This project showcases the implementation of popular machine learning algorithms, including K-Nearest Neighbors (KNN), Linear Regression, and Naive Bayes. The algorithms are crafted using the powerful NumPy library. The dataset for experimentation is generated within the main.py file using functions like make_classification.

1. K-Nearest Neighbors (KNN): A versatile algorithm for classification and regression tasks, KNN makes predictions based on the majority class or mean of its k-nearest neighbors in the feature space.

2. Linear Regression: This algorithm models the relationship between dependent and independent variables as a linear equation, making it suitable for predicting numeric values.

3. Naive Bayes: A probabilistic classification algorithm based on Bayes' theorem, Naive Bayes assumes independence between features. It is particularly effective for text classification tasks.

To run and explore the implemented algorithms:

1. Open the main.py file, where the dataset is generated and algorithms are utilized.
2. Execute the file to observe the algorithms in action.
3. Explore the output and results of KNN, Linear Regression, and Naive Bayes implementations.

Ensure that you have NumPy installed before running the project:

pip install numpy

Feel free to modify the code, experiment with different datasets, or extend the implementations. Reach out for any questions or improvements. Happy exploring and coding!