Machine Learning Practice Projects

This repository contains a collection of machine learning practice projects. Each project folder focuses on a specific machine learning concept, with a dataset and a Jupyter Notebook (.ipynb) file that demonstrates the concept in practice.

Folder Structure

Each folder corresponds to a machine learning concept, with the following structure:

• A dataset file (e.g., .csv).
• A Jupyter Notebook (.ipynb) that walks through the implementation of the machine learning technique.

Project Folders

• Bagging:

  • Bagging.ipynb – Demonstrates the bagging ensemble method.
  • Dataset: wine (3).csv

• Boosting:

  • GB_XGB_Boosting.ipynb – Explores gradient boosting with XGBoost.
  • Gradient_boosting_workedin_class.ipynb – Gradient boosting implementation from class practice.
  • Dataset: heart (2).csv

• Decision Tree:

  • Decision_Tree_RandomForest__Worked.ipynb – Combines decision trees and random forests.
  • Decision_tree.ipynb – Focuses on decision tree algorithms.
  • Dataset: HR-Employee-Attrition.csv

• K-Means:

  • K_Means.ipynb – Implements K-Means clustering.
  • Dataset: Iris (1).csv

• Linear Regression:

  • Linear_Regression_4sep_28Aug (3).ipynb – Linear regression practice.
  • Dataset: Advertising.csv

• Logistic Regression:

  • Logistic_4sep_28sug (1).ipynb – Logistic regression practice.
  • Dataset: diabetes.csv

• Random Forest:

  • Decision_Tree_RandomForest (1).ipynb – Focuses on random forest algorithm.

• Support Vector Machines (SVM):

  • SupportVectorMachines.ipynb – Implementation of SVM for classification.
  • Dataset: loan_approved.csv

How to Run

1. Clone the repository:

   git clone <repository-url>