U.S. Tennis Player Performance Analysis Project

Welcome to my U.S. Tennis Player Performance Analysis Project Jupyter Notebook! In this project, I will be analyzing a dataset related to tennis player performance in the United States using a variety of data analysis techniques. My goal is to uncover insights and patterns within the data, investigating factors that might influence player outcomes. To achieve this, I'll be utilizing Python along with key libraries such as NumPy, pandas, Matplotlib, Seaborn, and scikit-learn. These tools will help me manipulate the data, visualize trends, perform statistical analysis, and extract valuable insights that contribute to a better understanding of tennis player performance.

Project Overview

1. Installing Packages: To begin, I will install the necessary packages using the command: !{sys.executable} -m pip install scikit-learn.

2. Importing Libraries: Next, I will import essential libraries:

   • pandas for data manipulation
   • matplotlib and seaborn for data visualization
   • LinearRegression and train_test_split from scikit-learn for building the regression model.

3. Reading Data: The ATP data from the CSV file 'tennis_stats.csv' will be imported into a Pandas DataFrame called 'tennis'.

4. Exploratory Data Analysis (EDA):

   • Display the first 10 rows of data using the head() function.
   • Provide an overview of data types and non-null entries with info().
   • Generate summary statistics using describe() and calculate the correlation matrix for numerical variables.

5. Visualizing Correlation Matrix:

   • Create a heatmap using seaborn to visualize the correlation matrix.
   • This step will reveal relationships between features and outcomes, aiding in identifying predictors of success.

6. Creating Scatterplots:

   • Construct scatterplots for each feature against different outcomes (Wins, Losses, Winnings, Ranking).
   • Visual representation of linear relationships between features and outcomes helps in identifying patterns and correlations.

7. Data Analysis and Regression:

   • Perform a detailed analysis of data distribution and relationships.
   • Handle missing data and outliers appropriately.
   • Split the data into training and testing sets using train_test_split.
   • Build separate linear regression models for various performance outcomes using the LinearRegression class.
   • Fit the models with training data and evaluate their performance on the testing data.

Through these comprehensive steps, the goal is to develop an understanding of the data's structure, unveil significant relationships between playing habits and player outcomes, and ultimately create accurate linear regression models for predicting tennis player performance.