Basketball Player Performance

The dataset is designed to simulate the performance and training data of basketball players in order to develop personalized training plans using AI. It contains various physical performance metrics and calculates the overall training effectiveness of each player. The goal is to predict how effective a training regimen is based on individual player characteristics, such as speed, endurance, jump height, and strength.

Attributes:
heart_rate:

Description: The player's average heart rate during training sessions.
Type: Integer
Range: 120-180 beats per minute (bpm)
Purpose: Heart rate is a critical indicator of the intensity of physical activity and recovery, helping to understand the cardiovascular demands of training.
speed:

Description: The player's average speed during sprinting or fast-paced drills.
Type: Float
Range: 8-15 meters per second (m/s)
Purpose: This measures the player's agility and quickness, essential for various in-game movements, such as breaking away from defenders or fast breaks.
jump_height:

Description: The player's vertical jump height, measured during training.
Type: Float
Range: 20-40 inches
Purpose: Jump height is a key indicator of a player's explosiveness, particularly important in basketball for actions like rebounding and shooting.
endurance:

Description: The duration (in minutes) the player can maintain a high-intensity workout before exhaustion.
Type: Float
Range: 10-40 minutes
Purpose: Measures the player's stamina and ability to maintain performance over time, particularly important for sustained effort during games.
strength:

Description: The player's maximum lifting capacity or strength-related measure.
Type: Float
Range: 50-150 kilograms (kg)
Purpose: Strength influences a player's ability to compete physically, particularly in post play, defense, and maintaining body control in high-contact situations.
player_efficiency:

Description: A composite metric representing a player's overall efficiency based on in-game performance stats (e.g., shooting percentage, rebounds, assists).
Type: Float
Range: 10-30 (scaled index)
Purpose: A measure that combines various on-court statistics to provide an overall idea of a player's performance efficiency.
training_effectiveness (Target):

Description: The effectiveness of the player's training regimen, classified into three categories: Low, Moderate, or High.
Type: Categorical (integer)
Values:
0: Low Effectiveness – Training plan is not effective in improving performance.
1: Moderate Effectiveness – Training plan has a moderate impact on the player's improvement.
2: High Effectiveness – Training plan significantly improves performance.
Purpose: This target variable is used to assess how well the AI-generated personalized training plans improve player performance, based on the collected data.
Dataset Size:
Total Entries: 500 basketball players (simulated).
Number of Features: 6 features + 1 target variable.