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Sepsis Survival Prediction/Model/README.md

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+# Sepsis Survival Prediction - Models
+
+## Models Implemented
+
+### 1. Random Forest
+
+**Description**: Random Forest is an ensemble learning method that constructs multiple decision trees during training and outputs the mode of the classes as the prediction.
+
+### 2. XGBoost
+
+**Description**: XGBoost is an optimized gradient boosting framework that is highly efficient and scalable, often used for structured data.
+
+### 3. Logistic Regression
+
+**Description**: Logistic Regression is a linear model that uses the logistic function to model the probability of a binary class.
+
+### 4. Gradient Boosting
+
+**Description**: Gradient Boosting builds models sequentially, with each new model correcting errors made by the previous ones.
+
+### 5. AdaBoost
+
+**Description**: AdaBoost is an ensemble learning method that combines multiple weak classifiers to create a strong classifier.
+
+### 6. CatBoost
+
+**Description**: CatBoost is a gradient boosting algorithm that handles categorical features automatically and efficiently.
+
+### 7. LightGBM
+
+**Description**: LightGBM is a gradient boosting framework designed for efficiency with large datasets and low memory usage.
+
+### 8. K-Nearest Neighbors (KNN)
+
+**Description**: K-Nearest Neighbors is a simple, instance-based learning algorithm where classification is based on the majority vote of the nearest neighbors.
+
+### 9. Support Vector Machine (SVM)
+
+**Description**: SVM is a supervised learning model that finds the hyperplane which best divides a dataset into classes.
+
+### 10. Decision Tree
+
+**Description**: Decision Trees are non-parametric supervised learning methods used for classification based on simple decision rules inferred from the data features.
+
+## Performance of the Models based on the Accuracy Scores
+
+- **Random Forest**: 90.92%
+- **XGBoost**: 90.92%
+- **Logistic Regression**: 90.92%
+- **Gradient Boosting**: 90.92%
+- **AdaBoost**: 90.92%
+- **CatBoost**: 90.93%
+- **LightGBM**: 90.92%
+- **K-Nearest Neighbors (KNN)**: 90.46%
+- **Support Vector Machine (SVM)**: 90.92%
+- **Decision Tree**: 90.92%
+
+Each model was evaluated based on its accuracy in predicting sepsis surval using clinical records.
+
+![accuracy plot](https://github.com/adi271001/ML-Crate/blob/sepsis-survival-prediction/Sepsis%20Survival%20Prediction/Images/__results___21_0.png?raw=true)
+![accuracy plot](https://github.com/adi271001/ML-Crate/blob/sepsis-survival-prediction/Sepsis%20Survival%20Prediction/Images/__results___20_1.png?raw=true)
+![accuracy plot](https://github.com/adi271001/ML-Crate/blob/sepsis-survival-prediction/Sepsis%20Survival%20Prediction/Images/__results___20_11.png?raw=true)
+![accuracy plot](https://github.com/adi271001/ML-Crate/blob/sepsis-survival-prediction/Sepsis%20Survival%20Prediction/Images/__results___20_13.png?raw=true)
+![accuracy plot](https://github.com/adi271001/ML-Crate/blob/sepsis-survival-prediction/Sepsis%20Survival%20Prediction/Images/__results___20_15.png?raw=true)
+![accuracy plot](https://github.com/adi271001/ML-Crate/blob/sepsis-survival-prediction/Sepsis%20Survival%20Prediction/Images/__results___20_17.png?raw=true)
+![accuracy plot](https://github.com/adi271001/ML-Crate/blob/sepsis-survival-prediction/Sepsis%20Survival%20Prediction/Images/__results___20_19.png?raw=true)
+![accuracy plot](https://github.com/adi271001/ML-Crate/blob/sepsis-survival-prediction/Sepsis%20Survival%20Prediction/Images/__results___20_3.png?raw=true)
+![accuracy plot](https://github.com/adi271001/ML-Crate/blob/sepsis-survival-prediction/Sepsis%20Survival%20Prediction/Images/__results___20_5.png?raw=true)
+![accuracy plot](https://github.com/adi271001/ML-Crate/blob/sepsis-survival-prediction/Sepsis%20Survival%20Prediction/Images/__results___20_7.png?raw=true)
+![accuracy plot](https://github.com/adi271001/ML-Crate/blob/sepsis-survival-prediction/Sepsis%20Survival%20Prediction/Images/__results___20_9.png?raw=true)