AI | ML | DL Fully Covered for Basic
This project predicts loan approval outcomes (Approved/Rejected) using a PyTorch neural network. It includes data preprocessing, train/validation/test split, model training with BCEWithLogitsLoss, and inference with probability-based classification.
PyTorch implementation of binary and multi-class focal loss functions
Group work on MultiLayer Perceptron (MLP) and Hyperparameters Optimization
This project implements a machine learning model to detect phishing websites using a Multi-Layer Perceptron (MLP) neural network. The model analyzes various features extracted from URLs and website characteristics to classify them as either legitimate or phishing. The dataset used is dataset_phishing.csv, which contains 87 features and a binary lab
This project classifies SMS messages as spam or ham using a feedforward neural network in PyTorch with a bag-of-words representation. It includes train/validation/test splits, performance evaluation (accuracy, sensitivity, specificity, precision), and saving the trained model and vectorizer for reuse in inference.
A simple PyTorch-based neural network that classifies student exam outcomes (Pass/Fail) using study hours and previous exam scores. Implements dataset splitting (train/val/test), mini-batch training, and evaluation with configurable hyperparameters.
PyTorch implementation of binary and multi-class focal loss functions
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