Building a Basic AI for Predicting Employee Performance Implementing a Simple Linear Regression from Scratch

Developing a system that can use employee-related data, such as experience level, education, job role, and previous performance indicators, to forecast future performance is the first step in creating a basic AI model for employee performance prediction using linear regression. Putting this into practice with a basic linear regression model created from scratch offers a fundamental method for comprehending prediction challenges. The first step in the process is to collect and arrange pertinent data, with employee performance serving as the target variable. This data is usually continuous and numerical. A line that minimises the sum of the squared differences between the actual and expected performance values is fitted to the data points in order to apply linear regression. Finding the slope and intercept of the best-fit line, which are obtained using gradient descent or data-driven closed-form solutions, is one of the crucial computations. In order to minimise error, the model learns by iteratively modifying these parameters. Following training, the model’s ability to forecast performance ratings can be evaluated using fresh employee data. By doing this from the ground up instead of using machine learning libraries, one can gain a deeper understanding of the underlying mathematics and optimisation methods like gradient descent and mean squared error. Assuring data quality and managing multicollinearity when extra variables are added present difficulties. All things considered, this linear regression model provides a simple and understandable method for forecasting employee performance, which is helpful for talent management and HR analytics.