Pratik Khandelwal

Results-driven Market Analyst with Futures First, specializing in data-driven trading strategies and risk management. Proficient in Python and machine learning, I optimized portfolio performance through advanced hedging techniques and statistical analysis. Adept at guiding teams, I effectively communicated complex concepts to enhance strategic decision-making in energy markets.

• SQL, Hackerrank
• Six Sigma Certification, University System of Georgia
• Introduction to Numerical Method using Python, Ludwig-Maximilians-Universität München (LMU)
• PERT and CPM in Project Management, Udemy

• Conducted experimental and predictive analyses on tensile shear strength in FSW of Al-6061-T6, optimizing strength at high rotational speeds and 2.5 tilt angles, and achieved the lowest RMSE (9.21) using Random Forest and SVR (Polynomial Kernel), with Linear Regression showing the highest RMSE (18.21).
• Implemented Otsu's method, image subtraction, U-Net algorithm, and pyramid generation for multi-scale analysis, achieving a training accuracy of 90%-95% and a validation accuracy of 95.65% in defect detection for friction stir welding image.

• Developed a streamlined GUI using python, enabling effortless inputs of over 500 individual student records while maintaining robust management capabilities through seamless integration with an efficient SQLite database backend.
• Incorporated essential CRUD functionalities, enhancing the system's versatility in handling student records and implemented a seamless one-click export feature, enabling quick conversion of data to CSV format for external use.

• Utilized Tableau to create an interactive dashboard from pizza sales data, aiding strategic business choices. Examined hourly and weekly order trends, offering insights into consumption patterns and customer behavior.
• Analyzed 10,000 transactions for pizza sales data based on pizza categories and locations, the top and bottom 5 performing pizzas and extracted insights that enhanced inventory management strategies, reducing stockouts by 15%.