Exploratory Data Analysis (EDA) – Chronic Kidney Disease Dataset
📌 Project Overview
This project focuses on performing Exploratory Data Analysis (EDA) on a Chronic Kidney Disease (CKD) dataset to understand the underlying structure of the data, identify important patterns, handle missing values, and analyze key medical attributes related to kidney health. The insights derived from this analysis can support early diagnosis and serve as a foundation for machine learning models.
🎯 Objectives
Understand the distribution of clinical features
Identify missing and inconsistent values
Analyze relationships between medical parameters
Detect trends and patterns associated with chronic kidney disease
Prepare data for further predictive modeling
🧬 Dataset Description
The dataset contains patient medical records with attributes such as:
Age
Blood Pressure
Specific Gravity
Albumin
Sugar
Blood Glucose Random
Blood Urea
Serum Creatinine
Hemoglobin
Packed Cell Volume
White Blood Cell Count
Red Blood Cell Count
Hypertension, Diabetes Mellitus, Anemia, etc.
Target variable: Chronic Kidney Disease (CKD / Not CKD)
🛠️ Technologies Used
Python
Pandas – data manipulation
NumPy – numerical operations
Matplotlib & Seaborn – data visualization
Jupyter Notebook
📊 EDA Steps Performed
Data loading and inspection
Handling missing values
Data type corrections
Statistical summary of features
Univariate analysis (histograms, count plots)
Bivariate analysis (correlation heatmaps, comparisons)
Class distribution analysis
🔍 Key Insights
Several medical attributes contain missing values that require preprocessing
Certain features like serum creatinine, hemoglobin, and blood urea show strong correlation with CKD
CKD patients exhibit noticeable differences in blood-related parameters
📁 Project Structure ├── EDA_chronic_data.ipynb ├── README.md
🚀 Future Scope
Feature engineering and selection
Building machine learning models for CKD prediction
Model evaluation and optimization
Deployment as a web-based health screening tool
🤝 Contribution
Contributions are welcome! Feel free to fork the repository, raise issues, or submit pull requests.
This project is for educational and research purposes.