In this repository, we have meticulously implemented a diverse array of machine learning algorithms from scratch, demonstrating a deep understanding of the underlying principles and mechanisms. Throughout the development of these algorithms, we have adhered to a commitment to simplicity, relying solely on the powerful NumPy library.
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Load Data ✓
- Efficient data handling and preprocessing form the cornerstone of any machine learning project. Our data loading implementation ensures seamless integration with the subsequent algorithms.
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Logistic Regression ✓
- A fundamental classification algorithm, logistic regression, has been implemented from scratch. This algorithm is essential for binary classification tasks, showcasing our proficiency in foundational machine learning concepts.
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Linear Regression ✓
- Building upon the principles of regression analysis, our linear regression implementation demonstrates a thorough grasp of predictive modeling for continuous variables.
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Regression ✓
- A more generalized regression algorithm has been implemented, showcasing adaptability to various regression scenarios beyond linear regression.
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K-Nearest Neighbors (KNN) ✓
- The KNN algorithm, a versatile and intuitive method for both classification and regression tasks, has been implemented to showcase our competence in non-parametric algorithms.
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Weighted K-Nearest Neighbors (W-KNN) ✓
- Extending the KNN approach, we've implemented a weighted variant that considers the influence of each neighbor based on predefined weights.
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K-Means ✓
- Our K-Means implementation demonstrates proficiency in unsupervised learning, particularly in the context of cluster analysis.
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Cosine Similarity ✓
- Cosine similarity, a key metric for measuring similarity between vectors, has been implemented to underscore our expertise in similarity-based algorithms.
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Naive Bayes ✓
- The Naive Bayes algorithm, a probabilistic classifier based on Bayes' theorem, has been implemented to showcase our capabilities in probabilistic modeling.
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Naive Bayes with Alpha Parameter ✓
- A refined version of Naive Bayes with an additional alpha parameter has been implemented, demonstrating our attention to hyperparameter tuning.
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Decision Tree ✓
- Decision trees, a powerful tool for both classification and regression, have been implemented, showcasing our proficiency in tree-based algorithms.
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Random Forest ✓
- Building on the decision tree concept, our random forest implementation emphasizes ensemble learning, adding a layer of complexity to our repertoire.
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Principal Component Analysis (PCA) ✓
- PCA, a dimensionality reduction technique, has been implemented to showcase our expertise in feature extraction and data compression.
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Support Vector Machine (SVM)
- (Work in Progress) Our ongoing efforts include the implementation of SVM, a robust algorithm for both classification and regression tasks.
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AdaBoost ✓
- AdaBoost, an ensemble learning technique, has been implemented to showcase our proficiency in boosting algorithms.
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Non-Negative Matrix Factorization (NMF) ✓
- NMF, a dimensionality reduction technique with applications in feature extraction, has been implemented, highlighting our capabilities in matrix factorization.
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DBSCAN ✓
- Our meticulous implementation of DBSCAN reflects expertise in unsupervised density-based clustering for identifying clusters of varying shapes and sizes while robustly handling noise points.
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Minimum Distance Classifier✓
- Our implementation employs distance metrics for efficient classification, emphasizing simplicity and effectiveness in
This repository stands as a testament to our commitment to understanding and implementing machine learning algorithms from the ground up, providing a solid foundation for future developments and innovations in the field.