Dakshina Moorthy Ramesh

Dynamic Product Analyst with a proven track record at Infoflex Technologies, leveraging Python and data analysis to boost lead conversions and enhance customer engagement. Adept at technical support and communication, I streamlined processes, reducing manual effort by 30%, while delivering actionable insights that drive product improvements and customer satisfaction.

• I studied Slokas and Vedas for 10 years through Sri Sathya Sai Balvikas, winning 2nd prize in a state-level roleplay competition, and completing Level 2 certification at the Shivam Centre in Hyderabad. I volunteered at water camps, conducted Narayana Seva for government hospitals, and donated to the Devnar Foundation for the Blind
• Served as a member and captain of the school's Scouts and Guides, as well as the football and kabaddi teams
• Led as the PR Head of ICTAS, organizing alumni sessions for placements, and coordinating tech and non-tech events

• Data Science Job Simulation Certification, BCG
• Data Analytics Job Simulation, Deloitte Australia
• Python - Kaggle
• Tweet emotion recognition with TensorFlow, Coursera project network
• Working with BigQuery, Coursera project network
• Soft skill development, NPTEL
• Developing soft skills and personality, NPTEL

• Awarded a €5,900 scholarship as a reduction in tuition fees for the MSc in Computing (Artificial Intelligence) program at Dublin City University for the upcoming September 2025 intake, recognizing academic excellence and potential in the field of artificial intelligence

Diabetic prediction using fused algorithm   01/01/23 - 06/30/23, 

• Designed and developed a diabetes prediction model using machine learning techniques, including Support Vector Machine (SVM), Artificial Neural Network (ANN), K-Nearest Neighbors (KNN), Naive Bayes (NB), and Decision Trees.
• Conducted data analysis, built a prediction engine, and fused algorithms with Multilayer Perceptron (MLP) to enhance accuracy., Compared results with existing models and highlighted the system's benefits and real-world applications.

Deep Player Performance Index in T20 Cricket,  03/01/22- 06/30/22 

• Developed the Deep Player Performance Index (DPPI) project to evaluate T20 cricket players' strengths, aiding franchises and auctioneers in selecting players based on current form and team roles.
• Utilized K-means Clustering to categorize players based on performance and Random Forest Algorithm to assess player statistics scraped from ESPN, helping determine the optimal XI for tournaments like the IPL and T20 World Cup.