This repository contains a collection of Python scripts that serve as a practical workshop for learning data science and machine learning. The code is structured to guide a user from Python fundamentals to building and evaluating basic machine learning models.

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📚 Topics Covered

This workshop covers the essential libraries and concepts in the Python data science stack:

• Python Fundamentals: Core data structures (lists, dictionaries, sets), functions, error handling, and list comprehensions.
• NumPy: Creating and manipulating numerical arrays for scientific computing.
• Pandas: Data manipulation and analysis using Series and DataFrames, including handling missing data and file I/O.
• Data Visualization: Creating static plots and charts using Matplotlib and statistical visualizations with Seaborn.
• Machine Learning with Scikit-Learn:
  • Supervised Learning fundamentals.
  • Linear Regression for predicting continuous values.
  • Logistic Regression for classification tasks.
  • Decision Trees and Support Vector Machines (SVM) for more complex classification.
  • Model evaluation using metrics like accuracy, confusion matrix, and ROC curves.