SENTHILKUMAR.M

Developed and deployed an HSCode prediction model using AP clustering and Logistic Regression, improving classification accuracy by 10% through a Deep Learning Feed Forward Network.

Engineered an automated invoice data extraction system using ABBYY and Tesseract OCR, building a classification module and Flask API to process and store extracted fields in HazzelCast Database.

Designed and implemented a Cage Prediction module utilizing deep learning to forecast shipment caging probability, enabling the client to avoid customs penalties and accelerate delivery times.

Awarded the ACE AWARD for outstanding performance, recognizing significant contributions to project success and innovation in data science solutions.

Developed and deployed a classification model using Logistic Regression, KNN, SVM, Random Forest, and Deep Learning to predict Network Key Performance Indicators (KPIs) for UK customers, leading to improved network performance and degradation mitigation.

Implemented an NLP CNN Multi-classification Model with GloVe embeddings to analyze and classify network events, identifying coverage gaps, poor quality, and low throughput issues from large datasets.

Built and deployed a Logistic Regression classification model for traffic churn capacity modeling, delivering critical insights into churning behavior to enhance network retention and product development.

Received the 'Spot Recognition Award' for exceptional performance on the ATT & T LTE project, acknowledging significant contributions to network optimization.

Developed a Python-based Cell Reference Generator utilizing Selenium and Beautiful Soup to automatically extract and process high volumes of mobile site information from MIST, streamlining data acquisition.

Automated PowerPoint slide preparation for drive plots using the PPTx module, ensuring consistent formatting and accurate visual representation of network performance data.

Conducted comprehensive Mobile Network KPI analysis, including data fetching from OSS, KPI analysis, and providing implementation recommendations to enhance network performance.

Analyzed drive log files via Actix analyzer and managed pre- and post-launch network site activities for AT&T, ensuring optimal network functionality.

Conducted extensive SCFT, Cluster, Migration, and Highway Drives, verifying critical network parameters like SHO, IRAT HO, cell reselection, CS call, and HSDPA throughput from drive logs.

Successfully reduced pilot pollution and overshooting, optimizing antenna configurations (height, tilt, azimuth) across network cells/sectors to enhance overall network performance.

Troubleshot and resolved critical network issues, including call drops and handover failures, while also performing sector swap detection and rectification.

Actively engaged with customers to address and resolve network-related complaints, ensuring high levels of client satisfaction.