Introduction to Machine Learning project with the goal of improving the classification performance on a dataset by optimizing the number of features and weak learners.
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Updated
Sep 6, 2024 - Jupyter Notebook
Introduction to Machine Learning project with the goal of improving the classification performance on a dataset by optimizing the number of features and weak learners.
Codes and Project for Machine Learning
Regression, Classification, Clustering, Dimension-reduction, Anomaly detection
Fisher's LDA is a dimensionality reduction and classification method maximizing class separability by finding linear discriminants that optimize the ratio of between-class to within-class variance.
Implementation of PCA with KNN Clustering
Automated ML pipeline for Iris dataset classification using Decision Tree. Features PCA dimensionality reduction and standard scaling.
Application of PCA in facial recognition
Задача классификации (Оценка занятости помещения на основе многомерных сенсорных узлов) / Classification task. (Based Occupancy Estimation Using Multivariate Sensor Nodes)
This repository contains Pattern Recognition and Machine Learning programs in the Python programming language.
This repo contains implementation of IP2Vec model which is used for learning similarities between IP Addresses
SDS course assignments
ML Classification Algorithm to predict Approval or Decline of a Loan
A newspaper articles classification system based on theme/topic using BERT (HuggingFace)
This work involves two subtasks: assessing clustering results using all input variables and applying PCA for dimensionality reduction to improve understanding of multi-dimensional problems.
This project involves reducing testing time for car configurations. The tasks include removing columns with zero variance, checking for null values, applying label encoding, performing dimensionality reduction, and using XGBoost to predict testing time.
Reduce the curse of dimensionality
- Graph Based Feature Selection is a new approach of reducing the dimensionality of a dataset using a Graph Based approach. - The apporach tries to generate a Kruskal's minimum spanning tree of a graph where the features of the dataset are the vertices and the correlation among them are the weights of the edges. -The edges having weights greater…
This project focused on applying machine learning to build a clustering model to segment and analyze customer characteristics in the airline industry based on LRFMC scores using K-Means and suggest business strategy recommendations based on the results.
Tutorial- data Pre-processing
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