Data Cleaning and Wrangling

Data cleaning is an essential part of machine learning. Without properly cleaned data, the performance of machine learning models can suffer significantly. Dirty data, which may contain errors, inconsistencies, missing values, or irrelevant information, can lead to inaccurate predictions, overfitting, or misleading insights. Data cleaning ensures that the dataset is reliable, accurate, and relevant

Ataşehir Golf Club Data

I obtained the data for my golf app through the Ataşehir Golf Club players. The data used to train included 252 golfers, which I anonymized for privacy issues.

Data Description

The file `golfer_data_252.csv` contains a dataset of 252 golfers, each with various performance metrics and demographic details relevant to FairwayFriend.

Columns and Descriptions

GolferID: Unique identifier for each golfer, ranging from 1 to 252. This ID is a primary key for distinguishing each golfer in the dataset.

Name: Placeholder names are assigned for each golfer in the format `Golfer_1`, `Golfer_2`, ..., up to `Golfer_252`. In the real application, this field contains actual names of the golfers, which is beneficial for user personalization or historical tracking.

Age: Represents each golfer's age, ranging from approximately 25 to 44 years. This age range is created with a modulo operation to simulate realistic age distributions typical of many golfing communities. Understanding age distribution aids in identifying trends across different age groups in terms of performance.

Handicap: Handicap is a numeric measure representing each golfer's skill level. In this dataset, handicaps range from 5 to 24, cycling in increments to simulate a variety of skill levels. Handicap is critical for analyzing skill differences and adapting recommendations within the app to suit players’ expertise levels.

AverageScore: The average score per round of each golfer, ranging from approximately 75 to 79. This metric is essential for performance evaluation, goal setting, and tracking progress over time. In FairwayFriend, this could help tailor training suggestions based on scoring trends.

FairwayHitPercentage: This represents the golfer’s accuracy in hitting the fairway, with values ranging from 60% to 79%. Fairway accuracy is a key component of a golfer’s performance, often correlated with consistent play and lower scores. Analyzing this data can help identify which golfers might benefit from accuracy-improvement exercises.

GreenInRegulationPercentage: This statistic, ranging from 55% to 69%, indicates the percentage of holes in which a golfer hits the green in regulation (within par for that hole). Tracking greens in regulation helps identify a player’s ability to reach the green on target, contributing to lower scores and better overall play. This data could inform tailored drills focusing on approach shots or distance control.

PuttingAverage: The average number of putts per hole, ranges between approximately 1.8 and 2.3. Putting is one of the most significant contributors to a golfer’s score, so understanding averages here can highlight golfers’ strengths and weaknesses. FairwayFriend could use this data to recommend targeted putting drills or personalized green strategies.

DrivingDistance: Measures the average driving distance (in yards) for each golfer, with values ranging from 280 to 299. This statistic helps identify power hitters and those who may need to focus on distance. Driving distance data is especially useful in analyzing how players perform on long holes and could inform coaching recommendations for strength and power development.

Uses in FairwayFriend

Each data point offers valuable insights that can enhance the experience and personalization for users in the FairwayFriend golf app:

Performance Analytics: By aggregating these metrics, FairwayFriend provides personalized feedback based on players’ scoring averages, fairway accuracy, and greens in regulation. This tailored feedback can help users set realistic goals.

Customized Drills: For players with lower fairway accuracy or green-in-regulation stats, the app could suggest specific practice drills focused on these areas. Similarly, putting averages could help identify users needing more intensive putting practice.

Progress Tracking: The file’s structured format allows tracking progress over time by comparing a player’s current stats against historical data, thus fostering motivation and goal-setting.

Golf Data Explanation

Swing Speed (mph): Measures the speed of the golfer’s swing in miles per hour. Swing speed is a key factor in determining ball speed and overall driving distance.
Ball Speed (mph): The speed of the ball immediately after impact, measured in miles per hour. Ball speed is crucial for assessing the power of the shot and is often correlated with swing speed and impact efficiency.
Launch Angle (°): The angle at which the ball leaves the clubface after impact, measured in degrees. Launch angle affects the trajectory and distance of the shot, making it a critical factor in optimizing shot distance and control.
Backspin Rate (RPM):
- The rate of spin on the ball after it is struck, measured in revolutions per minute. Backspin helps the ball maintain lift and influences both carry distance and stopping power on the green.
Carry Distance (yards): The total distance the ball travels in the air, measured in yards, excluding any roll after landing. Carry distance is an essential metric for evaluating a player's distance control and effectiveness with various clubs.