Chronic Kidney Disease Prediction Using Machine Learning

This repository contains a machine learning model for predicting chronic kidney disease (CKD) based on various medical parameters. The model is developed using Python and popular machine learning libraries such as scikit-learn, pandas, and matplotlib. 🩺📊

Dataset

The dataset used for training and testing the model is the Chronic Kidney Disease dataset from the UCI Machine Learning Repository. It consists of various clinical and laboratory features related to CKD, such as age, blood pressure, specific gravity, albumin, sugar, red blood cells, pus cell, etc. The dataset also includes the target variable indicating the presence or absence of CKD. 📉

Dataset Source: Chronic Kidney Disease Data Set

Requirements

• Python 3.x
• NumPy
• Pandas
• Matplotlib
• Scikit-learn

Installation

1. Clone the repository:

   git clone https://github.com/TanoojSeelam/Chronic-Kidney-Disease-Predection-Using-ML.git
   

2. Install the required libraries:

   pip install -r requirements.txt
   

Usage

1. Navigate to the project directory:

   cd Chronic-Kidney-Disease-Predection-Using-ML
   

2. Run the Jupyter notebook:

   jupyter notebook
   

3. Open and execute the CKD_Prediction.ipynb notebook to train the model and make predictions. 🚀

Model Evaluation

The machine learning model is trained using various algorithms such as Logistic Regression, Decision Tree, Random Forest, etc. The performance of each model is evaluated using metrics like accuracy, precision, recall, and F1-score. 📈

Results

The results of the model evaluation, including accuracy scores and confusion matrices, are provided in the Results directory. 📋

License

This project is licensed under the MIT License - see the LICENSE file for details. 📝