Wine KNN
In this data science project, I embarked on a journey to create a classification model using the K-Nearest Neighbors (KNN) algorithm, demonstrating my commitment to understanding the intricacies of machine learning from the ground up. Instead of relying on existing libraries, I took on the challenge of implementing the KNN algorithm entirely from scratch, enabling a deep dive into the fundamentals of this classification technique.
Highlights:
- Algorithm Implementation: The project revolved around building the KNN algorithm from the ground up, enabling a comprehensive understanding of its inner workings. This hands-on approach allowed me to fine-tune the algorithm to suit specific requirements and datasets.
- Data Analysis: A data analysis was conducted to gain insights into the dataset's characteristics. This step included data exploration, feature engineering, and preprocessing to enhance the model's predictive power.
- Nested Cross Validation: To ensure robustness and prevent overfitting, I employed nested cross-validation, a technique that rigorously assesses model performance. This approach optimally fine-tuned model hyperparameters, enhancing generalizability.
- Introducing Noise: Recognizing the importance of model resilience, I introduced controlled noise into the dataset to enhance its robustness. This step simulated real-world scenarios where data may not be perfect, further fortifying the model's classification capabilities.
Outcomes:
This project yielded a K-Nearest Neighbors (KNN) classification model, implemented from scratch, with enhanced data handling, noise resilience, and nested cross-validation, showcasing a comprehensive mastery of machine learning fundamentals.
Full report in PDF