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Eye Care Data Analysis Project is a Python-based final year project developed for analyzing ophthalmology and ocular disease data using the ODIR-5K Ocular Disease Recognition dataset. This major project performs exploratory data analysis, patient demographic analysis, disease distribution analysis, gender-wise disease comparison, age-wise disease comparison, disease co-occurrence analysis, and machine learning based primary disease classification. The project supports Kaggle dataset download, manual Excel file loading, and sample data generation when the real dataset is not available. It compares machine learning models such as Random Forest, Gradient Boosting, and Logistic Regression using accuracy, F1-score, precision, and recall. This Eye Care Data Analysis source code is suitable for students who need a final year project, major project, minor project, source code, and project report based on Python, healthcare analytics, ophthalmology data analysis, and machine learning
This project has no login credentials because it is not a web application.
Credential note:
No admin panel, user login, password, database authentication, or dashboard login module is included. The project runs locally as a Python data analysis script or Jupyter Notebook.
The project analyzes the following ocular disease categories:
NDGCAHMOLoads Excel dataset:
full_data.xlsx
or:
data.xlsx
Supports manual file path configuration:
EXCEL_FILE_PATH = r"C:\path\to\full_data.xlsx"
The project generates visual outputs inside:
outputs/
1_disease_distribution_bar.png2_gender_pie.png3_age_by_disease_box.png4_disease_correlation_heatmap.png5_age_histogram.png6_disease_by_gender_grouped.png7_confusion_matrix.pngDataset name:
ODIR-5K Ocular Disease Recognition
Dataset source:
Kaggle
Dataset format:
Excel file
Supported Excel file names:
full_data.xlsx
data.xlsx
Dataset domain:
Eye care / ophthalmology / ocular disease recognition
Disease labels:
N, D, G, C, A, H, M, O
Dataset can be loaded by:
eye_care_analysis.py — main Python scripteye_care_analysis.ipynb — Jupyter Notebook versionrequirements.txt — Python dependency listREADME.md — project documentationoutputs/ — generated plot folderProject structure:
eye-care-jupiter-ml/
├── eye_care_analysis.py
├── eye_care_analysis.ipynb
├── requirements.txt
├── README.md
└── outputs/
The project generates outputs inside:
outputs/
Expected output files:
1_disease_distribution_bar.png2_gender_pie.png3_age_by_disease_box.png4_disease_correlation_heatmap.png5_age_histogram.png6_disease_by_gender_grouped.png7_confusion_matrix.pngConsole / notebook output includes:
Open terminal in the project folder:
cd "eye-care-jupiter-ml"
Install dependencies:
pip install -r requirements.txt
Place kaggle.json in:
~/.kaggle/
On Windows:
C:\Users\<username>\.kaggle\
full_data.xlsx from KaggleSet Excel path in eye_care_analysis.py:
EXCEL_FILE_PATH = r"C:\path\to\full_data.xlsx"
Run the project:
python eye_care_analysis.py
Check generated outputs:
outputs/
Install dependencies:
pip install -r requirements.txt
Start notebook:
jupyter notebook eye_care_analysis.ipynb
Or run:
python -m notebook eye_care_analysis.ipynb
Check generated plots:
outputs/
This project has no login credentials because it is not a web application.
Credential note:
No admin panel, user login, password, database authentication, or dashboard login module is included. The project runs locally as a Python data analysis script or Jupyter Notebook.