This repository contains implementations of various classification and clustering algorithms applied to diverse datasets for classification and prediction tasks. The algorithms employed include K-means clustering, Simple Linear Regression, Principal Component Analysis (PCA), Support Vector Machine (SVM), and Naive Bayes.
In this project, we explore a range of machine learning techniques to address classification and clustering challenges across different datasets. Each algorithm is meticulously implemented and thoroughly evaluated to ascertain its efficacy in solving specific tasks.
- K-means Clustering: A widely-used clustering algorithm that partitions data into distinct groups based on similarity.
- Simple Linear Regression: A foundational regression technique for modeling the relationship between a dependent variable and one independent variable.
- Principal Component Analysis (PCA): A dimensionality reduction technique used to transform high-dimensional data into a lower-dimensional space while preserving important information.
- Support Vector Machine (SVM): A powerful supervised learning algorithm capable of performing classification, regression, and outlier detection tasks.
- Naive Bayes: A probabilistic classifier based on Bayes' theorem with the assumption of independence among features.
- Data Preparation: Datasets are preprocessed and formatted to suit the requirements of each algorithm.
- Model Implementation: Each algorithm is implemented with careful attention to detail, ensuring accuracy and efficiency.
- Training and Testing: Models are trained on labeled datasets and rigorously tested to evaluate their performance and generalization capabilities.
- Evaluation Metrics: Performance metrics such as accuracy, precision, recall, and F1-score are computed to assess the effectiveness of each algorithm.
- Hyperparameter Tuning: Where applicable, hyperparameters are fine-tuned using techniques such as cross-validation to optimize model performance.