Cricket Player Segmentation Using K-Means Clustering

• Overview

  Segment cricket players based on performance metrics using K-Means clustering to identify player roles and strengths.

• Key Points Preprocess and scale player data

  Use Elbow Method to find optimal clusters (K=3)

  Group players into 3 clusters: impact players, balanced players, and top performers

  Visualize clusters with 2D and 3D plots

  Useful for team selection, scouting, and strategy

• Dataset

  Includes player stats like runs, average, strike rate, and career span.

• Tools

  Python, Pandas, Scikit-learn, Matplotlib, Seaborn, Plotly