+
+## ๐ฏ Goal
+
+The goal of this project is to predict the survival of patients with sepsis using machine learning models based on clinical records.
+
+## ๐งต Dataset
+
+The dataset used for this project is sourced from [Sepsis Survival Minimal Clinical Records on Kaggle](https://www.kaggle.com/datasets/joebeachcapital/sepsis-survival-minimal-clinical-records).
+
+## ๐งพ Description
+
+This project involves the analysis of clinical records to predict the survival of patients with sepsis. It includes data preprocessing, model training, evaluation, and comparison of multiple algorithms to determine the most accurate model.
+
+## ๐งฎ What I had done!
+
+1. **Data Collection**: Acquired the clinical records dataset for sepsis survival prediction.
+2. **Data Preprocessing**: Cleaned and prepared the data for analysis.
+3. **Exploratory Data Analysis (EDA)**: Visualized the data to understand the distribution and relationships between variables.
+4. **Model Training**: Implemented various machine learning models.
+5. **Model Evaluation**: Evaluated the performance of each model using accuracy scores.
+6. **Model Comparison**: Compared the models to identify the best performer.
+7. **Visualization**: Plotted confusion matrices for model predictions.
+
+## ๐ Models Implemented
+
+1. **Random Forest**: Selected for its robustness and ability to handle complex datasets.
+2. **XGBoost**: Chosen for its high performance and scalability.
+3. **Logistic Regression**: A baseline model for comparison.
+4. **Gradient Boosting**: Implemented for its accuracy in classification tasks.
+5. **AdaBoost**: Chosen for its ability to improve the performance of weak classifiers.
+6. **CatBoost**: Selected for its efficiency with categorical data (Accuracy: 90.93%).
+7. **LightGBM**: Chosen for its fast training speed and low memory usage.
+8. **K-Nearest Neighbors (KNN)**: Implemented for its simplicity and effectiveness (Accuracy: 90.46%).
+9. **Support Vector Machine (SVM)**: Utilized for its ability to handle complex relationships in data.
+10. **Decision Tree**: Implemented for its interpretability and ability to handle both numerical and categorical data.
+
+## ๐ Libraries Needed
+
+- pandas
+- numpy
+- matplotlib
+- seaborn
+- scikit-learn
+- xgboost
+- catboost
+- lightgbm
+
+## ๐ Exploratory Data Analysis Results
+
+
+
+
+
+
+
+
+
+
+## ๐ 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%
+
+## ๐ข Conclusion
+
+The project successfully implemented and evaluated various machine learning models for predicting sepsis survival using clinical records. CatBoost emerged as the best performer with an accuracy of 90.93%, closely followed by other models at 90.92% accuracy. K-Nearest Neighbors (KNN) also performed well with an accuracy of 90.46%.
+
+โ๏ธ Your Signature
+Aditya D
+* Github: https://wwww.github.com/adi271001
+* LInkedin: https://www.linkedin.com/in/aditya-d-23453a179/
+* Topmate: https://topmate.io/aditya_d/
+* Twitter: https://x.com/ADITYAD29257528
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diff --git a/Sepsis Survival Prediction/Results/model_accuracy_results.csv b/Sepsis Survival Prediction/Results/model_accuracy_results.csv
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+++ b/Sepsis Survival Prediction/Results/model_accuracy_results.csv
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+Model,Accuracy
+Logistic Regression,0.90923704711265
+Random Forest,0.909329788549524
+Gradient Boosting,0.90923704711265
+AdaBoost,0.90923704711265
+Decision Tree,0.9092679609249412
+k-NN,0.9045999752689502
+XGBoost,0.9092061333003586
+CatBoost,0.9092679609249412
+LightGBM,0.90923704711265
+Extra Trees,0.9092679609249412
diff --git a/Sepsis Survival Prediction/requirements.txt b/Sepsis Survival Prediction/requirements.txt
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+++ b/Sepsis Survival Prediction/requirements.txt
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+numpy==1.21.2
+pandas==1.3.3
+seaborn==0.11.2
+matplotlib==3.4.3
+scikit-learn==0.24.2
+xgboost==1.4.2
+catboost==0.26.1
+lightgbm==3.2.1