How to Build a Deep Learning Model to Predict Workdays from Attendance Data

In 2016 AlphaGo’s victory sparked widespread interest in artificial intelligence (AI), deep learning (DL) and related terms. The article introduces the hierarchical relationship: AI > Machine Learning > Deep Learning (Neural Networks) , and explains that supervised learning—especially classification—creates the most practical value.

From Human Rules to Machine Learning

Using a simple attendance example, the author first derives a manual rule: weekdays are Monday to Friday (1), weekends are Saturday and Sunday (0). This rule is expressed as a function f(x) where x is the day of the week (1‑7). When holidays are considered, additional variables x2 (Gregorian holidays) and x3 (Lunar holidays) are introduced, forming a more complex function f(x1, x2, x3). The manual process shows the limits of human analysis when data volume grows.

Preparing the Attendance Dataset

The raw table contains columns such as date, employee name, employee ID, and clock‑in/out times. After preprocessing, only three columns remain: date, employee_id, and a binary label is_workday (1 for a recorded clock‑in, 0 otherwise). This labeling step is a classic example of supervised‑learning data annotation.

Linear Model Illustration

For educational purposes, the article presents a simple linear equation y = w·x + b, where y is the predicted output (1 or 0), w and b are parameters, and x represents the input features (e.g., numeric date and employee ID). Example equations are shown: w1*27 + w2*10001 + b = 1 for 2019‑03‑27, employee 10001 w1*31 + w2*10002 + b = 0 for 2019‑03‑31, employee 10002

The model starts with random w and b values, and a loss function measures the discrepancy between predictions and the true labels.

Training via Gradient Descent

Iteratively, the loss guides adjustments to w and b (back‑propagation). Because a single linear unit cannot capture the complexity of the problem, multiple w·x + b units are stacked, forming a neural network. Each layer’s output becomes the next layer’s input, enabling the model to learn non‑linear patterns.

Neural Network Basics

The network consists of an input layer (the encoded date and employee ID), hidden layers (stacked linear units with activation functions such as sigmoid or ReLU), and an output layer that produces a probability for the binary classification. Forward propagation computes w·x + b through the layers, while backward propagation updates parameters using gradient descent.

By training on enough labeled attendance records, the model can achieve high accuracy in determining whether a given date is a workday, even when holidays and other exceptions are present.

The article concludes with a brief recap of data labeling, model training, and core deep‑learning concepts, providing beginners a concise overview of how AI can automate rule extraction from real‑world data.