This document provides a comprehensive overview of various machine learning algorithm implementations designed for academic lab work and hands-on practice. The included Jupyter notebooks cover foundational and advanced machine learning topics, including regression, classification, decision trees, and model evaluation.
- Linear Regression using Least Square Method.ipynb
- Multiple Linear Regression 1.ipynb
- Gradient Descent.ipynb
- Logistic Regression 1.ipynb
- Logistic Regression Binomial.ipynb
- Logistic regression Multinomial.ipynb
- decision_tree_1_using_entropy.ipynb
- decision_tree_1_using_gini.ipynb
- decision_tree_2_using_entropy.ipynb
- decision_tree_2_using_gini.ipynb
- decision_tree3_using_entropy.ipynb
- decision_tree3_using_gini.ipynb
- decision_tree4_using_entropy_taking_dataset_from_user.ipynb
- decision_tree4_using_gini_taking_dataset_from_user.ipynb
- confusion matrix for binary classification.ipynb
- Confusion Matrix For Multi-class Classification.ipynb
- Confusion matrix for multiple class.ipynb
- Comparison of different models.ipynb
- normalization types.ipynb
- Support Vector Machine (SVM) Algorithm.ipynb
- Easy-to-follow Jupyter notebooks
- Step-by-step implementations
- Hands-on comparison of model performance
- Includes visualization and evaluation techniques
Install dependencies using:
pip install numpy pandas matplotlib scikit-learn
-
Clone this repository:
git clone https://github.com/your-username/ml-lab-codes.git cd ml-lab-codes -
Open the notebooks in Jupyter Lab or Jupyter Notebook:
jupyter notebook
This project is licensed under the MIT License.