PCM analysis in Boundary Currents
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Updated
Jul 16, 2024 - Python
PCM analysis in Boundary Currents
Web app for identifying a firearm based on a picture
Fraudulent Activities - anomaly detection
The project aims to investigate the influence of classifier training on real-world data versus data generated by a Generative Adversarial Network (GAN).
My portfolio website regarding data science projects. Some visualization and analysis projects reflect work for PITAPOLICY clients.
This project is designed to detect and alert authorities about violent incidents in school environments in real-time. Utilizes a pretrained RESNET model for image classification and Twilio API for sending notifications. Achieves approximately 93% accuracy on the test dataset.
An online convolutional neural network (CNN) model for classifying fashion items.
An online digit classifier using the multi-layer perceptrons (MLP) model.
A Comparative Analysis of Machine Learning Models for Credit Card Transactions with an Emphasis on Maximizing Recall.
Python programming labs done throughout the course CSC406 - Artificial Intelligence
ValX is an open-source Python package for text cleaning tasks, including profanity detection and removal. Now also includes sensitive information detection, and removal.
Classifies the presence of key exogenous variables in projects subject to federal NEPA (National Environmental Policy Act) review using NEPA environmental permitting reports (EISs) as input, and LangChain, Pinecone, and OpenAI API (GPT-4) to parse the reports efficiently with low costs ($0.40/report, on average) and high (>85%) correctness.
In this project, we aim to predict whether a particular customer will switch to another telecom provider or not, a process referred to as churning and not churning in telecom terminology.
Code on my Internship project in DDL lab
This project analyzes tumor cell data from 550 patients using Python. It involves data cleaning, exploratory analysis, feature engineering, and machine learning to classify tumors as malignant or benign. Techniques include PCA, logistic regression, and k-fold cross-validation to ensure model accuracy and reliability.
Quran Recitation Audio Classification project aims to classify different recitations of the Quran using machine learning techniques. It involves preprocessing audio data, extracting features, training models, and evaluating their performance
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