ML

Data Analytics Python ML Workspace

Welcome! This workspace is organized for topic-based, project-oriented machine learning and data analytics in Python. Each folder contains Jupyter notebooks focused on a specific ML topic, progressing from basic to advanced, and designed for hands-on experimentation.

Structure

• Classification/: Notebooks for classification algorithms (Decision Trees, KNN, Logistic Regression, SVM).
• Clustering/: Notebooks for clustering algorithms (K-Means, Hierarchical, DBSCAN).
• Regression/: Notebooks for regression techniques (Simple, Multiple, Polynomial, Nonlinear).
• Recommender Systems/: Notebooks for content-based and collaborative filtering, with sample datasets in ml-latest/.
• The Best Classifier/: Loan classification project with data files.

How to Use

1. Open any notebook to start learning or experimenting with the topic.
2. Each notebook is structured with:
   • Topic overview and objectives
   • Data loading and exploration
   • Model building and evaluation
   • Personal experimentation space
   • Project-oriented challenge
3. Place any new datasets in the relevant folder or create a data/ folder if needed.

Requirements

• Python 3.8+
• Jupyter Notebook or JupyterLab
• See requirements.txt for package dependencies

Tips

• Use the "Personal Experimentation Space" in each notebook to try your own ideas.
• Complete the "Project-Oriented Challenge" sections for deeper learning.
• Refer to the ml-latest/README.txt for details on the recommender system datasets.

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Happy learning and experimenting!