🏏 IPL Data Analysis & Visualization

A Python project performing exploratory data analysis (EDA) and visualization on Indian Premier League (IPL) match data using Pandas, Seaborn, and Matplotlib.

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📁 Repository Structure

ipl_data_analysis/
├── Rahul_Biswas_ipl_224.py   # Main analysis script
├── ipl.csv                   # Dataset
└── README.md

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📊 Dataset

ipl.csv — Match-level IPL data including teams, toss results, win margins (by runs and wickets), umpires, and match outcomes.

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🔧 Requirements

• Python 3.x
• pandas
• seaborn
• matplotlib

Install dependencies:

pip install pandas seaborn matplotlib

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🚀 Usage

python Rahul_Biswas_ipl_224.py

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📝 Features

1. Data Analysis

Step	Description
Full Data Print	Displays entire DataFrame
Shape	Rows and columns count using df.shape
Columns	Lists column names and their values
Describe	Summary statistics via df.describe()
Head / Tail	First and last 10 rows
Min / Max	Min and max of win_by_runs column
Filtering	Matches where win_by_runs > 30
Slicing	Rows 2 to 5 using index slicing

2. Data Cleaning

• Null Value Detection — df.isnull() to identify missing entries across all columns
• Column-Level Null Check — Specifically checks win_by_runs and umpire3 columns for missing values

3. Data Visualization

Matplotlib:

Plot	Description
Line Plot	win_by_runs vs win_by_wickets
Scatter Plot	win_by_runs vs win_by_wickets
Box Plot	Distribution of win_by_runs

Seaborn:

Plot	Description
Bar Plot	Result vs Toss Winner, colored by win_by_runs
Joint Plot	Relationship between result and toss winner
Pair Plot	Pairwise relationships, colored by match result
Box Plot	Result vs Toss Winner distribution
Scatter Plot	Result vs Toss Winner, hued by win_by_runs
Histogram	Frequency distribution of match results

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👤 Author

Rahul Biswas GitHub: @RahulBiswas224

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📄 License

This project is open source and available for educational purposes.