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SAIDEEP KONDUR(Street Address )-765-4961 EMAIL AVAILABLE https://LINKEDIN LINK AVAILABLEEDUCATIONMaster of Science in Computer Science (M.S) August Street Address  - May 2024 Clemson University - GPA: 3.66 Clemson, SCBachelors in Electronics and Telecommunication Engineering (B.E) Graduated, May Street Address  Mumbai University - GPA: 3.20 Mumbai, IndiaTECHNICAL SKILLS Languages: Python, Java, C, R, HTML/CSS, Javascript Tools and Technologies: Jupyter Notebook, Spyder, VSCode, Github, Postman, Tableau, PowerBI, Excel, Powerpoint, Word, Git, Slack Frameworks/Libraries: Pytorch, Keras, Django, React, NodeJS, Bootstrap, Scikit, NumPy, Pandas, Plotly, Seaborn, Pytorch Databases: MYSQL, DB2, MongoDB, Cassandra, NoSQL Cloud Services: Amazon (S3, Redshift, Glue, Athena, Lamda), Azure(Data Factory, Databricks, Data Lake Storage). CERTIFICATIONSAWS Certified Cloud Practitioner Amazon Web Services January 2024 EXPERIENCESoftware  Revitalized Developer college (G.A)and, Clemson departmental University websites by leading a comprehensive design needs assessment and guiding a seamless January 2023 - Current transition to modern format, leveraging expertise in content management systems like Cascade and Wordpress. Evaluated a PostgreSQL database containing personnel information for the university, proactively purged individuals who had departed or were no longer affiliated with the institution, and diligently maintained a database to ensure accurate and up-to-date records. Improved user experience by performing comprehensive analysis of Google Analytics data, identifying various compliance issues. Streamlined page optimization efforts for Quality Assurance and SEO by achieving a 94% reduction in errors and eliminating broken links, resulting in significant efficiency improvements. Machine  Collaborated Learning Intern, cross-functionally Verzeo-Mumbai on a music store project to create a data-driven recommendation engine leveraging October python 2019 libraries - December 2019(numpy, pandas, matplotlib, scikit-learn); analyzed user age and gender to deliver personalized recommendations, driving a 20% boost in customer engagement and a 15% increase in average sales conversion rate. Utilized data analysis and feature engineering techniques to enhance accuracy of personalized music album suggestions, resulting in improved sales conversion rates. Demonstrated effective communication within the team by sharing project updates and insights, ensuring cohesion with overarching project objectives.ACADEMIC PROJECTSPower BI Project Global Sales June 2022 Data Visualization February 2023 - April 2023 Designing dashboard integrating DAX Leveraged DAX functions and expressions to create calculated columns, measures, and dynamic calculations for in-depth data analysis. Utilized SQL queries to retrieve and transform data from relational databases, resulting in a 30% reduction in data processing time and optimizing data retrieval. Developed models and created interactive dashboards to showcase profitability insights about the data. Tableau Project Sales Insights Data Visualization May 2023 - July 2023 Designing an insightful dashboard Demonstrated Data Preprocessing using SQL to study and gain more information about the global superstore data. Utilized ad-hoc analysis using tableau to get insights from complex data resulting in solving business problems with ease. Created individual worksheets of data variables and built interactive dashboards in Tableau which helps me gain a handful of intuitive ideas and business insights.Speed Detection for Traffic Safety Machine Learning and Image Processing August 2021 - May 2022 Detecting speed of speeding vehicles Implemented the 'YOLO' object detection algorithm and image processing collaboratively to achieve real-time vehicle detection accuracy of over 94%. Applied a teamwork approach in utilizing the Euclidean Distance formula and speed formula for assigning IDs to individual vehicles and ensuring accurate speed calculations with a high precision of 86%. Established organized image storage in a dedicated folder, implementing a red bounding box system to instantly identify vehicles exceeding speed limits. Enhanced vehicle recognition through the utilization of unique vehicle IDs assigned to each vehicle. Built a system to generate a summary text file detailing total road-crossing vehicles and speed-limit violators. Employed data visualization for insightful graph analysis of output.