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bytetrack

[ECCV 2022] ByteTrack: Multi-Object Tracking by Associating Every Detection Box

default_bank

The loan_data data frame contains information on 3-year loans that were originated in 2013 by a local bank for customers residing in the United States. The company is looking to see if it can determine the factors that lead to loan default and whether it can predict if a customer will eventually default on their loan at time of loan origination. The goal is to become better at identifying customers at risk of defaulting on their loans to minimize the bank’s financial losses. The data set contains a mixture of applicant demographics (gender, age, residence, etc..), financial information (income, debt ratios, FICO scores, etc..), and applicant behavior (number of open accounts, historical engagement with the bank’s products, number of missed payments, etc. . . ) Exploratory Data Analysis is conducted to examine the data and explore possible important variables. Variable Importance is determined by creating a Gini Index using the varImpPlot() function in Random Forest. After variables are selected, three different models are fit on the training data set including Random Forest, Decision Trees, and K-Nearest Neighbor. The models are then used on the test data set, and a recommendation is given to the bank through an analysis of F1 scores and false negative rates.

kannada_mnist_conv2d

Classifying handwritten digits using a Convolutional Neural Network and Data Augmentation

loco-yolov8

Using YOLOv8, ByteTrack, and Supervision to detect Logistics Objects in Context (LOCO)

pyceres

Factor graphs with Ceres in Python

sticky-notes

This is was a 1 day project I did to detect sticky notes on walls and output its content into a .txt file

student_pca

Unsupervised Learning using PCA

titanic_dt

Predicting Titanic survival rates using a Decision Tree Classifier and a Randomized Search Cross Validation

trackingandcaptioning

videocaptioningwithblip

vitb16-clustering