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🤖 Machine Learning Basics

This repository contains foundational machine learning projects and code examples that cover essential algorithms, concepts, and workflows in the field. It’s designed for beginners and enthusiasts aiming to build a solid understanding of machine learning using Python and popular libraries.

📚 What You’ll Find

  • 🧠 Supervised Learning Models:
    • Linear Regression
    • Logistic Regression
    • Decision Trees
    • K-Nearest Neighbors (KNN)
  • 🎯 Model Evaluation:
    • Accuracy, Precision, Recall, F1 Score
    • Confusion Matrix
    • Cross-validation techniques
  • 📊 Exploratory Data Analysis (EDA)
  • 🔍 Feature Engineering & Preprocessing

🛠️ Tech Stack

  • Languages: Python
  • Libraries: Scikit-learn, Pandas, NumPy, Matplotlib, Seaborn, Jupyter Notebook

📈 Sample Results

  • Visualizations of decision boundaries
  • Metrics comparison between models
  • Clean, commented notebooks for easy learning and modification

🚀 Getting Started

To run the notebooks locally:

  1. Clone this repository
git clone https://github.com/FaladeF311/Machine-Learning-Basic.git