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Part 1:

Task 1

The first task is based on envisioning the difference between CCAO Mailed and Certified Results , which is based on four algorithmic variations of models that will be expected with a high degree of precision. Therefore, on that note, in this case, “Linear Regression”, “Support Vector Classifier (SVC)”, “Random Forest Classifier”, and “Radiant Boosting Classifier”, models are operated. The process of each of the models is described below.

Linear Regression

The association between the CCAO sent and certified findings is modeled using linear regression. It is taken for granted that the variables have a linear connection. The model's initial stage involves handling values that are missing and encoding categorical variables (ARAS et al. 2021). Next, in order to normalize numerical characteristics, Feature Scaling is developed. With the training set of data, fit a linear regression model . Measurements like Mean Absolute Error (MAE) as well as Mean Squared Error (MSE) can be used to evaluate the model's performance.

Algorithmic Models1

Figure 1: Linear regression model evaluation

Support Vector Classifier (SVC)

For classification problems, SVC is employed. Here, it's modified to forecast the discrepancy between sent CCAO results and certified results. This model's data preprocessing is comparable to linear regression.

Algorithmic Models4

Figure 2: SVC model evaluation

The purpose of feature scaling is to make SVC indispensable. Utilizing the training data, train a Support Vector Machine classifier. Subsequently, the model's assessment component is developed to evaluate the model's F1 score and accuracy (AYON et al. 2022).

Random Forest Classifier

An ensemble learning technique called Random Forest creates a large number of decision trees for categorization. Information This specific process's preprocessing is identical to that of SVC and linear regression.

Algorithmic Models2

Figure 3: Random forest model evaluation

Random Forest Classifier creates the model's training. Hyperparameter tuning, which is designed to tune hyperparameters for improved performance, is the next phase. Therefore, in the review portion the evaluation of this model's precision, with exactness, and recall is examined.

Gradient Boosting Classifier

Gradient Boosting combines weak learners to produce a powerful predictive model by building a model step-by-step. The four steps in this approach are data preprocessing, model training, tuning of hyperparameters, and evaluation. As such, this model's initial step is comparable to earlier models' steps (EBRAHIM et al. 2023).

Algorithmic Models3

Figure 4: Gradient Boosting Classifiermodel evaluation

A Gradient Boosting Classifier is trained to construct the model training portion. Moreover, in the stage of “A hyperparameter Tuning” the optimum hyperparameters are produced. Recall, accuracy, and precision are evaluated during the assessment process.

Model Comparison and Justification

After evaluating all four models the accuracy level of the Support Vector Classifier (SVC) and Gradient Boosting Classifier is most, that to 97%. Analyze models according to assessment criteria. Think about the trade-offs between complexity and interpretability. Choose the model that strikes the optimal balance between generalizability and accuracy. Justify your decision with reference to the particulars of the information set and the issue at hand. The best-performing approach for each work should be chosen after the models have been assessed and compared using pertinent metrics. This selection process should take into account several aspects, including interpretability, generalization, and the particular needs of the task at hand.

Task 2:

Task 2 of this specific task is based on the "Predicting CCAO Adjustment Indicator," which is based in this instance on four algorithmic model modifications that are anticipated to be highly precise. That being said, the following models are used in this instance: "Linear Regression," "Support Vector Classifier (SVC)," "Random Forest Classifier," and "Radiant Boosting Classifier." As a result, this model is modified for binary classification in the context of linear regression in order to forecast the CCAO adjustment indicator. Analyze the model's performance with F1 score and accuracy, for example (FRANČIĆ et al. 2023). Following that, "Support Vector Classifier (SVC) can be applied for binary classification" is the situation in question. Furthermore, a Support Vector Machine classifier was used to train the model in this instance for binary classification. In order to compare and support the selection of the best-performing model, the Random Forest Classifier as well as Gradient Boosting Classifier are developed using similar ideas as in Task 1.

