abhisheks008 · abhisheks008 · Jul 28, 2024 · Jul 27, 2024 · Jul 27, 2024 · Jul 27, 2024
diff --git a/Asthma Disease Detection/Dataset/asthma_disease_data.csv b/Asthma Disease Detection/Dataset/asthma_disease_data.csv
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diff --git a/Asthma Disease Detection/Models/README.md b/Asthma Disease Detection/Models/README.md
@@ -0,0 +1,59 @@
+# 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)
diff --git a/Asthma Disease Detection/Models/asthma-disease-detection.ipynb b/Asthma Disease Detection/Models/asthma-disease-detection.ipynb
diff --git a/Asthma Disease Detection/README.md b/Asthma Disease Detection/README.md
@@ -0,0 +1,91 @@
+# Asthma Disease Detection
+
+## Goal
+The goal of this project is to build a machine learning model to accurately detect asthma disease using various classification algorithms.
+
+## Dataset
+The dataset used in this project is sourced from [Asthma Disease Dataset on Kaggle](https://www.kaggle.com/datasets/rabieelkharoua/asthma-disease-dataset). It contains data relevant to asthma disease detection.
+
+## Description
+This project involves training and evaluating multiple machine learning models to detect asthma disease. The models' performance is compared using metrics such as accuracy and ROC AUC score. Confusion matrices are also plotted for each model to visualize the performance.
+
+## What I Had Done
+1. Loaded the asthma disease dataset from Kaggle.
+2. Split the dataset into training and testing sets.
+3. Implemented multiple machine learning models.
+4. Trained and evaluated each model.
+5. Plotted confusion matrices for each model.
+6. Saved the results (accuracy and ROC AUC) to a CSV file.
+7. Generated a comparison plot of the models' performance.
+
+## 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
+
+## Libraries Needed
+- pandas
+- matplotlib
+- seaborn
+- scikit-learn
+- xgboost
+- catboost
+- lightgbm
+- mlxtend
+
+## EDA Results
+Exploratory Data Analysis (EDA) revealed the following key points:
+- Feature distributions and correlations
+- Class imbalance in the dataset
+- Identification of important features for asthma disease detection
+
+![eda](https://github.com/adi271001/ML-Crate/blob/Asthma-Disease/Asthma%20Disease%20Detection/Images/__results___5_1.png?raw=true)
+![eda](https://github.com/adi271001/ML-Crate/blob/Asthma-Disease/Asthma%20Disease%20Detection/Images/__results___6_0.png?raw=true)
+![eda](https://github.com/adi271001/ML-Crate/blob/Asthma-Disease/Asthma%20Disease%20Detection/Images/__results___8_2.png?raw=true)
+![eda](https://github.com/adi271001/ML-Crate/blob/Asthma-Disease/Asthma%20Disease%20Detection/Images/__results___9_1.png?raw=true)
+![eda](https://github.com/adi271001/ML-Crate/blob/Asthma-Disease/Asthma%20Disease%20Detection/Images/__results___10_1.png?raw=true)
+![eda](https://github.com/adi271001/ML-Crate/blob/Asthma-Disease/Asthma%20Disease%20Detection/Images/__results___11_1.png?raw=true)
+![eda](https://github.com/adi271001/ML-Crate/blob/Asthma-Disease/Asthma%20Disease%20Detection/Images/__results___12_1.png?raw=true)
+![eda](https://github.com/adi271001/ML-Crate/blob/Asthma-Disease/Asthma%20Disease%20Detection/Images/__results___13_1.png?raw=true)
+![eda](https://github.com/adi271001/ML-Crate/blob/Asthma-Disease/Asthma%20Disease%20Detection/Images/__results___14_1.png?raw=true)
+![eda](https://github.com/adi271001/ML-Crate/blob/Asthma-Disease/Asthma%20Disease%20Detection/Images/__results___15_1.png?raw=true)
+![eda](https://github.com/adi271001/ML-Crate/blob/Asthma-Disease/Asthma%20Disease%20Detection/Images/__results___16_2.png?raw=true)
+![eda](https://github.com/adi271001/ML-Crate/blob/Asthma-Disease/Asthma%20Disease%20Detection/Images/__results___17_1.png?raw=true)
+![eda](https://github.com/adi271001/ML-Crate/blob/Asthma-Disease/Asthma%20Disease%20Detection/Images/__results___18_0.png?raw=true)
+
+## 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%
+
+## 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%. These results indicate that ensemble methods and advanced gradient boosting techniques tend to perform well on this dataset.
