NBA Data Analysis - README

Project Overview

This project focuses on designing a solution architecture and implementing a data analytics system to analyze NBA basketball matches from the 2000-2001 season. The system supports both real-time and historical data analysis, providing insights for basketball analysts and team management groups.

Tasks and Deliverables

Task 1 - Designing a Solution Architecture

Design a system architecture to analyze real-time and historical data.

Task 2 - Data Analysis

Perform data analysis using various tools and frameworks:

1. Hadoop MapReduce
2. Hive or Pig
3. Spark

Task 3 - Machine Learning with Spark MLlib

Build a predictive model to estimate the average points a player must score to ensure a win for their team.

Task 4 - Presentation of the Analysis

Create a static web page or dashboard to display the results of the analysis:

1. Visualization Topics:

• Most scoring quarter for each team.
• Top 5 teams based on total points.
• Percentage of players scoring 40+ points in a single match.
• Total matches won and lost by each team.

2. Design Considerations:

• Ensure clear presentation of data.
• Focus on user experience (UX) and user interface (UI) aspects for better accessibility and understanding.