Abstract :

The Rapid Expansion Of Wireless Communication Technologies Such As WiFi, 4G, And 5G Has Significantly Increased The Need For Intelligent Systems Capable Of Analyzing Network Performance And Predicting Signal Behavior. Network Signal Quality Plays A Critical Role In Determining The Efficiency Of Data Transmission, Connection Stability, And Overall User Experience. The Main Problem Addressed In This Research Is The Difficulty Of Accurately Analyzing Network Signal Metrics And Predicting Important Parameters Such As Network Type And Signal Strength Using Traditional Analysis Methods. In Traditional Systems, Network Monitoring Tools Are Primarily Used To Observe Metrics Such As Latency, Throughput, And Signal Strength Through Graphical Dashboards. Several Limitations Exist In These Traditional Approaches. First, They Lack Automated Data Preprocessing Techniques Such As Feature Encoding, Normalization, And Missing Value Handling. To Address These Issues, The Proposed System Introduces A Machine Learning-based Web Application For Network Signal Analytics. The System Is Implemented Using The Flask Framework And Integrates Multiple Machine Learning Models To Analyze Network Signal Datasets. The Proposed System Implements Several Machine Learning Models, Including The Ridge Classifier (RC) And Ridge Regressor (RR) Models, Decision Tree Classifier (DTC) And Decision Tree Regressor (DTR), And A Hybrid Categorical Boosting (CB) Model. In This System, Network Type Prediction Is Treated As A Classification Task, While Signal Strength Prediction Is Treated As A Regression Task. Model Performance Is Evaluated Using Standard Evaluation Metrics Such As Accuracy, Precision, Recall, And F1-score For Classification, And Mean Absolute Error (MAE), Mean Squared Error (MSE), Root Mean Squared Error (RMSE), And R² Score For Regression.

Published:

24-4-2026

Issue:

Vol. 26 No. 4 (2026)

Page Nos:

3023-3034

Section:

Articles

License:

This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.

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