IPL Analysis
Project Overview
The IPL Analysis project dives into rich historical data from the Indian Premier League to uncover patterns, trends, and winning strategies. Using Python and key data science libraries, we analyse player performances, team stats, match outcomes. The project is packed with interactive visualisations, providing cricket enthusiasts and analysts with deep insights into the dynamics of the world’s most popular T20 league.
Tools & Technologies Used
Methodology / Process
- Loading Datasets.
- Data Preprocessing.
- Visualizing Numerical Columns.
- Visualizing Categorical Columns.
- Checking For Outliers On Numerical Columns.
- Checking For Outliers On Numerical Columns.
- Removing outliers using IQR method for each numerical columns and plotting box plots without outliers.
- EDA : Run Distribution Per Over, Run Distribution of Batsman, Run Distribution By Team, Extras Types during matches, Most Common Type of Dismissals, Determine Top Batsmen Against Each Team, Batsman Performance Trends Across Matches, Bowlers Performance Trends Across Matches.
- Consistency Analysis Preview.
Loading Datasets
Data Cleaning and Preprocessing
Visualizing Numerical Columns
Visualizing Categorical Columns
Checking For Outliers On Numerical Columns
By seeing this I can say that my Numerical Columns has many outliers and for better results I have to treat the outliers.
Removing outliers using IQR method for each numerical columns and plotting box plots without outliers.
EDA : Run Distribution Per Over, Run Distribution of Batsman, Run Distribution By Team, Extras Types during matches, Most Common Type of Dismissals, Determine Top Batsmen Against Each Team, Batsman Performance Trends Across Matches, Bowlers Performance Trends Across Matches
Consistency Analysis Preview.
📦 Free Project Package
Download all files including project code, dataset, model file (.pkl) etc.
⬇️ Download Project Bundle (ZIP)
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