Merge pull request #876 from Varunshiyam/fixes-873

Chronic Kidney Disease Prediction Model

Prediction Models/Chronic_Kidney_Disease_prediction/Readme.md

@@ -0,0 +1,34 @@
+# Chronic Kidney Disease Prediction Model
+
+## Project Description
+
+This project aims to predict chronic kidney disease (CKD) using advanced machine learning models. Leveraging a dataset that includes patient health metrics, the project implements various algorithms to achieve accurate classification of CKD. The primary objective is to create a robust model that can assist in early detection, contributing to better patient outcomes and proactive management of the disease.
+
+### Key Features:
+- **Data Preprocessing**: Cleaning, normalization, and transformation of the dataset to prepare for effective training.
+- **Model Implementation**: Application of various machine learning models such as Logistic Regression, Decision Trees, Random Forests, and Support Vector Machines (SVM).
+- **Evaluation Metrics**: Comprehensive evaluation using metrics like accuracy, precision, recall, and F1-score.
+- **High Accuracy**: The project achieves up to 98% accuracy, showcasing the effectiveness of the chosen methodologies.
+
+### Technologies Used:
+- **Python**: Primary programming language for coding and data analysis.
+- **Pandas & NumPy**: For data manipulation and analysis.
+- **Scikit-learn**: For implementing machine learning models and evaluation metrics.
+- **Matplotlib & Seaborn**: For data visualization to aid in understanding the dataset and model results.
+
+## Problem Statement
+
+Chronic kidney disease (CKD) is a global health challenge with significant morbidity and mortality. Early diagnosis is crucial for effective treatment and slowing disease progression. However, manual analysis of patient health metrics can be time-consuming and prone to human error. This project addresses the need for an automated, accurate, and efficient system to predict CKD from patient data. By employing machine learning techniques, the system helps:
+- **Streamline Diagnosis**: Providing faster, data-driven insights for healthcare professionals.
+- **Improve Accuracy**: Reducing the variability and potential inaccuracies in manual assessments.
+- **Assist in Preventative Care**: Enabling early intervention strategies to mitigate disease impact.
+
+## Project Structure
+
+- `data/`: Contains the dataset used for training and testing.
+- `notebooks/`: Jupyter notebooks for data exploration and model development.
+- `src/`: Python scripts for data processing, model training, and evaluation.
+- `results/`: Includes reports, plots, and saved models.
+- `README.md`: This documentation.
+
+