ML Models Basic Notebooks

This folder contains a set of Jupyter notebooks demonstrating the implementation and usage of several basic machine learning models in Python.

Notebooks

• LinearR.ipynb

  • Covers linear regression for modeling the relationship between numeric features and a continuous target.

• LRpolynomial.ipynb

  • Demonstrates polynomial regression, extending linear regression to model nonlinear relationships.

• logisticR.ipynb

  • Contains a logistic regression example for binary classification problems.

• decisionT.ipynb

  • Demonstrates a decision tree classifier or regressor (depending on notebook content) for supervised learning.

• knn.ipynb

  • Shows a k-nearest neighbors (KNN) model, typically for classification or regression tasks.

How to Use

1. Open each notebook in Jupyter Lab, Jupyter Notebook, or any compatible notebook viewer.
2. Run the cells sequentially to follow the data loading, training, and evaluation steps.
3. Modify the dataset, hyperparameters, or model settings to experiment with different results.

Recommended Environment

• Python 3.x
• numpy
• pandas
• scikit-learn
• matplotlib or seaborn (if plotting is included)

Notes

• This repository is intended for learning and experimentation with fundamental machine learning methods.

Author

• by Parth Jiman