Python-Visualizations

Data Visualization using Python libraries

Kidney Stone Detection Analysis

This repository contains an analysis of urine test results to detect the presence of kidney stones.

Project Overview

The analysis focuses on the following attributes derived from urine tests:

• Gravity
• pH
• Osmolality
• Conductivity
• Urea
• Calcium
• Target (binary indicator: 1 for presence of kidney stone, 0 for absence)

Visualizations

The following visualizations are included:

• Histograms: To visualize the distribution of each attribute.
• Boxplots: To show the central tendency and variability of each attribute, grouped by the target.
• Correlation Matrix: To examine pairwise relationships between attributes and their correlation with the target.
• Scatter Plots: To explore potential patterns and correlations between pairs of attributes.
• Pair Plots: To visualize relationships between multiple pairs of attributes.
• Kernel Density Estimate (KDE) Plots: To provide smoothed estimates of attribute distributions.

Repository Contents

• Kidney_Stone_Detection_Analysis.pdf: A detailed report of the analysis.
• visualizations.ipynb: Jupyter Notebook containing the code for the visualizations.

Usage

1. Clone the repository: git clone https://github.com/yourusername/kidney-stone-detection-analysis.git

2. Navigate to the project directory: cd kidney-stone-detection-analysis

3. Open the Jupyter Notebook to explore the visualizations: jupyter notebook visualizations.ipynb

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

##Acknowledgements Data for this analysis is based on synthetic urine test results. Dataset is available in Kaggle website. Visualizations created using Python libraries such as Pandas, Matplotlib, and Seaborn.