Create README.md

Bank Credit Analysis/Model/README.md

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+## 🚀 Models Implemented
+- **Random Forest**: Chosen for its robustness and ability to handle large datasets with higher accuracy.
+- **XGBoost**: Known for its performance and speed, making it suitable for complex datasets.
+- **Decision Tree**: Simple to interpret and visualize, though prone to overfitting.
+- **AdaBoost**: Effective in boosting the performance of weak classifiers.
+- **CatBoost**: Handles categorical features well and provides high accuracy.
+- **Logistic Regression**: Baseline model for classification tasks.
+- **Extra Trees**: Similar to Random Forest but with some differences in the splitting of nodes.
+- **Gaussian Naive Bayes**: Simple and effective, especially for smaller datasets.
+- **K-Nearest Neighbors**: Simple and easy to implement, but can be computationally expensive.
+- **Support Vector Machine**: Effective in high-dimensional spaces and suitable for classification tasks.
+
+## 📈 Performance of the Models based on the Accuracy Scores
+| Model                   | Train Accuracy | CV Mean Accuracy | Test Accuracy |
+|-------------------------|----------------|------------------|---------------|
+| K Nearest Neighbors     | 81.81%         | 75.38%           | 75.19%        |
+| Support Vector Machine  | 83.37%         | 82.92%           | 81.59%        |
+| Random Forest           | 99.40%         | 85.79%           | 83.70%        |
+| XGBoost                 | 100.00%        | 85.47%           | 84.42%        |
+| Decision Tree           | 87.51%         | 81.92%           | 80.25%        |
+| AdaBoost                | 84.04%         | 82.91%           | 82.58%        |
+| CatBoost                | 90.36%         | 86.58%           | 85.89%        |
+| Logistic Regression     | 82.55%         | 82.10%           | 81.68%        |
+| Extra Trees             | 98.76%         | 83.38%           | 82.22%        |
+| Gaussian Naive Bayes    | 73.92%         | 73.58%           | 74.56%        |
+
+## ✒️ Your Signature
+Aditya D
+
+GitHub: [https://www.github.com/adi271001](https://www.github.com/adi271001)  
+LinkedIn: [https://www.linkedin.com/in/aditya-d-23453a179/](https://www.linkedin.com/in/aditya-d-23453a179/)  
+Topmate: [https://topmate.io/aditya_d/](https://topmate.io/aditya_d/)  
+Twitter: [https://x.com/ADITYAD29257528](https://x.com/ADITYAD29257528)