Liver Disease Prediction using Machine Learning

This project aims to predict liver disease in patients using machine learning techniques. The project uses a dataset obtained from Kaggle containing data about chemical compounds obtained from tests like SGOT, SGPT to classify patients and provide insights that may help doctors. The project performs data visualization and pre-processing to gain insights from the data and prepares it for modeling. It performs classification using Decision Tree, K Nearest Neighbors, Logistic Regression, and Support Vector Machine algorithms, and evaluates the performance of each algorithm using different evaluation metrics. The final accuracy report shows the performance of each algorithm on test data.

Getting Started

To get started with this project, you will need to have Python installed on your system. You can download Python from the official website https://www.python.org/downloads/.

You will also need to install the following libraries:

pandas numpy matplotlib seaborn scikit-learn

You can install these libraries using pip. Open the command prompt and type the following command:

pip install pandas numpy matplotlib seaborn scikit-learn

Dataset

The dataset used for this project is obtained from Kaggle. The dataset contains data about chemical compounds obtained from tests like SGOT, SGPT which mentions whether a patient needs to be diagnosed or not. The dataset has 583 rows and 11 columns.

Data Visualization and Pre-processing

The project performs data visualization and pre-processing to gain insights from the data and prepares it for modeling. The project uses different visualization techniques to explore the data and identify trends and patterns. The project also performs pre-processing steps such as converting categorical variables to numerical variables, handling missing values, and normalizing the data.

Classification

The project performs classification using Decision Tree, K Nearest Neighbors, Logistic Regression, and Support Vector Machine algorithms. The project evaluates the performance of each algorithm using different evaluation metrics such as Jaccard score, F1-score, and log loss. The project generates an accuracy report that shows the performance of each algorithm on test data.

Conclusion

The liver disease prediction system using machine learning techniques can be useful for doctors and medical professionals to predict liver disease in patients. The project provides insights into the data and identifies trends and patterns that can help doctors make informed decisions. The project also demonstrates the effectiveness of machine learning techniques in predicting liver disease.