Fetal Health Classification

Fetal Health Classification/README.md

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+# Fetal Health Classification
+
+## Overview
+This project aims to predict fetal health status using various machine learning models. The dataset contains records of fetal health measurements, and the goal is to classify the health status into three categories: normal, suspect, and pathologic. 
+
+The project involves data exploration, preprocessing, model training, hyperparameter tuning, and evaluation.
+
+## Dataset
+The Fetal Health Classification dataset contains 2,126 records of features extracted from Cardiotocogram (CTG) exams. These features were classified by three expert obstetricians into three categories: Normal, Suspect, and Pathological. The dataset aims to support the development of models that can classify fetal health, helping to prevent child and maternal mortality.
+
+Link to the dataset: [Fetal Health Classification Dataset](https://www.kaggle.com/datasets/andrewmvd/fetal-health-classification/data)
+
+
+## Models Used
+
+1. Logistic Regression
+2. Decision Tree Classifier
+3. Random Forest Classifier
+4. Gradient Boosting Classifier
+
+## Results
+The test accuracy results for the models are as follows:
+
+- Gradient Boosting: 96.00%
+- Random Forest: 95.00%
+- Decision Tree: 93.00%
+- Logistic Regression: 89.00%
+
+Ensemble methods like Gradient Boosting and Random Forest performed the best, indicating strong predictive power on unseen data compared to simpler models like Decision Tree and Logistic Regression.
+
+## Contribution
+Contributions are welcome! Feel free to submit issues, feature requests, or pull requests to improve the system.