Machine Learning Models

📌 Overview

This repository contains a collection of Google Colab notebooks created to learn, experiment with, and train multiple Machine Learning models. Each notebook focuses on a specific algorithm or concept, covering the complete workflow from data preprocessing to model evaluation.

The goal of this repository is hands-on learning and practical understanding of machine learning fundamentals through implementation.

---

🧠 Contents

• Supervised learning models (Regression & Classification)
• Unsupervised learning models (Clustering, Dimensionality Reduction)
• Model training, testing, and evaluation techniques
• Data preprocessing and feature engineering
• Performance comparison of different ML algorithms

---

🛠️ Tech Stack

• Language: Python
• Environment: Google Colab
• Libraries & Tools:
  • NumPy
  • Pandas
  • Scikit-learn
  • Matplotlib
  • Seaborn
  • Joblib
  • Plotly
  • Imbalanced-learn
  • Pydotplus

---

📚 Knowledge Gained

• Understanding of core Machine Learning algorithms and their use cases
• Hands-on experience with data cleaning, preprocessing, and feature selection
• Training and evaluating models using metrics like accuracy, precision, recall, RMSE, and confusion matrix
• Hyperparameter tuning and model optimization
• Comparing model performance to select the best-performing approach
• Practical exposure to real-world ML workflows

---