Introduction:

This repository contains an Exploratory Data Analysis (EDA) project focused on the player statistics from the Indian Premier League (IPL). The IPL is a professional Twenty20 cricket league in India, featuring various franchises representing different cities. The dataset used in this analysis encompasses player statistics across multiple seasons of the IPL, including batting, bowling, and fielding metrics.

Objective:

The main objective of this EDA project is to gain insights into the performance trends of IPL players. By analyzing their statistics, we aim to identify patterns, correlations, and significant factors that contribute to the success of players in different aspects of the game.

Dataset:

The dataset comprises several CSV files, each representing a particular season of the IPL. These files contain a comprehensive set of attributes for each player, including but not limited to:

Batting: Runs scored, strike rate, average, number of boundaries, number of sixes, etc.

Bowling: Wickets taken, economy rate, average, strike rate, etc.

Fielding: Catches, run-outs, stumpings, etc.

Summary:

This EDA project is not exhaustive and serves as a starting point for exploring IPL player statistics. Further advanced analyses, predictive modeling, and domain-specific insights can be built upon this foundation.

• Kaggle - https://www.kaggle.com/datasets/ramjidoolla/ipl-data-set
• Questions to ask while doing EDA examples- https://www.google.com/
• Matplotlib multiple bar charthttps://pythonguides.com/matplotlib-multiple-bar-chart/
• seaborn plots - https://seaborn.pydata.org/tutorial/distributions.html

Resources:

Official IPL Website:

www.iplt20.com - The official website of the Indian Premier League provides up-to-date information on teams, players, schedules, and more.

Cricket API:

www.cricketapi.com - An API that provides cricket-related data, including player stats, match details, and historical data.

Python for Data Science Handbook:

jakevdp.github.io/PythonDataScienceHandbook - A comprehensive resource for data analysis using Python, including tutorials on Pandas, Matplotlib, and data visualization.

Seaborn Documentation:

seaborn.pydata.org - Documentation for the Seaborn library, which provides enhanced data visualization capabilities.

Stack Overflow:

stackoverflow.com - A community-driven platform where you can ask questions and find answers related to programming and data analysis.

• 👋 Hi, I’m Sriguhan
• 👀 I’m interested in Data Science
• 🌱 I’m currently learning python,SQL,PowerBI,Pyspark,scikit-learn
• 💞️ I’m looking to collaborate on Data professionals
• 📫 Reach me through linkedin - https://www.linkedin.com/in/sriguhan-c-v-272a8a146/