AI Productivity Predictor
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.
• 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
• 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
• 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
• SciKit Learn
• Sigmoid Function
• Python
• Back Propogation
• Industry-Standard Model Analysis Metrics
Repo
• Sigmoid Function
• Python
• Back Propogation
• Industry-Standard Model Analysis Metrics