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:
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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.
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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.
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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.
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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