dimensionality-reduction-technique

There are 0 repository under dimensionality-reduction-technique topic.

Ayantika22 / PCA-Principle-Component-Analysis-For-Seed-Dataset
PCA(Principle Component Analysis) For Seed Dataset in Machine Learning
principle-component-analysis seed-dataset jupyter-notebook python-3 unsupervised-learning machine-learning classifier classification accuracy cohen-kappa logistic-regression knn-classifier k-nearest-neighbor-classifier dimensionality-reduction-technique support-vector-machines gaussian-naive-bayes decision-tree-classifier random-forest-classifier
Language:Jupyter Notebook 5
Ayantika22 / LDA-Linear-Discriminant-Analysis-for-Seed-Dataset
LDA(Linear Discriminant Analysis) for Seed Dataset
linear-discriminant-analysis-lda jupyter-notebooks python-3 seed-dataset unsupervised-learning machine-learning dimensionality-reduction-technique accuracy cohen-kappa logistic-regression-classifier k-nearest-neighbors support-vector-machine knn-classifier classifier classification gaussian-naive-bayes decision-tree-classifier random-forest-classifier
Language:Jupyter Notebook 3
rajvi-patel-22 / Linear-Discriminant-Analysis-of-a-32x32-grayscale-image-for-image-compression.
Discriminant analysis methods can be good candidates to address such problems. These methods are supervised, so they include label information. The goal is to find directions on which the data is best separable. One of the very wellknown discriminant analysis method is the Linear Discriminant Analysis. Linear Discriminant Analysis (LDA) is most commonly used as dimensionality reduction technique in the pre-processing step for pattern-classification and machine learning applications. The goal is to project a dataset onto a lower-dimensional space with good class-separability in order avoid overfitting (curse of dimensionality) and also reduce computational costs. Pertaining to our problem, we are given a [32 X 32] binary image as input and the goal is to apply LDA technique to transform the features into a lower dimensional space, which maximizes the ratio of the between-class variance to the within-class variance, thereby guaranteeing maximum class separability between two classes in our case with the minimal loss.
linear-discriminant-analysis wellknown-discriminant-analysis discriminant-analysis-methods dimensionality-reduction-technique variance lda-technique linear-algebra-concepts signals-and-systems computer-organisation la-ss-co-project
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SahilK-027 / Factor-Analysis-Mini-Project
Mini project on Dimensionality Reduction
dimensionality-reduction-technique factor-analysis
Language:Jupyter Notebook 2
different_processings_for_ML
Jean-Lcs / different_processings_for_ML
In this project, we use differents methods to transform our dataset (usually dimension modification) before making prediction thanks to machine learning and regressions.
pca-analysis pca regularisation regularisation-methods ridge-regression ridge variable-selection variables ols-regression dimensionality-reduction dimensional-analysis dimensionality-reduction-technique principal-component-analysis principal-components principal-component-analysis-pca
Language:Jupyter Notebook 0
mohammad95labbaf / UMAP_breast_cancer
This repository explores the interplay between dimensionality reduction techniques and classification algorithms in the realm of breast cancer diagnosis. Leveraging the Breast Cancer Wisconsin dataset, it assesses the impact of various methods, including PCA, Kernel PCA, LLE, UMAP, and Supervised UMAP, on the performance of a Decision Tree.
breast-cancer-wisconsin decision-tree decision-tree-classifier dimensionality-reduction dimensionality-reduction-technique eda expolatory-data-analysis kernel-pca lle locally-linear-embedding principal-component-analysis umap
Language:Jupyter Notebook 0
satishmekkonda / DataScience-Projects
This repository consists of 6 sections, detailing hands on Machine Learning Models: Regression, Classification, Clustering, AssocaitionRuleLearning, Deep Learning and Natural Language Processing Techniques
regression-models classification-methods clustering-methods association-rules reinforcement-learning natural-language-processing neural-networks dimensionality-reduction-technique xgboost-model kfold-cross-validation gridsearchcv
Language:Python 0
tanyakuznetsova / Multidimensional-Scaling-of-European_Cities
This project explores the spatial relationships between twenty European cities using classical manual Multidimensional Scaling (MDS), MDS from scikit-learn, and compares the results with Principal Component Analysis (PCA).
classical-ml-algorithms dimensionality-reduction dimensionality-reduction-technique mds multidimensional-scaling pca principal-component-analysis unsupervised-learning unsupervised-machine-learning
Language:Jupyter Notebook 0
Trisha11r / dim_reduction_study
data-science dimensionality-reduction dimensionality-reduction-technique google-colaboratory pca python3 qualitative-analysis quantitative-analysis sklearn tsne umap
Language:Jupyter Notebook

dimensionality-reduction-technique

Ayantika22 / PCA-Principle-Component-Analysis-For-Seed-Dataset

Ayantika22 / LDA-Linear-Discriminant-Analysis-for-Seed-Dataset

rajvi-patel-22 / Linear-Discriminant-Analysis-of-a-32x32-grayscale-image-for-image-compression.

SahilK-027 / Factor-Analysis-Mini-Project

Jean-Lcs / different_processings_for_ML

mohammad95labbaf / UMAP_breast_cancer

satishmekkonda / DataScience-Projects

tanyakuznetsova / Multidimensional-Scaling-of-European_Cities

Trisha11r / dim_reduction_study