meets molecular dynamics.
Used PCA for dimension reduction of a 25x25 animal image dataset. After the feature extraction step, a KNN classifier to distinguish the images in a 3D plane (3PC extraction). PCA and KNN are implemented from scratch. Matplot is used for 3D visualization.
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