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https://github.com/mktechai-0786/data-analysis-on-dr-visits

Data Analysis On Dr. Visits dataset
https://github.com/mktechai-0786/data-analysis-on-dr-visits

data-analysis matplotlib-pyplot numpy pandas seaborn

Last synced: 16 days ago
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Data Analysis On Dr. Visits dataset

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README 

        
#Doctors Visit Analysis

  The Doctors Visit Analysis project is a data analytics initiative aimed at understanding patterns in healthcare visits, illness frequency, and insurance coverage. The project focuses on analyzing patient demographics, health outcomes, and socioeconomic factors that influence healthcare utilization.



#Key objectives include:

  Counting the total number of people based on illness frequency.

  Analyzing gender-wise illness trends and activity reduction due to illness.

  Visualizing missing data using heatmaps.

  Studying correlations between dataset variables.

  Investigating the impact of income on hospital visits.

  Analyzing insurance coverage trends (government vs. private).

  Providing actionable insights for healthcare providers, policymakers, researchers, and insurers.



#Technology Stack

  Programming Language: Python 3.11.1



#Development Environments:

  Jupyter Notebook / Google Colab

  Visual Studio Code



#Libraries:

  Pandas (2.2.2): Data cleaning and preprocessing.

  NumPy (2.0.2): Numerical operations.

  Matplotlib (3.9.2): Data visualization.

  Seaborn (0.13.2): Enhanced data visualizations.



#Installation Steps

  1. Install Python 3.11.1

     Download and install Python from the official Python website. (https://www.python.org/)

  

  2. Install Required Libraries

    Use pip to install the necessary Python libraries:

      pip install pandas==2.2.2

      pip install numpy==2.0.2

      pip install matplotlib==3.9.2

      pip install seaborn==0.13.2

     

  3. Set Up IDE

    Install and configure your preferred IDE:

      Jupyter Notebook: Use pip install notebook to set up.

      Google Colab: Access it directly via your browser.

      Visual Studio Code: Download from Visual Studio Code website.

  

  4. Run the Project

    Open the project in your IDE, load the dataset, and execute the analysis scripts.



#Results

Correlation Between Variables:



    The analysis identified strong correlations between variables, helping to pinpoint key factors influencing healthcare visits, such as income, health insurance coverage, and gender.

    Gender-Based Illness Analysis:

    

    Visualized the frequency of illness among males and females, revealing patterns such as higher illness rates in specific demographics.

    Activity Reduction Due to Illness:

    

    Generated bar charts showing the number of days of reduced activity for males and females separately due to illness.

    Handling Missing Data:

    

    Utilized heatmaps to detect and manage missing data effectively, ensuring the reliability of results.

    Income's Impact on Hospital Visits:

    

    Found that individuals with lower income levels had higher hospital visit frequencies. This suggests a direct relationship between socioeconomic factors and healthcare utilization.

    Health Insurance Coverage:

    

    Government Health Insurance: A higher percentage of low-income or elderly individuals benefited from government health insurance.

    Private Health Insurance: Calculated and visualized the proportion of people covered by private insurance compared to government insurance.

    Relation Between Health Status and Activity Reduction:

    

    Heatmaps revealed clear patterns linking poor health status to extended activity reduction days.

    Illness Count by Demographics:

    

    The dataset was analyzed to count illness occurrences across different age groups, genders, and income brackets.

    Duplicate Entry Detection:

    

    Identified and removed duplicate data entries to improve data quality.

    Visualization of Key Trends:

    

    Created detailed visualizations (scatter plots, bar charts, and heatmaps) to simplify complex data and highlight meaningful patterns.



#Acknowledgments

  This project is designed to improve healthcare analytics and assist stakeholders in making informed decisions for better healthcare outcomes.