Vignesh Ramakrishnan

• Interacting with the client to get the requirements, ML feasibility to provide the solutions and insights for a Life Science client
• Worked on Cost Optimization project to reduce the CDL infra cost
• Provided ML-based insights for tuning the Databricks Cluster usage.
• Developed Linear Regression, KNN, SVM, Time Series, Random Forest and Logistic Regression.
• Demand Forecasting project to find the pattern, to forecast the fire Kidde demand throughout the year.
• Developed Time Series, KNN, SVM, RandomForest and Linear Regression.
• Developed NLP-based AutoDoc Project to Automate the Documentation process.
• Involved in Spacy, Biobert, Gensim, Fuzzy Logic, Hugging Face, Tesseract, and Easyocr.
• Achieved client satisfaction and loyalty by establishing collaborative relationships, facilitating needs assessment.

• Worked in determining client needs, deliverables, estimates, and feasibility for analytical projects concerning a custom study for a BFSI customer.
• Worked in NLP, Random Forest and Logistic Regression Model to automate and provide insight for Agrochemical customers.
• Developed Holt-Winters, Arima and Sarima models on Time Series data to forecast asset balance and Linear Regression and Timeseries model to predict query execution time.
• Used critical thinking to break down problems, evaluate solutions and make decisions.

• Interpret M&A and Bankruptcy data from primary and secondary sources, involved in statistical techniques and provide Business Insights
• Worked on Decision Tree, Logistic Regression and Random forest
• Proficiency with Linear Regression, Logistic Regression, Random Forest and Decision Tree.
• Utilized analytical and technical expertise to provide insights and proposals to support business improvement
• Actively involved in the quantitative analysis of sophisticated modeling to address business issues.

• Extracted, compiled, tracked and analyzed data to generate reports
• Involved in data preprocessing and data mining
• Leveraged analytical tools to develop efficient system operations.

GPA: 6.3

GPA: 63

GPA: 66