This project presents a comprehensive data analysis of the 2021 Indian Premier League (IPL) season using Python. The analysis pipeline covers raw data preprocessing, cleaning, and insightful visualizations of players and team performance.
- Data cleaning and transformation from ball-by-ball format
- Exploratory Data Analysis (EDA) using pandas, matplotlib, and seaborn
- Insights on match outcomes, player stats, and team trends
- Visual storytelling of IPL 2021 through Python notebooks
| Notebook | Description |
|---|---|
01_file_rename.ipynb |
Renames and standardizes raw files |
02_data_preparation.ipynb |
Loads data, merges relevant columns |
03_data_cleaning.ipynb |
Cleans missing/inconsistent entries |
04_data_analysis.ipynb |
Performs visual and statistical EDA |
IPL-2021-Analysis/
├── dataset/
├── csv/
├── raw/
├── 01_file_rename.ipynb
├── 02_data_preparation.ipynb
├── 03_data_cleaning.ipynb
├── 04_data_analysis.ipynb
Example plots include:
- Toss impact on winning
- Top 10 run scorers and wicket takers
- Team-wise performance summary
- Over-wise run distribution
(You can add screenshots of visualizations here)
- Python 3.x
- pandas
- numpy
- matplotlib
- seaborn
- jupyter
- Clone the repository:
git clone https://github.com/arabind-meher/IPL-2021-Analysis.git cd IPL-2021-Analysis - Launch Jupyter Notebook and explore the analysis step-by-step.