sinanbertan/biomechanical-features-of-orthopedic-patients

Biomechanical-features-of-orthopedic-patients

• This repository contains the code and data for a project on Orthopedic Patients Classification. The project uses the column_2C_weka.csv dataset, which can be downloaded from Kaggle:https://www.kaggle.com/datasets/uciml/biomechanical-features-of-orthopedic-patients

Data Content

Column_2C_weka.csv (file with two class labels)

• the categories Disk Hernia and Spondylolisthesis were merged into a single category labelled as 'abnormal'. Thus, the second task consists in classifying patients as belonging to one out of two categories: Normal (100 patients) or Abnormal (210 patients).

Installation

The following tools were used for this analysis:

• Python 3

• Pandas

• NumPy

• Matplotlib

• Seaborn

• Plotly

• Sklearn

• xgboost

• lightgbm

• catboost

• To run this project, you will need to have Python 3 installed on your machine. You can install the required libraries by running the following command:

• pip install pandas matplotlib seaborn numpy plotly Sklearn xgboost catboost lightgbm

Usage

• To run the analysis, simply execute the notebook. The script will generate several visualizations that help illustrate analysis of data.

Roadmap

The analysis includes the following tasks:

• Data loading and cleaning
• Exploratory data analysis
• data visualization
• outlier detection
• building model
• tuning model
• feature importance

The analysis includes visualizations using Matplotlib, Plotly and Seaborn.

Contributing

• Contributions to this project are welcome. If you notice any errors or have ideas for additional analyses, please feel free to open an issue or submit a pull request.

Conclusion

• In This Project, I built a model of Orthopedic Patients classification with Lightgbm algorithm after evaluating lots of classification algorithms. I have explored lots of insights by visualizing the dataset and I got precise result from model classification. According to my model, The accuracy score is % 84.92.