In my project, I focused on analyzing a Research dataset. This dataset was initially split into two distinct subsets: a training set and a testing set. This division is a standard practice in machine learning, allowing for the development of models on one subset (training) and evaluating their performance on another (testing).
The next step involved calculating the average service time for different categories of studies within the dataset. This calculation is crucial as it provides insights into the typical duration associated with each study type, forming a basis for further analysis.
Subsequently, I prepared the features (independent variables) and the target variable (dependent variable) for developing a linear regression model. Linear regression is a statistical method used for predicting a continuous target variable based on one or more features.
The model was then applied to the test set to predict service times. Predictions are essential for assessing the model’s ability to generalize to new, unseen data, which is a critical aspect of machine learning models.
For visualization, I used matplotlib, a popular Python library, to plot the regression line. This line represents the model’s predictions across the range of study types, illustrating the relationship between the type of study and the service time as interpreted by the model.
To evaluate the model’s accuracy, I employed the Root Mean Squared Error (RMSE) metric. RMSE is a standard measure in regression analysis that quantifies the difference between the observed actual outcomes and the outcomes predicted by the model. A lower RMSE value indicates better model performance.
The culmination of this process is a comprehensive figure. This visual representation not only depicts the predicted average service time for each study type as determined by the linear regression model but also provides an intuitive understanding of the model’s predictive accuracy and its fit to the actual data.