This paper is published in Volume-10, Issue-6, 2024
Area
Machine Learning, Artificial Intelligence
Author
Suyog Karpe, Atharva Ostwal, Yashshree Kirad, Atharva Pawar
Org/Univ
PVG'S COET, Pune, Maharashtra, India
Keywords
Data Integration, ML Model Development, Predictive Analytics, User Interface, Validation, Deployment
Citations
IEEE
Suyog Karpe, Atharva Ostwal, Yashshree Kirad, Atharva Pawar . ConnectCraft – Simplifying Network Choices, International Journal of Advance Research, Ideas and Innovations in Technology, www.IJARIIT.com.
APA
Suyog Karpe, Atharva Ostwal, Yashshree Kirad, Atharva Pawar (2024). ConnectCraft – Simplifying Network Choices. International Journal of Advance Research, Ideas and Innovations in Technology, 10(6) www.IJARIIT.com.
MLA
Suyog Karpe, Atharva Ostwal, Yashshree Kirad, Atharva Pawar . "ConnectCraft – Simplifying Network Choices." International Journal of Advance Research, Ideas and Innovations in Technology 10.6 (2024). www.IJARIIT.com.
Suyog Karpe, Atharva Ostwal, Yashshree Kirad, Atharva Pawar . ConnectCraft – Simplifying Network Choices, International Journal of Advance Research, Ideas and Innovations in Technology, www.IJARIIT.com.
APA
Suyog Karpe, Atharva Ostwal, Yashshree Kirad, Atharva Pawar (2024). ConnectCraft – Simplifying Network Choices. International Journal of Advance Research, Ideas and Innovations in Technology, 10(6) www.IJARIIT.com.
MLA
Suyog Karpe, Atharva Ostwal, Yashshree Kirad, Atharva Pawar . "ConnectCraft – Simplifying Network Choices." International Journal of Advance Research, Ideas and Innovations in Technology 10.6 (2024). www.IJARIIT.com.
Abstract
In our increasingly connected world, network quality is a fundamental concern for users. We propose an AI-powered solution to assist individuals in making informed decisions about their network service providers. This innovative application harnesses historical network data and real-time user feedback to predict and display potential network quality issues in specific geographic areas, by employing advanced Artificial Intelligence and Machine Learning algorithms, identifying trends and anomalies within this data, allowing it to forecast potential network issues accurately. Key features include predictive analytics, a visual representation of network quality problems on an interactive map, integration of real-time user feedback, provider comparisons based on historical data and reviews, personalized recommendations, and proactive alerts about potential network problems. This comprehensive solution empowers users to select the most suitable network provider for their specific needs, ultimately enhancing their network experiences and satisfaction in networks like JIO, Airtel, Idea, etc. It bridges the gap between consumers and network providers by providing transparency and data-driven insights into network quality, ensuring users can confidently make choices that align with their connectivity requirements.