# IPL Match Winner Prediction using Orange Data Mining Tool
> Learn how to predict IPL match winners using machine learning, Orange Data Mining, and Logistic Regression with historical data from seasons 2020-2025.

Tags: ipl-prediction, machine-learning, data-mining, orange-tool, sports-analytics, python-project, logistic-regression, college-project
## IPL Match Winner Prediction Project Overview
* **Objective:** Predict IPL match winners using ML models and the Orange Data Mining tool based on 2020–2025 data.
* **Key Tools:** Orange Data Mining, Microsoft Excel, and Kaggle datasets.

## Machine Learning Models & Performance
* **Models Tested:** Decision Tree (41.9%), Random Forest (47.5%), and Logistic Regression (61.4%).
* **Best Performer:** Logistic Regression was selected for its 61.4% accuracy with categorical data.

## Data Preprocessing Workflow
* **Steps:** Data cleaning, removing ball-by-ball complexity, handling missing values via imputation, and applying One-Hot Encoding (Continuize).
* **Features used:** Team1, Team2, Toss Winner, Toss Decision, Stadium, City, and Season.

## Prediction and Evaluation
* **Methodology:** Pre-match details are entered into a CSV; the Orange 'Predictions' widget estimates the winner.
* **Evaluation Metrics:** Confusion Matrix, Accuracy, AUC, Precision, Recall, and F1 Score were used to validate results.

## Outcomes and Future Scope
* **Future Improvements:** Adding player performance, team strength scores, recent form, and injury availability analysis.
* **Conclusion:** Successfully demonstrated an end-to-end data mining pipeline for real-world sports analytics.
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