Machine Learning Algorithms & Data Manipulation with Python

This repository includes a series of exercises and examples that cover various machine learning algorithms and data manipulation techniques using Python and Scikit-learn. The goal is to provide a set of examples that demonstrate how to apply different algorithms and techniques to real-world datasets.

Content

The project includes implementations of the following algorithms and techniques:

1. Linear Regression
2. Logistic Regression
3. Polynomial Regression
4. Support Vector Machines (SVM)
5. Decision Trees
6. Random Forests
7. Naive Bayes
8. Clustering Algorithms
9. Isolation Forest
10. Neural Networks

Additional Techniques:

• Dataset splitting
• Data preparation and preprocessing
• Pipeline and transformer creation
• Model selection
• Feature selection
• Feature extraction with PCA

Datasets

The repository includes two datasets used for testing the mentioned algorithms:

1. Real email dataset: Located in /datasets/
2. NSL-KDD dataset (network packets): Located in /datasets/nsl-kdd/

Requirements

To run this project, you need to have the following installed:

• Python 3.x
• The dependencies listed in requirements.txt

To install the dependencies, run the following command in your terminal:

pip install -r requirements.txt

## Running the Examples