Machine Learning Projects with Scikit-Learn

A collection of machine learning projects and exercises implemented using Scikit-Learn, covering regression, classification, and clustering algorithms.

📋 Projects

1. Simple Linear Regression

• File: Simple_Linear_Regression/ML0101EN-Reg-Simple-Linear-Regression-Co2.ipynb
• Description: Implementation of simple linear regression to predict CO2 emissions based on engine size.

2. Multiple Linear Regression

• File: Multiple_Linear_Regression/Multiple_Linear_Regression.ipynb
• Description: Predicting housing prices using multiple features from the California Housing dataset.

3. Polynomial Regression

• File: Polynomial_Regression/Polynomial_Regression.ipynb
• Description: Modeling non-linear relationships using Polynomial Regression on synthetic data.

4. Logistic Regression

• File: Logistic_Regression/Logistic_Regression.ipynb
• Description: Classification of Breast Cancer dataset using Logistic Regression.

5. K-Nearest Neighbors (KNN)

• File: K_Nearest_Neighbors/K_Nearest_Neighbors.ipynb
• Description: Classification using KNN on the Iris dataset.

6. Decision Trees

• File: Decision_Trees/Decision_Trees.ipynb
• Description: Classification using Decision Trees on the Wine dataset.

7. Support Vector Machines (SVM)

• File: Support_Vector_Machines/Support_Vector_Machines.ipynb
• Description: Classification using SVM on the Breast Cancer dataset.

8. Clustering with K-Means

• File: Clustering_KMeans/Clustering_KMeans.ipynb
• Description: Unsupervised learning to cluster synthetic data.

9. Random Forest

• File: Random_Forest/Random_Forest.ipynb
• Description: Ensemble learning using Random Forest on the Digits dataset.

🛠️ Setup

1. Clone the repository:

   git clone https://github.com/yourusername/Machine_Learning_course_IBM.git
   cd Machine_Learning_course_IBM

2. Create and activate a virtual environment (recommended):

   python -m venv .venv
   source .venv/bin/activate  # On Linux/Mac
   # .\.venv\Scripts\activate  # On Windows

3. Install the required packages:

   pip install -r requirements.txt

4. Run Jupyter Notebook:

   jupyter notebook

📝 License

This project is licensed under the MIT License - see the LICENSE file for details.

🙏 Acknowledgments

• IBM Machine Learning Course
• Scikit-Learn Documentation
• Open Source Community