Forecasting the Adoption Process of Technology Using AI Methods

Python Code for MKT - Consumer and Brand Research Course for Simon Business School

This project has two parts a) Computer-activ Project using Linear Regression which aims to predict the percentage time the computer remains in user mode. b) Women's contraceptive Project using Classification techniques to classify how many women use or not use contraceptive

Chemometrics library for data fusion, model training and prediction of data from multiple sensor sources.

ML-algorithms from scratch using Python. Classic Machine Learning course.

A time series analysis method using Shapelets and Fischer Linear Discriminant.

A review of the most popular topic modeling techniques, featuring hands-on tutorials.

"This repository contains implementations of Linear Discriminant Analysis (LDA) algorithms for data mining tasks. Linear Discriminant Analysis is a dimensionality reduction technique used to find a linear combination of features that characterizes or separates classes of data."

This project focuses on analyzing tweets from Twitter using topic modeling techniques and interactive visualizations. It employs Latent Dirichlet Allocation (LDA) to discover topics within the tweet data and generates interactive word clouds based on topic-term strengths derived from the model. Users can explore topics and related tweets interactiv

This is my final year project "customer reviews classification and analysis system using data mining and nlp". It analyzes and then classifies the customer reviews on the basis of their fakeness, sentiments, contexts and topics discussed. The reviews are taken from various e-commerce platforms like daraz and amazon.

High performance topic modeling for Ruby

Topic Embedding, Text Generation and Modeling using diffusion

This study explores the themes of destruction and hope in the Prophets section of the Bible using topic modeling and network analysis. By applying Latent Dirichlet Allocation (LDA), we identify central topics across the Prophets books, revealing key themes such as God’s glory, judgment, and restoration.

Implementation of algorithms such as normal equations, gradient descent, stochastic gradient descent, lasso regularization and ridge regularization from scratch and done linear as well as polynomial regression analysis. Implementation of several classification algorithms from scratch i.e. not used any standard libraries like sklearn or tensorflow.

Proyecto de investigación en ML para identificar factores genéticos en pronóstico de lesiones pre-tumorales. Aprendizaje no supervisado para discernir perfiles genéticos distintivos entre grupos de buen y mal pronóstico, mejorando detección y tratamiento temprano del cáncer.

A Julia package for multivariate statistics and data analysis (e.g. dimension reduction)

PRML Lab Assignments (CSL2050), including models made from scratch.