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Asthma Disease Detection/Models/README.md

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+# Asthma Disease Detection - Models
+
+## Models Implemented
+- Logistic Regression
+- Random Forest
+- Gradient Boosting
+- Support Vector Machine
+- XGBoost
+- K-Nearest Neighbors
+- AdaBoost
+- Extra Trees
+- Bagging
+- CatBoost
+- LightGBM
+- Naive Bayes
+- Decision Tree
+- Stacking Classifier
+
+## Performance of the Models based on Accuracy Scores
+- Logistic Regression: 95.20%
+- Random Forest: 95.20%
+- Gradient Boosting: 94.99%
+- Support Vector Machine: 95.20%
+- XGBoost: 95.20%
+- K-Nearest Neighbors: 95.20%
+- AdaBoost: 95.20%
+- Extra Trees: 95.20%
+- Bagging: 94.78%
+- CatBoost: 95.20%
+- LightGBM: 95.20%
+- Naive Bayes: 95.20%
+- Decision Tree: 87.47%
+- Stacking Classifier: 95.20%
+
+![EDA](https://github.com/adi271001/ML-Crate/blob/Asthma-Disease/Asthma%20Disease%20Detection/Images/__results___23_2.png?raw=true)
+![EDA](https://github.com/adi271001/ML-Crate/blob/Asthma-Disease/Asthma%20Disease%20Detection/Images/__results___23_4.png?raw=true)
+![EDA](https://github.com/adi271001/ML-Crate/blob/Asthma-Disease/Asthma%20Disease%20Detection/Images/__results___23_6.png?raw=true)
+![EDA](https://github.com/adi271001/ML-Crate/blob/Asthma-Disease/Asthma%20Disease%20Detection/Images/__results___23_8.png?raw=true)
+![EDA](https://github.com/adi271001/ML-Crate/blob/Asthma-Disease/Asthma%20Disease%20Detection/Images/__results___23_10.png?raw=true)
+![EDA](https://github.com/adi271001/ML-Crate/blob/Asthma-Disease/Asthma%20Disease%20Detection/Images/__results___23_12.png?raw=true)
+![EDA](https://github.com/adi271001/ML-Crate/blob/Asthma-Disease/Asthma%20Disease%20Detection/Images/__results___23_14.png?raw=true)
+![EDA](https://github.com/adi271001/ML-Crate/blob/Asthma-Disease/Asthma%20Disease%20Detection/Images/__results___23_16.png?raw=true)
+![EDA](https://github.com/adi271001/ML-Crate/blob/Asthma-Disease/Asthma%20Disease%20Detection/Images/__results___23_18.png?raw=true)
+![EDA](https://github.com/adi271001/ML-Crate/blob/Asthma-Disease/Asthma%20Disease%20Detection/Images/__results___23_20.png?raw=true)
+![EDA](https://github.com/adi271001/ML-Crate/blob/Asthma-Disease/Asthma%20Disease%20Detection/Images/__results___23_22.png?raw=true)
+![EDA](https://github.com/adi271001/ML-Crate/blob/Asthma-Disease/Asthma%20Disease%20Detection/Images/__results___23_24.png?raw=true)
+![EDA](https://github.com/adi271001/ML-Crate/blob/Asthma-Disease/Asthma%20Disease%20Detection/Images/__results___23_26.png?raw=true)
+![EDA](https://github.com/adi271001/ML-Crate/blob/Asthma-Disease/Asthma%20Disease%20Detection/Images/__results___23_28.png?raw=true)
+![EDA](https://github.com/adi271001/ML-Crate/blob/Asthma-Disease/Asthma%20Disease%20Detection/Images/__results___24_0.png?raw=true)
+
+## Conclusion
+The Logistic Regression, Random Forest, Support Vector Machine, XGBoost, K-Nearest Neighbors, AdaBoost, Extra Trees, CatBoost, LightGBM, Naive Bayes, and Stacking Classifier all achieved the highest accuracy of 95.20%. The Decision Tree model performed the worst with an accuracy of 87.47%. Ensemble methods and gradient boosting techniques tend to perform well on this dataset, indicating their robustness in handling complex patterns and interactions within the data.
+
+## Signature
+**Name:** 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)