Python Data Preprocessing & Classification Techniques

This repository demonstrates data preprocessing techniques and basic classifiers using scikit-learn. It includes examples with visualizations to help understand how classifiers separate different classes.

Techniques Covered

Data Preprocessing

• Mean Removal (Standardization): Centering data so that the mean is 0 and standard deviation is 1.
• Scaling (Min-Max Scaling): Rescales features to a fixed range (0 to 1).
• Normalization (L1/L2): Scales rows to have unit norm.
• Binarization: Converts numeric data into 0/1 using a threshold.
• Label Encoding: Converts categorical labels into numeric form.

Classification

• Naïve Bayes Classifier: Uses Bayes theorem and assumes features are independent.
  Example decision boundary:
  
• Logistic Regression Classifier: Predicts class probabilities using a logistic function.
  Example decision boundary:
  

Confusion Matrix

Visualizes the performance of a classification model.

Getting Started

Prerequisites

• Python 3.x
• NumPy
• scikit-learn
• Matplotlib (for visualizations)

Installation

1. Clone the repository (optional):

   git clone https://github.com/LeonMotaung/AI-Engineer.git
   cd AI-Engineer

2. Install required packages:

   pip install -r requirements.txt

This project provides a hands-on introduction to preprocessing, basic classifiers, and visual evaluation metrics for supervised learning tasks.