Exploring-the-Cricket-World-Cup-An-EDA-Journey

Overview

This repository contains an Interactive Jupyter Notebook demonstrating data exploring, missing values, and visualizations.

Contents

• Data Cleaning and Preprocessing
• Exploratory Data Analysis Using Matplotlib and Seaborn
• Handling missing values
• basis plots
• Still exploring
• Data Transformation
• Data visualization
• exporting clean data
• Statistical Analysis

libraries Used

• pandas
• NumPy
• seaborn
• matplotlib
• pandas
• scipy

Clone the repository to get a local copy on your machine.

Ensure you have Jupyter installed or use Google Colab to open the .ipynb files. Follow the notebooks in sequential order for a structured learning experience.

Contributing

Contributions are welcome! If you have suggestions or improvements, please open an issue or a pull request.