Features (raw model)
Ai classifcation models, from scratch to predict worker productivity based on worker inputs, using a custom made neural network taht utillises sigmoid function and back propogation.
• Transforms raw productivity data into a binary classification problem • Handcrafted Neural Network. Built from scratch using NumPy—no frameworks • Dynamic backpropagation with customizable learning rate • Tracks F1-score, precision, recall, and accuracy to gauge model effectiveness • Plots a loss-over-epochs graph
Features (Scikit Learn Model)
Ai classifcation models, both from scratch and from SciKit learn to predict worker productivity based on worker inputs, using neural networks.
• Uses scikit-learn for a polished, production-ready workflow (train-test splits, scaling, metrics). • Trains a neural network iteratively (warm_start=True) for fine-grained control over epochs. Tracks loss and metrics (F1, precision, recall, ROC AUC) in real time. • Normalizes features to standardized scales for optimal neural net performance • Plots error reduction over epochs to visualize learning dynamics
Components
• SciKit Learn • Sigmoid Function • Python • Back Propogation • Industry-Standard Model Analysis Metrics