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This project explores the application of the K-Nearest Neighbors (KNN) algorithm to classify data using a synthetic dataset. KNN, a widely used machine learning technique, assigns class labels based on the majority vote of the nearest neighbors. The analysis begins with exploratory data analysis (EDA) to understand the dataset’s characteristics, followed by feature scaling to ensure the accuracy of distance-based computations. We implemented the KNN classifier using Python and evaluated its performance through metrics like precision, recall, and F1-score. Initial results achieved an accuracy of 94%, but through hyperparameter tuning, we optimized the value of K to further improve the model’s performance. The project demonstrates the effectiveness of KNN for classification tasks while highlighting the impact of feature scaling and hyperparameter selection. Future work includes exploring more advanced algorithms and techniques for enhanced predictive accuracy.

Two sample tests

This post shows how to determine the fetal gender using cfDNA as in NIPT test.