Merge pull request #805 from Shravanikale/main

Prediction on Patient Length of Stay in Hospitals

Predicting-Patients-Length-of-Stay-in-Hospital/Dataset/train_data_dictionary.csv

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+Column,Description
+case_id,Case_ID registered in Hospital
+Hospital_code,Unique code for the Hospital
+Hospital_type_code,Unique code for the type of Hospital
+City_Code_Hospital,City Code of the Hospital
+Hospital_region_code,Region Code of the Hospital
+Available Extra Rooms in Hospital,Number of Extra rooms available in the Hospital
+Department,Department overlooking the case
+Ward_Type,Code for the Ward type
+Ward_Facility_Code,Code for the Ward Facility
+Bed Grade,Condition of Bed in the Ward
+patientid,Unique Patient Id
+City_Code_Patient,City Code for the patient
+Type of Admission,Admission Type registered by the Hospital
+Severity of Illness,Severity of the illness recorded at the time of admission
+Visitors with Patient,Number of Visitors with the patient
+Age,Age of the patient
+Admission_Deposit,Deposit at the Admission Time
+Stay,Stay Days by the patient

Predicting-Patients-Length-of-Stay-in-Hospital/README.md

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+
+# Predicting The Patients Length of Stay in Hospital
+![download](https://encrypted-tbn0.gstatic.com/images?q=tbn:ANd9GcRu80ZK2mbHQnb5NMg_TUYxpPLS9braF6ewIw&s)
+
+
+## Goal
+The goal is to develop a model that accurately predicts the duration of a patient's hospital stay to optimize resource allocation and improve patient care.
+
+## Dataset
+I have Downloaded this dataset from kaggle website. Here is the link: https://www.kaggle.com/datasets/nehaprabhavalkar/av-healthcare-analytics-ii
+## What Have I Done?
+
+- Imported all the required libraries and dataset for this project.
+- Exploratory Data Analysis and Visualizing different aspects of the dataset.
+- Finding number of observations and outliers in the dataset.
+- Building the machine learning and data model.
+- Prepared data for modeling by imputing missing values and scaling features for analysis.
+
+
+
+## Library used:
+1. numpy
+2. pandas
+3. plotly 
+4. matplotlib
+5. seaborn
+
+## Visuals:
+
+
+<img src = "https://github.com/Shravanikale/PatientLength-of-Stay-in-Hospital/blob/main/HS1.png"/>
+<img src = "https://github.com/Shravanikale/PatientLength-of-Stay-in-Hospital/blob/main/HS2.png"/>
+<img src = "https://github.com/Shravanikale/PatientLength-of-Stay-in-Hospital/blob/main/HS3.png"/>
+<img src = "https://github.com/Shravanikale/PatientLength-of-Stay-in-Hospital/blob/main/HS4.png"/>
+<img src = "https://github.com/Shravanikale/PatientLength-of-Stay-in-Hospital/blob/main/HS5.png"/>
+
+
+
+## Conclusion:
+The project "Predicting the Patients Length of Stay in Hospital" successfully developed a predictive model to estimate hospital stay durations. Through comprehensive exploratory data analysis and visualization, important patterns and correlations were identified. Various machine learning algorithms were evaluated, and the final model demonstrated strong predictive capabilities with high accuracy. The results were compiled into a detailed report and submitted to stakeholders, providing actionable insights for optimizing hospital resources and enhancing patient care. Future work will focus on improving data quality, experimenting with advanced modeling techniques, and integrating the model into hospital management systems for real-time decision support.
+## Authors
+
+- Created by [@Shravanikale](https://github.com/Shravanikale), GSSoC 2024