+
+## 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)
diff --git a/Asthma Disease Detection/Results/confusion_matrix_AdaBoost.png b/Asthma Disease Detection/Results/confusion_matrix_AdaBoost.png
diff --git a/Asthma Disease Detection/Results/confusion_matrix_Bagging.png b/Asthma Disease Detection/Results/confusion_matrix_Bagging.png
diff --git a/Asthma Disease Detection/Results/confusion_matrix_CatBoost.png b/Asthma Disease Detection/Results/confusion_matrix_CatBoost.png
diff --git a/Asthma Disease Detection/Results/confusion_matrix_Decision Tree.png b/Asthma Disease Detection/Results/confusion_matrix_Decision Tree.png
diff --git a/Asthma Disease Detection/Results/confusion_matrix_Extra Trees.png b/Asthma Disease Detection/Results/confusion_matrix_Extra Trees.png
diff --git a/Asthma Disease Detection/Results/confusion_matrix_Gradient Boosting.png b/Asthma Disease Detection/Results/confusion_matrix_Gradient Boosting.png
diff --git a/Asthma Disease Detection/Results/confusion_matrix_K-Nearest Neighbors.png b/Asthma Disease Detection/Results/confusion_matrix_K-Nearest Neighbors.png
diff --git a/Asthma Disease Detection/Results/confusion_matrix_LightGBM.png b/Asthma Disease Detection/Results/confusion_matrix_LightGBM.png
diff --git a/Asthma Disease Detection/Results/confusion_matrix_Logistic Regression.png b/Asthma Disease Detection/Results/confusion_matrix_Logistic Regression.png
diff --git a/Asthma Disease Detection/Results/confusion_matrix_Naive Bayes.png b/Asthma Disease Detection/Results/confusion_matrix_Naive Bayes.png
diff --git a/Asthma Disease Detection/Results/confusion_matrix_Random Forest.png b/Asthma Disease Detection/Results/confusion_matrix_Random Forest.png
diff --git a/Asthma Disease Detection/Results/confusion_matrix_Stacking Classifier.png b/Asthma Disease Detection/Results/confusion_matrix_Stacking Classifier.png
diff --git a/Asthma Disease Detection/Results/confusion_matrix_Support Vector Machine.png b/Asthma Disease Detection/Results/confusion_matrix_Support Vector Machine.png
diff --git a/Asthma Disease Detection/Results/confusion_matrix_XGBoost.png b/Asthma Disease Detection/Results/confusion_matrix_XGBoost.png
diff --git a/Asthma Disease Detection/Results/model_comparison.png b/Asthma Disease Detection/Results/model_comparison.png
diff --git a/Asthma Disease Detection/Results/model_results.csv b/Asthma Disease Detection/Results/model_results.csv
@@ -0,0 +1,15 @@
+Model,Accuracy,ROC AUC
+Logistic Regression,0.9519832985386222,0.5225972540045767
+Random Forest,0.9519832985386222,0.3812452326468344
+Gradient Boosting,0.9498956158663883,0.4405034324942792
+Support Vector Machine,0.9519832985386222,0.5087719298245613
+XGBoost,0.9519832985386222,0.4032227307398932
+K-Nearest Neighbors,0.9519832985386222,0.5416666666666666
+AdaBoost,0.9519832985386222,0.4804538520213577
+Extra Trees,0.9519832985386222,0.4488939740655987
+Bagging,0.9478079331941545,0.4977116704805492
+CatBoost,0.9519832985386222,0.4680587337909992
+LightGBM,0.9519832985386222,0.4597635392829901
+Naive Bayes,0.9519832985386222,0.5242181540808543
+Decision Tree,0.8747390396659708,0.5007151029748284
+Stacking Classifier,0.9519832985386222,0.49890350877192985
diff --git a/Asthma Disease Detection/requirements.txt b/Asthma Disease Detection/requirements.txt
@@ -0,0 +1,9 @@
+pandas==2.0.3
+numpy==1.25.0
+matplotlib==3.7.2
+seaborn==0.12.2
+scikit-learn==1.3.0
+xgboost==1.7.6
+catboost==1.2
+lightgbm==4.0.0
+mlxtend==0.21.0