README for Sepsis Dataset Analysis
Introduction

This project focuses on analyzing a dataset for sepsis prediction and classification, emphasizing patient categorization based on sepsis conditions. The implementation includes data manipulation, visualization, and statistical analysis.
Dataset

    File Name: Dataset.csv
    Dimensions: 1,552,210 rows × 44 columns
    Description: The dataset contains patient medical records, including physiological measurements and sepsis labels. Key columns include:
        Patient_ID: Unique identifier for each patient.
        SepsisLabel: Indicator of sepsis presence (1 = sepsis, 0 = no sepsis).
        Temp, HR, Resp, WBC: Measurements for calculating SIRS (Systemic Inflammatory Response Syndrome) scores.
        Hour: Time since admission.

Key Objectives

    Categorize patients based on sepsis:
        SepsisBeforeAdm: Patients admitted to ICU with sepsis.
        SepsisAfterAdm: Patients who developed sepsis post-admission.
        NonSepsis: Patients with no recorded sepsis.
    Analyze SIRS scores for patient conditions.
    Visualize patterns in sepsis occurrence by gender and other variables.
    Explore correlations among key medical parameters.

Implementation Overview

The implementation is organized into the following steps:

    Data Preprocessing
        Load dataset using Pandas.
        Check for missing values and visualize null-value distribution.
        Configure display settings to show all rows and columns.

    Sepsis Patient Categorization
        Identify and categorize patients based on sepsis conditions.
        Create new columns (sepsisType and hasSIRS) to reflect patient conditions.

    SIRS Score Calculation
        Compute SIRS scores based on physiological thresholds:
            Temp: >38°C or <36°C.
            HR: >90 bpm.
            Resp: >20 breaths/min or PaCO2 < 32 mmHg.
            WBC: >12,000 or <4,000 cells/µL.
        Summarize and visualize SIRS scores.

    Visualizations
        Distribution of sepsis labels.
        Gender-wise comparison of sepsis prevalence.
        Correlation matrix of key physiological metrics.

    Statistical Analysis
        Examine statistical summaries and relationships among critical variables.

Dependencies

    Python Libraries:
        pandas: Data manipulation.
        numpy: Numerical computations.
        seaborn & matplotlib: Visualization.
        hvplot: Interactive plotting.
        scipy: Statistical functions.

Usage

    Prerequisites: Install required libraries:

    pip install pandas numpy seaborn matplotlib hvplot scipy

    Run the Code:
        Ensure the dataset is located at the specified path (../input/prediction-of-sepsis/Dataset.csv).
        Execute the Python script.

    Outputs:
        Summary statistics and visualizations of sepsis distribution.
        SIRS score breakdown and correlation heatmaps.
        Gender-based analysis of sepsis prevalence.

Results

    Key Insights:
        Number of patients with sepsis and their categorization.
        SIRS score distribution across the dataset.
        Gender disparities in sepsis prevalence.
        Correlations among physiological parameters (Alkalinephos, BaseExcess, Lactate).