Continuation of my machine learning works based on Subjects....starting with Evaluating Classification Models Performance
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Updated
Jul 26, 2018
Continuation of my machine learning works based on Subjects....starting with Evaluating Classification Models Performance
Machine learning algorithms from scratch in python.
Applying different machine learning algorithms on PCGA Prostate Cancer Gene Dataset for Feature Selection, Dimensional Reduction and Classification and Regression
The projects are part of the graduate-level course CSE-574 : Introduction to Machine Learning [Spring 2019 @ UB_SUNY] . . . Course Instructor : Mingchen Gao (https://cse.buffalo.edu/~mgao8/)
Implementation of Fisher Linear Discriminant Analysis in Python
Analysing different dimensionality reduction techniques and svm
LDA(Linear Discriminant Analysis) for Seed Dataset
Implementation of Machine Learning Algorithms (KNN, Linear, Logistic, SVM, K-Means, Decision Tree, Naive Bayes) from Scratch using Python & Numpy only
Data Understanding using- PCA, LDA, tSNE, and UMAP.
NUS Pattern Recognition module graded assignments
In this project we conducted linear discriminant analysis to determine whether a given car is above or below the median mpg.
Autoencoder model implementation in Keras, trained on MNIST dataset / latent space investigation.
Various Machine learning algorithms
Exploratoy Data Analysis,Logistic Regression,Penalized Logistic Regression (LASSO), LDA, Decision Trees, Bagging, Random Forest
Heart Disease Predictor QDA Framingham Dataset
This project is based on 2 cases studies : Gems Price Prediction and Holiday Package prediction. In the first case study, concepts of linear regression are tested and it is expected from the learner to predict the price of gems based on multiple variables to help company maximize profits. In the second case, concepts of logistic regression and l…
Using classification algorithms to predict the geographical origin of an individual.
Participating in Hacktoberfest 2022. Code performing dimensionality reduction on datasets accepted.
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