Once upon a time, there was a team of data scientists working for a company that provided weather forecasting services. They were tasked with developing an algorithm that could predict future weather patterns based on historical data. The team decided to use linear regression, a technique that could help them identify trends and patterns in the data and use them to make predictions.
The algorithm they developed was called linearRegressionPrediction. It worked by taking in a 2D array of numbers, where each row represented a set of data points and each column represented a different feature or variable. The algorithm then calculated the linear regression coefficients for each column using the calculateLinearRegressionCoefficients function.
Once the coefficients were calculated, the algorithm used them to make predictions for the next set of data points. For each column, the algorithm calculated the predicted value using the slope and intercept, and then rounded the result to the nearest whole number.
To evaluate the accuracy of the predictions, the algorithm used the accuracies array. For each column, it compared the predicted value to the actual value for each row in the array. If the predicted value was within a certain range of the actual value, the algorithm considered it accurate and added 1 to the accuracySum. The overall accuracy was then calculated by taking the average of the accuracies for each column.
After running the algorithm on historical weather data, the team was pleased to see that it was able to accurately predict future weather patterns. The company was impressed with their work and decided to use the algorithm to provide more accurate weather forecasts to its customers. The team was happy to have made a difference in people's lives by helping them better prepare for the weather.