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README.md

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+# Gladiator Winning Model
+
+This repository contains a trained **Gradient Boosting classifier** used to predict gladiator fight outcomes.
+
+## Dataset
+The full dataset is available on Kaggle:  
+https://www.kaggle.com/datasets/anthonytherrien/gladiator-combat-records-and-profiles-dataset
+
+A representative sample may be included in this repository for demonstration purposes.
+
+## Model Performance
+- **F1-score:** 0.910  
+- **Accuracy:** ~90%  
+- **ROC-AUC:** 0.970  
+The Gradient Boosting model performed the best out of all tested classifiers.
+
+## Model Comparison
+
+Several models were tested, including Logistic Regression, Random Forest, and Gradient Boosting. 
+In both the regression-style and classification tasks, **Gradient Boosting consistently performed the best**, 
+showing higher accuracy and a better balance between precision and recall. 
+This makes it the most reliable model for predicting gladiator outcomes.
+
+
+## Usage
+```python
+import pickle
+
+with open("gladiator_gradient_boosting_classifier.pkl", "rb") as f:
+    model = pickle.load(f)
+]
+
+
+
+## Exploratory Data Analysis (EDA)
+Before modeling, the dataset was explored to understand the distribution of gladiator attributes and fighting outcomes.
+Outliers were handled carefully to ensure they did not distort model training.
+
+### Key Findings
+- The dataset contains **detailed gladiator profiles**, including age, height, weight, fighting style, armor type, victory count, and more.
+- Many features show **clear relationships with the final battle outcome** (Win/Loss), as the numerical features: Battle Experience, Public Favor.
+- Categorical features such as **Gladiator Type, Weapon Type, Fighting Style, Crowd Appeal Techniques and Previous Occupation** showed meaningful differences between winners and non-winners.
+- Correlation analysis indicated that **experience-based features** (e.g., previous wins) have stronger predictive power than purely physical attributes.
+
+Strong correlation between Wins and Public Favor & Battle Experience:
+![heat-map](https://cdn-uploads.huggingface.co/production/uploads/691478b16066c85edf046dd8/QM8tepRS9JD56F8kNCWDM.png)
+
+Categorical features with strong correlation with the target variable Wins:
+![Screenshot 2025-12-11 231821](https://cdn-uploads.huggingface.co/production/uploads/691478b16066c85edf046dd8/kSEAkE7ad8l5AqnEThm8H.png)
+
+### Data Cleaning Steps
+- Missing values were imputed or removed depending on relevance.
+- Boolean and categorical features were encoded into numeric form.
+- New engineered features such as **BMI**, **interaction terms**, and **readiness scores** were added to improve predictive performance.
+- The target variable was converted into a binary class using a median split (`Win` vs. `Not Win`).
+
+### Feature Insights
+During EDA, battle experience emerged as the strongest predictor, showing a very high correlation (~0.95) with the target outcome.
+However, because this feature was essentially a direct indicator of the final result, it created data leakage.
+To ensure a fair and realistic model, this feature was removed from training, which made the remaining predictors more meaningful and prevented overly optimistic performance.
+
+This EDA process provided important insights that shaped feature engineering and model selection.