IPL Analysis and Visualization

A data analysis project on the Indian Premier League (IPL) focused on understanding match outcomes, team performances, player contributions, and venue-based insights using Python and interactive visualizations.

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📁 Project Structure

• ipl_anlaysis_visuals.ipynb – Jupyter notebook containing full analysis, cleaning steps, and visualizations.
• data/ – Folder to store input IPL datasets (e.g., matches.csv).
• README.md – Project overview and instructions.
• requirements.txt – List of dependencies (optional).

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🧠 Objectives

• Clean and preprocess IPL match data
• Analyze team and player performances
• Visualize trends across IPL seasons
• Examine win patterns and venue impact

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🔧 Tools & Technologies

• Python
• Jupyter Notebook
• Pandas, NumPy
• Matplotlib, Seaborn
• Plotly (for interactive visualizations)

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🧼 Data Cleaning

The dataset contained null values in key columns such as:

• city
• player_of_match
• winner
• result_margin
• target_runs
• target_overs
• method

These were handled using a combination of replacement, deletion, and type conversion techniques for consistency and usability. Also some corrupt datas were handled.

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📊 Key Analyses

• Most winning ipl team

  

• Toss decision per team trends

  

• Number of ipl trophy won by teams

  

• Top "Player of the Match" in ipl history

  

• Chasing vs Defending team history

  

• Wins per team and venue(heatmap)

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🌍 Heatmap: Team vs Venue Wins

A heatmap was created showing how many matches each team has won at every IPL venue. This helps identify home advantage and venue dominance.

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📌 How to Run

1. Clone this repository:

   git clone https://github.com/yourusername/ipl-analysis.git
   cd ipl-analysis
   

👤 Contributer

_{Ritesh Kr. Pandit
B.Tech Student · Developer · Analyst} (https://github.com/ritesh-begin)