predictive

Project Details Table

Project description	The Predictive Modeling project focused on analyzing football player data to understand the key factors that influence a player’s market value and to build models that predict value based on performance and skill attributes. The project involved data cleaning, feature engineering, exploratory analysis, and testing multiple predictive modeling techniques. Model performance was evaluated and compared to identify the most accurate approach for estimating player value.
Domain	Sports Analytics / Data Science
Platform, server and database	Python, SQL
Methodology	Data analysis and predictive modeling lifecycle
Responsibilities	•⁠ ⁠Collected and prepared football player data for analysis. •⁠ ⁠Performed exploratory data analysis to identify patterns and key variable. •⁠ ⁠Engineered and selected features to improve model performance. •⁠ ⁠Built and evaluated multiple predictive models to estimate player market value. •⁠ ⁠Compared model performance using standard evaluation metrics. •⁠ ⁠Documented findings and summarized model insights.
Findings	•⁠ ⁠Identified the most important factors influencing player market value. •⁠ ⁠Developed predictive models with high accuracy compared to baseline approaches. •⁠ ⁠Demonstrated how data-driven modeling can be used to estimate player value effectively.