EDA_IPL_data

This repository contains a comprehensive exploratory data analysis (EDA) of the Indian Premier League (IPL) data spanning from 2008 to 2019.The goal of this project is to uncover insights, patterns, and trends in the IPL matches through detailed analysis and visualizations.

Contents

• IPL_EDA_Notebook.ipynb: Jupyter notebook containing the complete EDA of the IPL dataset. The notebook includes data cleaning, transformation, analysis, and visualizations.
• README.md: Description and instructions for the repository.

Key Features

• Data Cleaning and Preparation:

  • Handling missing values, duplicates, and data inconsistencies.
  • Transforming raw data into a structured format suitable for analysis.

• Detailed Analysis:

  • Match Outcomes: Analysis of win/loss patterns and trends over the years.
  • Team Performance: Evaluation of team performances across different seasons.
  • Player Insights: Investigation of top performers, emerging players, and individual milestones.
  • Venue Analysis: Study of the impact of different venues on match outcomes.

• Visualizations:

  • Interactive plots using libraries like Matplotlib, Seaborn.
  • Visualizations to explore match results, player statistics, and team performances.
  • Comparative analysis of teams and players through insightful visualizations.

Technologies Used

• Python: Programming language used for data analysis and visualization.
• Pandas: Library for data manipulation and analysis.
• Numpy: Library for numerical operations.
• Matplotlib: Library for static, animated, and interactive visualizations.
• Seaborn: Library for statistical data visualization.

Getting Started

1. Clone the Repository:

   git clone https://github.com/harshalyaravalkar/EDA_IPL_data.git