Prithiv Kumar

Background

About Prithiv Kumar

Data Analyst with experience in transforming data into actionable insights. Currently at Axion Ray, with data validation, tagging, and issue identification. Previously, at Mu Sigma, I worked on high-impact projects for Fortune 500 clients, leveraging SQL, Python, Power BI, Spreadsheets and cloud platforms like Azure and AWS. Passionate about data-driven decision-making and optimizing business outcomes. Professional Experience: Data Analyst | Axion Ray - Managed data collection, tagging, dashboard creation, and issue identification to improve data accuracy and insights- Automated processes using Python scripts and leveraged Google Spreadsheets for efficient data analysis. Trainee Decision Scientist | Mu Sigma Inc. During my tenure at Mu Sigma, I worked on high-impact projects for Fortune 500 companies, using cutting-edge technologies to drive business outcomes- Utilized Azure Databricks, Azure Data Factory, and Power BI to process large-scale datasets- Built dynamic dashboards to generate actionable insights, enabling strategic decision-making for clients. Improving NPS Score- Performed extensive Exploratory Data Analysis (EDA) on user feedback data- Provided recommendations that directly contributed to enhancing customer experience.>> Learning Projects: IMDB Movie Data Analysis- Conducted in-depth analysis of the IMDB Movie Dataset, focusing on data cleaning, visualization, and exploratory data analysis- Gained insights using Python libraries like Pandas, NumPy, and Matplotlib to uncover key trends in the movie industry. NLP-based Chatbot for IPL Dataset- Developed an intelligent chatbot using AWS Lex, Lambda function, and S3 to generate responses based on IPL match data- Built a simple webpage UI using HTML, CSS, and JavaScript to test the chatbot, integrating advanced NLP algorithms to provide accurate, conversational insights. Credit Risk Prediction- Predicted customer credit failures using Random Forest Classifier, Support Vector Machine, and Logistic Regression- Implemented data cleaning, plotting, and tuning optimal parameters for enhanced prediction accuracy in Jupyter Notebook.