This repository contains Natural Language Processing Projects like Sarcasm Detection, Quora Insincere Questions Classification & Edgar Sentiment Analysis
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Updated
Jul 14, 2020 - Jupyter Notebook
This repository contains Natural Language Processing Projects like Sarcasm Detection, Quora Insincere Questions Classification & Edgar Sentiment Analysis
This project focuses on applying advanced simulation methods for derivatives pricing. It includes Monte-Carlo, Variance Reduction Techniques, Distribution Sampling Methods, Euler Schemes, and Milstein Schemes.
Fast Online Triplet mining in Pytorch
mcs_kfold stands for "monte carlo stratified k fold". This library attempts to achieve equal distribution of discrete/categorical variables in all folds. The greatest advantage of this method is that it can be applied to multi-dimensional targets.
The objective is to analyze flight delays in the United States. Data from airlines, airports, and runways will be collected and processed. Machine learning models will be built using logistic regression, decision trees, and XGB classifiers. Visualizations will be created in Tableau, and Excel dashboards and SQL queries will be used for analysis.
BI Master - Automated methods to detect and classify human diseases from medical images. Convolutional Neural Network, Data Augmentation, Transfer Learning, Tensorflow, Keras, Xception, ImageNet, StratifiedKFold.
WiDS Datathon 2020 on patient health through data from MIT’s GOSSIS (Global Open Source Severity of Illness Score) initiative.
Loan Approval Prediction
CSCI316 Group assignment 1
Data sampling library
An optimal stratified sample design for Commodity Flow Survey (CFS) based on Simulated Annealing and Genetic Algorithm. A script in Procedural PostgreSQL is used to generate a frame with 100,000 records based on publicly available data.
Predicting house prices in an area
Data consists of tweets scrapped using Twitter API. Objective is sentiment labelling using a lexicon approach, performing text pre-processing (such as language detection, tokenisation, normalisation, vectorisation), building pipelines for text classification models for sentiment analysis, followed by explainability of the final classifier
A C library with Python bindings for efficient stratified random sampling from binary buffers or files.
Perform Data Sampling with Python
Credit card fraud detection using various sampling methods and machine learning algorithms.
Data sampling library
Web scraper to get professor information, and a mass emailer that sends a website with a survey.
Employing advanced techniques, the project seamlessly integrates binary and multiclass classifiers for character classification. It offers a comprehensive analysis and adeptly addresses challenges in the realm of computer vision.This project was part of my uOttawa Master's in Computer Vision course (2023).
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