Market Trend Prediction Model

About the Project

I worked on a self-initiated project, Rohan Verma developed a predictive analytics model that forecasts market trends using a hybrid approach of technical indicators and machine learning algorithms. The goal of the project I was to empower decision-makers in the retail sector with foresight into short and mid-term market shifts. Using datasets from Kaggle and historical sales records, he built a regression model leveraging Random Forest and XGBoost to detect patterns across price movements, consumer demand cycles, and seasonal trends. The model incorporated macroeconomic indicators such as interest rates and inflation to improve prediction accuracy. Rohans expertise in Google Data Analytics I was evident in the meticulous data cleaning, transformation, and model validation phases. His insights were summarized through dynamic dashboards that could simulate possible outcomes under different economic scenarios. This model not only showcased his technical proficiency but also reflected his strong business acumen and understanding of real-world market behavior. The project I was presented at a knowledge-sharing session within Infosys’ analytics team and received acclaim for its practical applicability.