Unsupervised-ML-t-SNE-Data-Mining-Cancer. Import Libraries, Import Dataset, Convert data to array format, Separate array into input and output components, TSNE implementation, Cluster Visualization

Creating empty dataframe, data normalization, Dimensionality reduction, Outlier detection, Overfitting of model and its solution, Remove column with zero values, Replace NA with zeros.

MNIST Digit recognition using machine learning techniques

Analysing different dimensionality reduction techniques and svm

Exploratory Data Analysis with TSNE ON_ DonorsChoose data set

Word Embedding visualization with T-SNE (t-distributed stochastic neighbor embedding) for BERT, ALBERT, ELMO, ELECTRA, XLNET, GLOVE.

This project used NLP to topic model classical music based on musical chords. Classical composers were then clustered using the topics from the NLP, and then a neural network was used to generate original music based on trained music of the different classical composers. All of these metrics were used as a model for complexity of music.

3D T-SNE graphs with sliders and checkboxes to visualize the T-SNE cloud at every epoch for specific labels. Optionally you can also track specific datapoint by labeling it with a unique marker.

TensorFlow Deep Feature Consistent VAE Implementation on the Kaggle Fashion Dataset

Pen-Based Recognition of Handwritten Digits.

a simple python script to train and visualize a WordsEmbedding

Repository of Data Analysis and Visualization Homework: Contains my work for the class using Tableau, Excel, and Python. Explore the files for insights and knowledge.

IU Projects

A python based tool for clustering text , CSV and logs.

Predicting Colorado forest cover types using diverse ML models for classification. Baseline creation, feature selection, comparison, and tuning optimize accuracy in this University of Ottawa Master's Machine Learning course final project (2023).

Data visualization by implementing T-sne and PCA