Vulnerability Analysis

This project explores the versatility of NBA players through comprehensive data analysis combining web scraping, advanced data manipulation, and machine learning techniques. Utilizing data from the 2013-2014 NBA season, the analysis focuses on player salaries and performance metrics to identify patterns and determine what factors contribute to a player's versatility and effectiveness on the court.

🏗️ System Architecture

🔑 Key Feature

Web Scraping:

Automated data collection from websites like Basketball Reference and ESPN to gather comprehensive player statistics and salary information.

Data Merging and Cleaning:

Integration of multiple datasets to create a robust dataset that includes player statistics alongside salary data. Techniques include handling duplicates, merging data frames, and converting data types for analysis.

Statistical Analysis and Visualization:

Application of descriptive statistics, correlation analysis, and advanced visualizations (e.g., Kernel Density Plots) to uncover relationships between player performance metrics and salaries.

Predictive Modeling:

Development of regression models to predict player salaries based on performance metrics, employing techniques like Linear Regression and Ridge Regression to evaluate the impact of various player statistics.

🖥️ Final Output:

• Key Predictors Identified: Determined which player statistics are most strongly correlated with salaries.
• Insights into Player Versatility: Provided insights into players' versatility and their value on the court.
• Data-Driven Decision Making: Enabled NBA teams to make informed decisions based on player performance data.
• Salary Prediction Models: Developed regression models to predict player salaries effectively.
• Enhanced Recruitment and Development: Offered data to help refine recruitment and player development strategies.
• Skills Demonstration: Showcased advanced statistical and machine learning skills applicable in various contexts.

🧠 Skills

• Python (Pandas, NumPy, Seaborn, Matplotlib, Scikit-learn)
• Web Scraping (BeautifulSoup, requests, lxml)
• Data Visualization
• Statistical Modeling
• Machine Learning

🦾Future Work

• Model Enhancement: Explore more complex models such as Random Forest and Gradient Boosting Machines for better predictive performance.
• Expand Data Sources: Incorporate additional data points such as advanced metrics (e.g., Player Efficiency Rating, Win Shares) and off-court factors (e.g., marketability, social media presence) to enhance model accuracy.