Part 2:

Task 3:

The results of the thorough investigation of the four algorithmic variants in Task 1 and Task 2 show that the highest accuracy levels, up to 97%, are constantly displayed by the Support Vector Classifier (SVC) as well as the Gradient Boosting Classifier. The assessment criteria emphasize the trade-offs between interpretability and model complexity and include F1 score, accuracy, and recall. The optimal model should be chosen after taking into account the particular requirements of the work, balancing accuracy and generalizability. The potential benefits of AutoML in automation choice of model and hyperparameter tweaking are shown by the comparison of manual, AutoML, and deep learning models. The job demands, dataset size, as well as the trade-off between comprehension and predictive capability ultimately determine which model is best. The best strategy for attaining optimal model performance must be chosen after giving careful thought to these variables.

Predicted on the idea that variables have a linear relationship, the first method, called Linear Regression, explores the link between CCAO sent and certified findings. The initial phases of the model entail coding categorical variables and filling in missing data. Feature Scaling is used to standardize numerical features. The training dataset is then used to construct a linear regression model. Next, measures like Mean Absolute Error (MAE) and Mean Squared Error (MSE) are used to evaluate the model's performance.

Task 4:

An extensive understanding of model performance may be obtained by contrasting manual models, AutoML models, and deep learning models. Because autoML automates the process of selecting a model and fine-tuning its hyperparameters, it is anticipated to provide a competitive edge. Whether the automated technique performs better than human-engineered models will be shown in the chart that contrasts AutoML with manual models. Since the model used for deep learning is a sophisticated neural network, it could perform better than conventional methods in cases when the dataset is big and contains complicated patterns (KULYUKIN et al. 2023). However, interpretability suffers as a result. The outcomes should be thoroughly examined, taking into account both the models' usefulness in a real-world setting and accuracy measures The particular needs of the task determine which model performs the best. Assuming interpretability is significant and the data set is not too big, a classic model with some fine tuning could work just well. However, the deep learning model could have the highest prediction potential if the dataset is large and complex (GRGIĆ et al. 2021).

Comparing the results of CCAO (Certified and Corrected Assessment Office) Mailed and Certified Results using four different algorithmic variations all aiming for higher precision is the main topic of this study. A variety of techniques are used, including "Gradient Boosting Classifier," "Linear Regression," "Support Vector Classifier (SVC)," and "Random Forest Classifier."

References

ARAS, M.R., TREFA MOHAMMED, A.M., MIRAN, H.M., ZANA, Q.O. and FADIL, A.K., 2023. Orthodontic Implementation of Machine Learning Algorithms for Predicting Some Linear Dental Arch Measurements and Preventing Anterior Segment Malocclusion: A Prospective Study. Medicina, 59(11), pp. 1973.

AYON, R., ZUBAYER, T.A., TABASSUM, N., ISLAM, M.N. and SATTAR, M.A., 2022. CurFi: An automated tool to find the best regression analysis model using curve fitting. Engineering Reports, 4(12),.

EBRAHIM, M., AHMED AHMED, H.S. and MESBAH, S., 2023. Accuracy Assessment of Machine Learning Algorithms Used to Predict Breast Cancer. Data, 8(2), pp. 35.

FRANČIĆ, V., HASANSPAHIĆ, N., MANDUŠIĆ, M. and STRABIĆ, M., 2023. Estimation of Tanker Ships’ Lightship Displacement Using Multiple Linear Regression and XGBoost Machine Learning. Journal of Marine Science and Engineering, 11(5), pp. 961.

GRGIĆ, V., MUŠIĆ, D. and BABOVIĆ, E., 2021. Model for predicting heart failure using Random Forest and Logistic Regression algorithms. IOP Conference Series.Materials Science and Engineering, 1208(1),.

KULYUKIN, V.A., COSTER, D., TKACHENKO, A., HORNBERGER, D. and KULYUKIN, A.V., 2023. Ambient Electromagnetic Radiation as a Predictor of Honey Bee (Apis mellifera) Traffic in Linear and Non-Linear Regression: Numerical Stability, Physical Time and Energy Efficiency. Sensors, 23(5), pp. 2584.

SREEPARVATHY, C.M. and SRAVANTI, C., 2022. Linear Regression Analysis on the Contributory Factor of Accident Identified in Road Safety Audit Using Python. IOP Conference Series.Earth and Environmental Science, 982(1), pp. 012051.

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