🧠 Machine Learning Notebooks Collection

This repository contains hands-on Jupyter notebooks that explore key concepts in machine learning, such as logistic regression, regularization, dimensionality reduction, clustering, and more. Each notebook is designed to explain core ideas through practical examples and datasets.

📘 Notebooks Included

Notebook	Description
Brest_Cancer_Classification_Using_Logistic_Regression.ipynb	Uses logistic regression to classify malignant and benign tumors from the breast cancer dataset.
Regularization_on_Logistic_Regression.ipynb	Demonstrates how L1 and L2 regularization affect logistic regression performance and overfitting.
Kmeans_on_Breast_Cancer_Dataset.ipynb	Applies the K-means clustering algorithm on the breast cancer dataset for unsupervised learning.
PCA.ipynb	Performs Principal Component Analysis (PCA) for dimensionality reduction and visualizes the results.
Calculate_Bias_and_Variance.ipynb	Shows how to compute and interpret bias and variance in machine learning models.
TV_marketing_price_prediction_with_LR.ipynb	Builds a simple linear regression model to predict sales based on TV advertising budget.

---

📂 Structure

├── Brest_Cancer_Classification_Using_Logistic_Regression.ipynb

├── Regularization_on_Logistic_Regression.ipynb

├── Kmeans_on_Breast_Cancer_Dataset.ipynb

├── PCA.ipynb

├── Calculate_Bias_and_Variance.ipynb

├── TV_marketing_price_prediction_with_LR.ipynb

└── README.md

---

🛠️ Tools & Libraries Used

• Python
• NumPy
• Pandas
• Scikit-learn
• Matplotlib
• Seaborn
• Jupyter Notebook

---

🎯 Purpose

This repository serves both as:

• A personal learning portfolio of core ML concepts
• A helpful resource for beginners seeking real code examples

Each notebook is written to be clear, practical, and self-contained, with explanations embedded alongside the code.

---

🔗 How to Use

You can run these notebooks directly in your browser using:

• Google Colab
• Jupyter Notebook (locally via jupyter notebook or JupyterLab)

---

💡 Future Improvements

• Add more models (e.g., decision trees, SVMs, etc.)
• Integrate model evaluation metrics
• Improve visualizations

---

📬 Contact

If you have any questions or suggestions, feel free to open an issue or connect with me at:

• GitHub: github.com/belviskhoremk
• Email: [belviskhoremk@gmail.com]

---

📌 Star this repo if you found it useful — and feel free to fork and build upon it!