Welcome to the Python Data Science Basics repository! 🚀 This repo serves as a foundational guide for beginners and enthusiasts who want to explore the exciting world of Data Science using Python. It covers essential concepts such as data preparation and data mining, ensuring a solid grasp of key techniques in the field.

Data preparation is a crucial step in any data science workflow. This directory contains notebooks and scripts focused on:

• Data Cleaning: Handling missing values, removing duplicates, and correcting errors.
• Data Transformation: Normalization, scaling and more.
• Data Exploration: Understanding data distributions and identifying patterns with visualizations.

Data mining is where the magic happens! This section delves into techniques for extracting valuable insights from data, including:

• Exploratory Data Analysis: Identifying trends, correlations, and anomalies.
• Clustering & Classification: Implementing K-Means, Decision Trees, and other ML models.
• Dimensionality Reduction: Simplifying complex datasets with PCA and more.
• Association Rule Mining: Finding hidden relationships in large datasets.

To get started with the repository, follow these simple steps:

1. Install Python from the official website.
2. Install Jupyter Notebook using pip:

   pip install jupyter

3. Create a folder and open it inside the terminal.
4. Launch Jupyter Notebook by running:

   jupyter notebook

   This will open Jupyter in your browser, allowing you to run and edit notebooks interactively.

Python is often referred to as an interpreted language, but there's more to the story!

• Compiled languages (like C, C++) convert source code into machine code before execution, resulting in faster runtime performance but requiring platform-specific binaries.
• Interpreted languages (like Python, JavaScript) execute code line by line, making development more flexible and cross-platform but typically slower.

Python is unique because it compiles to bytecode before interpretation:

1. Compilation Step: Python code is first converted into bytecode (.pyc files), an intermediate form that is not machine code.
2. Execution Step: The Python Virtual Machine (PVM) interprets the bytecode and executes it.

This approach allows Python to maintain flexibility while optimizing execution. Additionally, different implementations like Jython (for Java bytecode) and IronPython (for C# bytecode) enable Python to run in diverse environments.