This repository contains Heart Attack Details to see Death Event using Random Forest Classifier, to help determine the optimum model to see dichotomus value [0,1] as main algorithm. The Python Script is Focused in main thing such : Data Understanding, Data Adjusment, Data insight via Visualization, Modelling, and Deployement Model.
The repository is organized as follows:
data: This directory contains the dataset used for analysis. The dataset includes information about Patient heart attack detail and Factors, like a age, anemia, diabetes, serum, etc.
scripts: This directory contains the Python scripts used for data Adjusment, Visualization, Modeling, and Evaluating.
notebooks: This directory contains Jupyter notebooks that provide step-by-step walkthroughs of the analysis process. These notebooks are named and organized by the specific analysis they cover.
The data visualization part of the analysis aims to provide insights into Employee Left Analyzing for Specific Criteria. Visualization techniques such as Kde Plot, bar charts, and scatter plot, are used to see age Distribution, display data points of platelets, serum, and another Numerical Distribution. To explore the data visualization analysis, refer to the notebooks in the "notebooks".
Using GridSearchCV to find the best parameters for Random Forest Classifier is a common approach to optimize the model's performance. This process will help me find the best hyperparameters for my Random Forest Classifier model and improve its performance for my specific dataset and problem.
By examining relevant variables such as Age, Smoking, Platelets, Creatinine, and other relevant factors, you can build a predictive model to estimate the likelihood of Death by Heart Attack.
To evaluate the performance of my Random Forest Classifier model using a recall, precision, AUC, and accuracy score. These metrics help us understand how well the model predicts Death by Heart Attack compared to actual values.