📈 Built a machine learning model to accurately predict flight ticket prices based on multiple influencing factors such as travel dates, airline, route, and number of stops.
Key Contributions:
Integrated and analyzed datasets from multiple sources.
Processed thousands of records efficiently using Python data processing libraries.
Engineered a Random Forest Regression model achieving 98% accuracy.
Reduced prediction time to 5 seconds, enabling near real-time fare forecasts.
Tools & Technologies: Python, Pandas, Scikit-learn, NumPy, Matplotlib, Random Forest Regression
📈 Designed a business sales performance dashboard with insights on trends and growth.
Tools Used: Tableau, Python (Pandas, Matplotlib)
Highlights: Automated calculations, clear visualizations for decision-making.